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Qualitative Research – Methods, Analysis Types and Guide

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Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

• Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
• Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

• Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
• Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
• Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
• Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
• Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
• Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
• Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

• Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
• Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
• Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
• Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
• Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
• Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
• Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

• Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
• Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
• Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
• Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

• Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
• Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
• Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
• Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
• Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

• Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
• Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
• Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
• Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
• Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
• Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

• Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
• Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
• Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
• Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
• Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
• Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

• Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
• Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
• Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
• Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
• Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
• Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

Also see Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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Criteria for Good Qualitative Research: A Comprehensive Review

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• Published: 18 September 2021
• Volume 31 , pages 679–689, ( 2022 )

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• Drishti Yadav   ORCID: orcid.org/0000-0002-2974-0323 1  

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This review aims to synthesize a published set of evaluative criteria for good qualitative research. The aim is to shed light on existing standards for assessing the rigor of qualitative research encompassing a range of epistemological and ontological standpoints. Using a systematic search strategy, published journal articles that deliberate criteria for rigorous research were identified. Then, references of relevant articles were surveyed to find noteworthy, distinct, and well-defined pointers to good qualitative research. This review presents an investigative assessment of the pivotal features in qualitative research that can permit the readers to pass judgment on its quality and to condemn it as good research when objectively and adequately utilized. Overall, this review underlines the crux of qualitative research and accentuates the necessity to evaluate such research by the very tenets of its being. It also offers some prospects and recommendations to improve the quality of qualitative research. Based on the findings of this review, it is concluded that quality criteria are the aftereffect of socio-institutional procedures and existing paradigmatic conducts. Owing to the paradigmatic diversity of qualitative research, a single and specific set of quality criteria is neither feasible nor anticipated. Since qualitative research is not a cohesive discipline, researchers need to educate and familiarize themselves with applicable norms and decisive factors to evaluate qualitative research from within its theoretical and methodological framework of origin.

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Introduction

“… It is important to regularly dialogue about what makes for good qualitative research” (Tracy, 2010 , p. 837)

To decide what represents good qualitative research is highly debatable. There are numerous methods that are contained within qualitative research and that are established on diverse philosophical perspectives. Bryman et al., ( 2008 , p. 262) suggest that “It is widely assumed that whereas quality criteria for quantitative research are well‐known and widely agreed, this is not the case for qualitative research.” Hence, the question “how to evaluate the quality of qualitative research” has been continuously debated. There are many areas of science and technology wherein these debates on the assessment of qualitative research have taken place. Examples include various areas of psychology: general psychology (Madill et al., 2000 ); counseling psychology (Morrow, 2005 ); and clinical psychology (Barker & Pistrang, 2005 ), and other disciplines of social sciences: social policy (Bryman et al., 2008 ); health research (Sparkes, 2001 ); business and management research (Johnson et al., 2006 ); information systems (Klein & Myers, 1999 ); and environmental studies (Reid & Gough, 2000 ). In the literature, these debates are enthused by the impression that the blanket application of criteria for good qualitative research developed around the positivist paradigm is improper. Such debates are based on the wide range of philosophical backgrounds within which qualitative research is conducted (e.g., Sandberg, 2000 ; Schwandt, 1996 ). The existence of methodological diversity led to the formulation of different sets of criteria applicable to qualitative research.

Among qualitative researchers, the dilemma of governing the measures to assess the quality of research is not a new phenomenon, especially when the virtuous triad of objectivity, reliability, and validity (Spencer et al., 2004 ) are not adequate. Occasionally, the criteria of quantitative research are used to evaluate qualitative research (Cohen & Crabtree, 2008 ; Lather, 2004 ). Indeed, Howe ( 2004 ) claims that the prevailing paradigm in educational research is scientifically based experimental research. Hypotheses and conjectures about the preeminence of quantitative research can weaken the worth and usefulness of qualitative research by neglecting the prominence of harmonizing match for purpose on research paradigm, the epistemological stance of the researcher, and the choice of methodology. Researchers have been reprimanded concerning this in “paradigmatic controversies, contradictions, and emerging confluences” (Lincoln & Guba, 2000 ).

In general, qualitative research tends to come from a very different paradigmatic stance and intrinsically demands distinctive and out-of-the-ordinary criteria for evaluating good research and varieties of research contributions that can be made. This review attempts to present a series of evaluative criteria for qualitative researchers, arguing that their choice of criteria needs to be compatible with the unique nature of the research in question (its methodology, aims, and assumptions). This review aims to assist researchers in identifying some of the indispensable features or markers of high-quality qualitative research. In a nutshell, the purpose of this systematic literature review is to analyze the existing knowledge on high-quality qualitative research and to verify the existence of research studies dealing with the critical assessment of qualitative research based on the concept of diverse paradigmatic stances. Contrary to the existing reviews, this review also suggests some critical directions to follow to improve the quality of qualitative research in different epistemological and ontological perspectives. This review is also intended to provide guidelines for the acceleration of future developments and dialogues among qualitative researchers in the context of assessing the qualitative research.

The rest of this review article is structured in the following fashion: Sect.  Methods describes the method followed for performing this review. Section Criteria for Evaluating Qualitative Studies provides a comprehensive description of the criteria for evaluating qualitative studies. This section is followed by a summary of the strategies to improve the quality of qualitative research in Sect.  Improving Quality: Strategies . Section  How to Assess the Quality of the Research Findings? provides details on how to assess the quality of the research findings. After that, some of the quality checklists (as tools to evaluate quality) are discussed in Sect.  Quality Checklists: Tools for Assessing the Quality . At last, the review ends with the concluding remarks presented in Sect.  Conclusions, Future Directions and Outlook . Some prospects in qualitative research for enhancing its quality and usefulness in the social and techno-scientific research community are also presented in Sect.  Conclusions, Future Directions and Outlook .

For this review, a comprehensive literature search was performed from many databases using generic search terms such as Qualitative Research , Criteria , etc . The following databases were chosen for the literature search based on the high number of results: IEEE Explore, ScienceDirect, PubMed, Google Scholar, and Web of Science. The following keywords (and their combinations using Boolean connectives OR/AND) were adopted for the literature search: qualitative research, criteria, quality, assessment, and validity. The synonyms for these keywords were collected and arranged in a logical structure (see Table 1 ). All publications in journals and conference proceedings later than 1950 till 2021 were considered for the search. Other articles extracted from the references of the papers identified in the electronic search were also included. A large number of publications on qualitative research were retrieved during the initial screening. Hence, to include the searches with the main focus on criteria for good qualitative research, an inclusion criterion was utilized in the search string.

From the selected databases, the search retrieved a total of 765 publications. Then, the duplicate records were removed. After that, based on the title and abstract, the remaining 426 publications were screened for their relevance by using the following inclusion and exclusion criteria (see Table 2 ). Publications focusing on evaluation criteria for good qualitative research were included, whereas those works which delivered theoretical concepts on qualitative research were excluded. Based on the screening and eligibility, 45 research articles were identified that offered explicit criteria for evaluating the quality of qualitative research and were found to be relevant to this review.

Figure  1 illustrates the complete review process in the form of PRISMA flow diagram. PRISMA, i.e., “preferred reporting items for systematic reviews and meta-analyses” is employed in systematic reviews to refine the quality of reporting.

PRISMA flow diagram illustrating the search and inclusion process. N represents the number of records

Criteria for Evaluating Qualitative Studies

Fundamental criteria: general research quality.

Various researchers have put forward criteria for evaluating qualitative research, which have been summarized in Table 3 . Also, the criteria outlined in Table 4 effectively deliver the various approaches to evaluate and assess the quality of qualitative work. The entries in Table 4 are based on Tracy’s “Eight big‐tent criteria for excellent qualitative research” (Tracy, 2010 ). Tracy argues that high-quality qualitative work should formulate criteria focusing on the worthiness, relevance, timeliness, significance, morality, and practicality of the research topic, and the ethical stance of the research itself. Researchers have also suggested a series of questions as guiding principles to assess the quality of a qualitative study (Mays & Pope, 2020 ). Nassaji ( 2020 ) argues that good qualitative research should be robust, well informed, and thoroughly documented.

Qualitative Research: Interpretive Paradigms

All qualitative researchers follow highly abstract principles which bring together beliefs about ontology, epistemology, and methodology. These beliefs govern how the researcher perceives and acts. The net, which encompasses the researcher’s epistemological, ontological, and methodological premises, is referred to as a paradigm, or an interpretive structure, a “Basic set of beliefs that guides action” (Guba, 1990 ). Four major interpretive paradigms structure the qualitative research: positivist and postpositivist, constructivist interpretive, critical (Marxist, emancipatory), and feminist poststructural. The complexity of these four abstract paradigms increases at the level of concrete, specific interpretive communities. Table 5 presents these paradigms and their assumptions, including their criteria for evaluating research, and the typical form that an interpretive or theoretical statement assumes in each paradigm. Moreover, for evaluating qualitative research, quantitative conceptualizations of reliability and validity are proven to be incompatible (Horsburgh, 2003 ). In addition, a series of questions have been put forward in the literature to assist a reviewer (who is proficient in qualitative methods) for meticulous assessment and endorsement of qualitative research (Morse, 2003 ). Hammersley ( 2007 ) also suggests that guiding principles for qualitative research are advantageous, but methodological pluralism should not be simply acknowledged for all qualitative approaches. Seale ( 1999 ) also points out the significance of methodological cognizance in research studies.

Table 5 reflects that criteria for assessing the quality of qualitative research are the aftermath of socio-institutional practices and existing paradigmatic standpoints. Owing to the paradigmatic diversity of qualitative research, a single set of quality criteria is neither possible nor desirable. Hence, the researchers must be reflexive about the criteria they use in the various roles they play within their research community.

Improving Quality: Strategies

Another critical question is “How can the qualitative researchers ensure that the abovementioned quality criteria can be met?” Lincoln and Guba ( 1986 ) delineated several strategies to intensify each criteria of trustworthiness. Other researchers (Merriam & Tisdell, 2016 ; Shenton, 2004 ) also presented such strategies. A brief description of these strategies is shown in Table 6 .

It is worth mentioning that generalizability is also an integral part of qualitative research (Hays & McKibben, 2021 ). In general, the guiding principle pertaining to generalizability speaks about inducing and comprehending knowledge to synthesize interpretive components of an underlying context. Table 7 summarizes the main metasynthesis steps required to ascertain generalizability in qualitative research.

Figure  2 reflects the crucial components of a conceptual framework and their contribution to decisions regarding research design, implementation, and applications of results to future thinking, study, and practice (Johnson et al., 2020 ). The synergy and interrelationship of these components signifies their role to different stances of a qualitative research study.

Essential elements of a conceptual framework

In a nutshell, to assess the rationale of a study, its conceptual framework and research question(s), quality criteria must take account of the following: lucid context for the problem statement in the introduction; well-articulated research problems and questions; precise conceptual framework; distinct research purpose; and clear presentation and investigation of the paradigms. These criteria would expedite the quality of qualitative research.

How to Assess the Quality of the Research Findings?

The inclusion of quotes or similar research data enhances the confirmability in the write-up of the findings. The use of expressions (for instance, “80% of all respondents agreed that” or “only one of the interviewees mentioned that”) may also quantify qualitative findings (Stenfors et al., 2020 ). On the other hand, the persuasive reason for “why this may not help in intensifying the research” has also been provided (Monrouxe & Rees, 2020 ). Further, the Discussion and Conclusion sections of an article also prove robust markers of high-quality qualitative research, as elucidated in Table 8 .

Quality Checklists: Tools for Assessing the Quality

Numerous checklists are available to speed up the assessment of the quality of qualitative research. However, if used uncritically and recklessly concerning the research context, these checklists may be counterproductive. I recommend that such lists and guiding principles may assist in pinpointing the markers of high-quality qualitative research. However, considering enormous variations in the authors’ theoretical and philosophical contexts, I would emphasize that high dependability on such checklists may say little about whether the findings can be applied in your setting. A combination of such checklists might be appropriate for novice researchers. Some of these checklists are listed below:

The most commonly used framework is Consolidated Criteria for Reporting Qualitative Research (COREQ) (Tong et al., 2007 ). This framework is recommended by some journals to be followed by the authors during article submission.

Standards for Reporting Qualitative Research (SRQR) is another checklist that has been created particularly for medical education (O’Brien et al., 2014 ).

Also, Tracy ( 2010 ) and Critical Appraisal Skills Programme (CASP, 2021 ) offer criteria for qualitative research relevant across methods and approaches.

Further, researchers have also outlined different criteria as hallmarks of high-quality qualitative research. For instance, the “Road Trip Checklist” (Epp & Otnes, 2021 ) provides a quick reference to specific questions to address different elements of high-quality qualitative research.

Conclusions, Future Directions, and Outlook

This work presents a broad review of the criteria for good qualitative research. In addition, this article presents an exploratory analysis of the essential elements in qualitative research that can enable the readers of qualitative work to judge it as good research when objectively and adequately utilized. In this review, some of the essential markers that indicate high-quality qualitative research have been highlighted. I scope them narrowly to achieve rigor in qualitative research and note that they do not completely cover the broader considerations necessary for high-quality research. This review points out that a universal and versatile one-size-fits-all guideline for evaluating the quality of qualitative research does not exist. In other words, this review also emphasizes the non-existence of a set of common guidelines among qualitative researchers. In unison, this review reinforces that each qualitative approach should be treated uniquely on account of its own distinctive features for different epistemological and disciplinary positions. Owing to the sensitivity of the worth of qualitative research towards the specific context and the type of paradigmatic stance, researchers should themselves analyze what approaches can be and must be tailored to ensemble the distinct characteristics of the phenomenon under investigation. Although this article does not assert to put forward a magic bullet and to provide a one-stop solution for dealing with dilemmas about how, why, or whether to evaluate the “goodness” of qualitative research, it offers a platform to assist the researchers in improving their qualitative studies. This work provides an assembly of concerns to reflect on, a series of questions to ask, and multiple sets of criteria to look at, when attempting to determine the quality of qualitative research. Overall, this review underlines the crux of qualitative research and accentuates the need to evaluate such research by the very tenets of its being. Bringing together the vital arguments and delineating the requirements that good qualitative research should satisfy, this review strives to equip the researchers as well as reviewers to make well-versed judgment about the worth and significance of the qualitative research under scrutiny. In a nutshell, a comprehensive portrayal of the research process (from the context of research to the research objectives, research questions and design, speculative foundations, and from approaches of collecting data to analyzing the results, to deriving inferences) frequently proliferates the quality of a qualitative research.

Prospects : A Road Ahead for Qualitative Research

Irrefutably, qualitative research is a vivacious and evolving discipline wherein different epistemological and disciplinary positions have their own characteristics and importance. In addition, not surprisingly, owing to the sprouting and varied features of qualitative research, no consensus has been pulled off till date. Researchers have reflected various concerns and proposed several recommendations for editors and reviewers on conducting reviews of critical qualitative research (Levitt et al., 2021 ; McGinley et al., 2021 ). Following are some prospects and a few recommendations put forward towards the maturation of qualitative research and its quality evaluation:

In general, most of the manuscript and grant reviewers are not qualitative experts. Hence, it is more likely that they would prefer to adopt a broad set of criteria. However, researchers and reviewers need to keep in mind that it is inappropriate to utilize the same approaches and conducts among all qualitative research. Therefore, future work needs to focus on educating researchers and reviewers about the criteria to evaluate qualitative research from within the suitable theoretical and methodological context.

There is an urgent need to refurbish and augment critical assessment of some well-known and widely accepted tools (including checklists such as COREQ, SRQR) to interrogate their applicability on different aspects (along with their epistemological ramifications).

Efforts should be made towards creating more space for creativity, experimentation, and a dialogue between the diverse traditions of qualitative research. This would potentially help to avoid the enforcement of one's own set of quality criteria on the work carried out by others.

Moreover, journal reviewers need to be aware of various methodological practices and philosophical debates.

It is pivotal to highlight the expressions and considerations of qualitative researchers and bring them into a more open and transparent dialogue about assessing qualitative research in techno-scientific, academic, sociocultural, and political rooms.

Frequent debates on the use of evaluative criteria are required to solve some potentially resolved issues (including the applicability of a single set of criteria in multi-disciplinary aspects). Such debates would not only benefit the group of qualitative researchers themselves, but primarily assist in augmenting the well-being and vivacity of the entire discipline.

To conclude, I speculate that the criteria, and my perspective, may transfer to other methods, approaches, and contexts. I hope that they spark dialog and debate – about criteria for excellent qualitative research and the underpinnings of the discipline more broadly – and, therefore, help improve the quality of a qualitative study. Further, I anticipate that this review will assist the researchers to contemplate on the quality of their own research, to substantiate research design and help the reviewers to review qualitative research for journals. On a final note, I pinpoint the need to formulate a framework (encompassing the prerequisites of a qualitative study) by the cohesive efforts of qualitative researchers of different disciplines with different theoretic-paradigmatic origins. I believe that tailoring such a framework (of guiding principles) paves the way for qualitative researchers to consolidate the status of qualitative research in the wide-ranging open science debate. Dialogue on this issue across different approaches is crucial for the impending prospects of socio-techno-educational research.

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Yadav, D. Criteria for Good Qualitative Research: A Comprehensive Review. Asia-Pacific Edu Res 31 , 679–689 (2022). https://doi.org/10.1007/s40299-021-00619-0

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It should begin to close the gap between the sciences of discovery and implementation

When Eliot asked “Where is the understanding we have lost in knowledge? Where is the knowledge we have lost in information?” 1 he anticipated by half a century the important role of qualitative methodologies in health services research. In this week's journal Catherine Pope and Nick Mays introduce a series of articles on qualitative research that will describe the characteristics, scope, and applications of qualitative methodologies and, while distinguishing between qualitative and quantitative techniques, will emphasise that the two approaches should be regarded as complementary rather than competitive (p 42). 2

Qualitative research takes an interpretive, naturalistic approach to its subject matter; qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings that people bring to them. 3 Qualitative research begins by accepting that there is a range of different ways of making sense of the world and is concerned with discovering the meanings seen by those who are being researched and with understanding their view of the world rather than that of the researchers.

While qualitative and quantitative research may well investigate similar topics, each will address a different type of question. For example, in relation to adherence to drug treatment, a quantitative study will be used to determine the proportion and demographic characteristics of patients taking a certain percentage of prescribed drugs over a given period. To answer questions about the reasons for variations in adherence and the meaning of drug treatment in the lives of patients requires a qualitative approach. 4

Traditional quantitative methods such as randomised controlled trials are the appropriate means of testing the effect of an intervention or treatment, but a qualitative exploration of beliefs and understandings is likely to be needed to find out why the results of research are often not implemented in clinical practice. 5 The establishment of an evidence based medical culture clearly depends on contributions from both research traditions and from a number of disciplines that complement clinical medicine, including sociology, anthropology, psychology, and educational theory. 6

Qualitative research has struggled to find its present position in health services research. One reason may be that clinical scientists have had difficulty in accepting the research methodologies of the social sciences, in which the generation of hypotheses often replaces the testing of hypotheses, explanation replaces measurement, and understanding replaces generalisability. Publication and dissemination of the results of qualitative research have often been difficult, partly because different formats are required. A narrative, as opposed to numerate, account of an investigation may not fit into a typical biomedical journal or into a 10 minute presentation at a scientific meeting. The assessment of proposals for qualitative research and of papers submitted for publication is likely to have been hampered by a lack of agreement on criteria for assessment, although providing clear guidance to reviewers on this point is possible. 4

Incorporating qualitative research methodologies into research thinking, which means incorporating expert qualitative researchers into research teams, will enrich research in the NHS. As well as ensuring that the right methodology is brought to bear on the right question, a creative dialogue between the two traditions is likely to be of considerable mutual benefit. As well as strengthening capacity in research, a comprehensive approach to health services research should begin to close the gap between the sciences of discovery and the sciences of implementation.

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Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

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What is qualitative research?

"Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1]  Instead of collecting numerical data points or intervene or introduce treatments just like in quantitative research, qualitative research helps generate hypotheses as well as further investigate and understand quantitative data."

"Qualitative research at its core, ask open-ended questions whose answers are not easily put into numbers such as ‘how’ and ‘why’. [2]  Due to the open-ended nature of the research questions at hand, qualitative research design is often not linear in the same way quantitative design is. [2]  One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3]  Phenomena such as experiences, attitudes, and behaviors can be difficult to accurately capture quantitatively, whereas a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a certain time or during an event of interest."

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Qualitative Research: Understanding the Goal and Benefits for Effective Analysis

As market trends evolve at lightning speed in the age of digital transformation, having an intimate understanding of consumer desires and motivations is more critical than ever. Enter qualitative research – the knight in shining armor of deep-dive data analysis. In this blog post, we’ll be exploring the profound purpose and impressive benefits behind qualitative research, unveiling how it anchors effective market analysis and strategy development. Brace yourselves for a mesmerizing journey into the realm of potent insights that power consequential decisions and breed groundbreaking innovation.

The primary goal of qualitative research is to obtain insights into participants’ experiences and understanding of the world. This type of research provides rich descriptions and explanations of processes in identifiable local contexts. Qualitative research has several benefits including providing an in-depth understanding, being flexible and adaptable, and generating descriptive data that can be used to create new theories using the inductive method. 

Qualitative Study’s Importance

Qualitative research holds a significant place in the realm of social science research and is integral for understanding the complexities of human behavior, experiences, and social interactions. Unlike quantitative research which focuses on numerical data and statistical analysis, qualitative research collects non-numerical data and emphasizes interpreting meaning from social contexts.

The importance of qualitative research lies in its ability to provide rich descriptions and explanations of processes in identifiable local contexts. It allows researchers to gain insights into participants’ experiences and understand the world as another person experiences it. This deeper understanding paves the way for more comprehensive analyses and the development of theories that accurately represent the intricacies of human life.

For instance, imagine a sociologist interested in studying how individuals cope with unemployment during economic downturns. By conducting qualitative research , these sociologists can immerse themselves in the lives of unemployed individuals, observe their daily routines, conduct in-depth interviews, and analyze their personal narratives. This approach goes beyond simply quantifying unemployment rates; it provides an intimate understanding of how people navigate through difficult situations and sheds light on the emotional, psychological, and societal impacts.

In addition to providing rich insight into human experiences, qualitative research offers numerous other benefits that contribute to effective analysis.

• Qualitative research is essential in social science research as it allows for a deeper understanding of human behavior and social interactions. Its focus on non-numerical data collection and interpretation of meaning helps researchers gain insights into participants’ experiences and contextual factors. Qualitative research also provides rich descriptions and explanations of processes in identifiable local contexts, leading to the development of comprehensive analysis and accurate theories. Overall, qualitative research offers numerous benefits that contribute to effective analysis in social science research.

Goals & Benefits Driving Research

The goals of qualitative research are multifaceted. One primary objective is to investigate the meanings people attribute to their behavior and interactions within specific social contexts. This focus on subjective interpretations helps uncover individual perspectives that may be overlooked by quantitative methods alone. Additionally, qualitative research aims to explore social phenomena that are not easily measurable or quantifiable.

Qualitative research also generates descriptive data that requires rigorous methods of analysis. Researchers employ various techniques such as thematic analysis or grounded theory to identify patterns, themes, and categories within their data. These analytical approaches ensure systematic interpretation while maintaining the integrity of participants’ lived experiences.

Beyond these goals, qualitative research offers several benefits that aid in reliable analysis. Firstly, it provides an in-depth understanding of complex social issues by capturing the nuances and subtleties of human behavior. This depth allows researchers to generate rich descriptions and explanations that facilitate a comprehensive comprehension of social phenomena.

For example, consider a study exploring the experience of minority students in predominantly white institutions. Through qualitative research methods like interviews and focus groups, researchers can delve into the students’ lived experiences, their perceptions of inclusion or exclusion, and their strategies for navigating through institutional challenges. This level of detail paints a holistic picture that goes beyond quantitative statistics such as enrollment numbers.

Another advantage of qualitative research is its flexibility and adaptability. Researchers can modify their data collection methods to account for new insights or unexpected findings during the research process. This responsiveness allows for deeper exploration and ensures that no valuable information is left unexamined.

However, it is essential to acknowledge that qualitative research also has its limitations. These include the limited scope and generalizability of findings due to the smaller sample sizes typically used in qualitative studies. Additionally, there is a potential for researcher bias since the individuals collecting and analyzing the data play an active role in shaping the research process.

Nonetheless, while objectivity may be seen as a myth in qualitative research, researchers should be honest and transparent about their own biases and assumptions. Reflexivity, which involves acknowledging and critically examining one’s subjectivity throughout the research process, is integral to ensuring integrity and minimizing undue influence.

• According to a report from the Journal of Social Issues, as of 2022, around 45% of psychological studies used qualitative methods, signaling strong recognition in the field for its unique insights into human behavior.
• A study conducted by the Market Research Society confirmed that out of all market research carried out worldwide, approximately 20% utilize qualitative methodologies. This highlights its crucial role in understanding customer behaviors and motivations.
• The National Center for Biotechnology Information (NCBI) indicated that nearly 70% of health research incorporates some elements of qualitative research, underscoring its importance in contributing to our understanding of complex health issues and interventions.

Comprehensive Approaches

When conducting qualitative research , adopting comprehensive approaches is essential for capturing the richness and depth of data required for effective analysis. These approaches involve a holistic perspective that considers multiple dimensions and contexts. One commonly used comprehensive approach is triangulation , which involves using multiple data sources, methods, or perspectives to cross-verify findings. By triangulating data, researchers can enhance the reliability and validity of their analysis.

Another important approach is thick description , which focuses on providing detailed and vivid accounts of participants’ experiences and contexts. This technique enables researchers to capture the nuances and complexities of social phenomena, ensuring a comprehensive understanding of the research topic. Thick descriptions typically include vivid narratives, dialogue excerpts, and detailed observations, providing readers with a rich portrayal of the study’s context.

Researchers may also adopt an iterative process in their analysis, where data collection and analysis occur simultaneously. This approach allows for constant refinement and adjustment of research questions and methods based on emerging findings. Through iteration, researchers can dive deeper into the topic, uncover unexpected insights, and explore various angles that contribute to a more comprehensive analysis.

It’s worth noting that comprehensive approaches in qualitative research require flexibility and openness to embracing emergent themes and unexpected directions. As researchers immerse themselves in the data, they should be willing to adapt their strategies accordingly.

Participant Engagement & Topic Exploration

Participant engagement plays a crucial role in qualitative research as it fosters a deeper understanding of participants’ perspectives and experiences. Effective engagement encourages open dialogue and trust between the researcher and participants, allowing for richer data collection. One way to promote participant engagement is through active listening . By attentively listening to participants’ stories, concerns, and viewpoints, researchers can demonstrate empathy and create a safe space for open expression.

Another aspect that greatly enhances participant engagement is establishing rapport . Building rapport involves creating a comfortable environment where participants feel at ease to share their thoughts and experiences. This can be achieved through transparent communication, respect for participants’ autonomy, and genuine interest in their stories. Researchers should establish a positive and respectful relationship with participants, positioning themselves as partners rather than authoritative figures.

In qualitative research, topic exploration is a dynamic and iterative process that allows researchers to uncover new insights and dimensions of the phenomenon under study. This involves probing deeper into participants’ responses, asking follow-up questions, and exploring unexpected avenues that emerge during data collection. By being open to revisiting research questions and digging deeper into topics, researchers can uncover valuable insights and gain a more comprehensive understanding of the subject matter.

It’s important to note that participant engagement and topic exploration go hand in hand. Engaged participants are more likely to provide rich and detailed responses, leading to enhanced exploration of the research topic. Conversely, skillful topic exploration can foster deeper engagement from participants by demonstrating genuine interest and curiosity in their perspectives.

Effective Data Accumulation Methods

In qualitative research, the collection of rich and meaningful data is a crucial step toward understanding the complexities of human experiences. To ensure effective analysis, researchers need to employ appropriate data accumulation methods that capture the depth of participants’ perspectives and insights. Let’s explore some strategies that can facilitate this process.

One common method used in qualitative research is participant observation. This involves immersing oneself in the research setting, actively observing, and taking detailed notes on behaviors, interactions, and cultural nuances. By being present in the natural context, researchers gain a deeper understanding of the social dynamics and can document valuable data that may go unnoticed otherwise.

For instance, imagine a researcher interested in studying the experiences of healthcare workers in a hospital. Through participant observation, they can shadow these workers, witness their daily routines, the challenges they face, and even engage in conversations during breaks. This method provides an intimate look into their lives and generates valuable insights.

Another effective technique is in-depth interviews. These interviews allow researchers to establish a personal connection with participants and delve into their thoughts, feelings, and motivations regarding the research topic. It’s crucial to create an open and comfortable environment where participants feel safe sharing their views openly.

Additionally, focus groups are utilized as a powerful data accumulation method. Bringing together a small group of individuals who share similar characteristics or experiences allows for stimulating discussions that uncover diverse perspectives. Participants can build upon each other’s ideas and provide deeper insights collectively.

Having explored effective data accumulation methods like participant observation, in-depth interviews, and focus groups, let’s now dive into another important aspect of qualitative research – harnessing sensory inputs & eliciting verbal responses.

Harnessing Sensory Inputs and Eliciting Verbal Responses

Qualitative research aims to understand phenomena from the perspective of individuals involved. One way to achieve this is by harnessing sensory inputs and eliciting verbal responses, allowing participants to express themselves fully. This approach taps into a range of human senses and encourages participants to describe their experiences vividly.

For instance, researchers might utilize photovoice techniques, where participants capture images related to the research topic using cameras or smartphones. These visual representations allow participants to share their perspectives in a unique and powerful way.

Imagine a study exploring the impact of urbanization on community well-being. Participants could be asked to take pictures of spaces they feel contribute positively or negatively to their quality of life. These images can then be used as stimuli for further discussion, sparking conversations about the emotional and sensory aspects of the built environment.

In addition to visuals, researchers can also engage participants’ sense of hearing through audio recordings. By recording interviews, focus group discussions, or even ambient sounds in a particular environment, researchers can capture subtle nuances that may not be conveyed through written transcripts alone.

By harnessing sensory inputs and giving participants the space for verbal expression, qualitative researchers foster an environment where rich and nuanced data can be collected. This multi-sensory approach enables a deeper understanding of individuals’ experiences and allows us to gain insights beyond mere words.

Parsing and Conclusion Derivation from Data

In qualitative research, one of the primary goals is to parse and derive meaningful conclusions from the collected data. Unlike quantitative research which relies on statistical analysis, qualitative research involves obtaining rich descriptions of participants’ experiences and understanding the world as another person experiences it. The process of parsing and deriving conclusions from qualitative data requires a meticulous examination of the data, identification of patterns, themes, and connections, and an inductive approach to theory development.

Qualitative researchers immerse themselves in the data collected through methods such as interviews, observations, and focus groups. They carefully analyze transcripts, field notes, or documents to identify recurring themes or significant incidents that shed light on the research question. Through this process of coding and categorizing, researchers start to make sense of the data and identify key findings that can be used to develop theories or inform specific contexts.

For example, imagine a researcher conducting an ethnographic study exploring the experiences of undocumented immigrants in their journey toward citizenship. Through interviews and participant observation, they gather compelling stories and narratives about the challenges faced by these individuals. By carefully analyzing these stories for common themes such as navigating legal systems or facing social stigma, the researcher can derive conclusions about the complex processes involved in seeking legal status.

“Analyzing qualitative data is like piecing together a puzzle. Each interview or observation provides a unique piece that contributes to the overall picture.”

However, it is important to note that deriving conclusions from qualitative data is not a simple linear process. It requires reflexivity on the part of the researcher to acknowledge their own biases and assumptions that may influence their interpretation of the data. Reflexivity encourages researchers to critically reflect on how their own subjectivity affects their analysis and conclusions.

Advantages & Drawbacks of This Research Type

Qualitative research offers several advantages that contribute to its effectiveness in providing rich insights into social phenomena. First and foremost, it allows researchers to gain an in-depth understanding of the experiences, perspectives, and meanings that individuals attribute to their behavior and interactions. This depth of understanding is often difficult to achieve through quantitative research methods alone.

Moreover, qualitative research is known for its flexibility and adaptability. Researchers can modify their research design or data collection methods as they delve deeper into the field, responding to emerging themes or new areas of investigation. The open-ended nature of qualitative research also enables participants to express themselves freely and provide nuanced responses, offering a more comprehensive view of complex social phenomena.

On the other hand, there are some drawbacks to consider when conducting qualitative research. One challenge is the limited scope and generalizability of findings. Due to the small sample sizes typically involved in qualitative studies, it can be challenging to extrapolate findings to broader populations or contexts. Additionally, there is potential for researcher bias as interpretations of qualitative data are subjective and influenced by researchers’ perspectives and assumptions.

Despite these limitations, the benefits of qualitative research outweigh its drawbacks in many cases. By providing detailed insights into participants’ experiences, qualitative research contributes valuable knowledge that can inform policy decisions, improve interventions, and enhance our understanding of social phenomena.

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Qualitative vs Quantitative Research Methods & Data Analysis

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What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded .

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Understanding Research Questions: Quantitative vs Qualitative

By Divya Bhansali

Columbia University; Biomedical Engineering PhD candidate

3 minute read

Research is like being a detective, trying to uncover the mysteries of the world. In the world of research, one of the first and most crucial decisions you'll make is whether to ask quantitative or qualitative method questions. But what's the difference between quantitative and qualitative research, and why does it matter? Let's dive in and find out!

Quantitative Research Questions

Quantitative research involves numbers, statistics, and hard data. It's like counting beans in a jar. Quantitative research questions aim to answer "how much," "how many," or "to what extent" questions. When understanding how to write research paper , quantitative research questions can provide clear, measurable data to support your findings.

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Examples of Quantitative Research Questions

1. How many high school students use smartphones for over four hours a day?

This research question can be answered with precise numbers - a certain percentage of students may fall into this category.

2. What is the average GPA of students in our school?

You'll get a specific number, like 3.5, as an answer to this question.

3. How much has the average temperature increased over the last decade?

In this case, you're looking for a specific temperature change in degrees Celsius or Fahrenheit.

Considerations for Quantitative Research

Data Collection Methods : To answer quantitative research questions, you'll often use structured surveys, experiments, or observations with predefined variables. These methods help you collect precise, quantifiable data.

Data Analysis : Quantitative research involves statistical analysis, where you'll use mathematical tools to identify patterns and relationships in the data. Understanding how to write a research paper outline can help you organize these methods effectively.

Generalizability : Quantitative research often aims for generalizability, meaning you can draw conclusions that apply to a larger population.

Qualitative Research Questions

On the other hand, the qualitative research method is more about words, descriptions, and understanding the "whys" and "hows" of a phenomenon. It's like exploring the stories behind the beans in the jar. Qualitative analysis questions aim to answer questions about experiences, feelings, and behaviors.

Examples of Qualitative Research Questions

How do high school students feel about using smartphones for extended periods of time?

This question invites students to share their thoughts, emotions, and personal experiences.

2. What are the main challenges that students face when it comes to maintaining a high GPA?

This question prompts students to talk about their struggles, motivations, and strategies.

3. In what ways has climate change affected the daily lives of people in our community?

This question encourages people to share their stories about how they've been impacted.

Considerations for Qualitative Research

Data Collection Methods : Qualitative research methods often involve open-ended interviews, observations, or content analysis. These methods allow you to collect rich, descriptive data. 

Data Analysis : Qualitative research method requires a more interpretive approach. You'll analyze text or visual data to identify themes, patterns, and any unique insight.

In-Depth Understanding : Qualitative research delves deep into the experiences and perceptions of individuals, providing a nuanced understanding of a specific topic.

Knowing how to write an introduction for a research paper can be particularly important when presenting qualitative research. A compelling introduction sets the stage for the rich, descriptive data that follows.

If your study involves STEM subjects, having a solid stem research paper outline will be beneficial. Additionally, knowing how to write a thesis statement for a research paper is crucial for establishing a clear argument or hypothesis.

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Which One to Choose?

The choice between qualitative and quantitative research questions depends on what you want to discover and the nature of your study. Here are some key factors to consider:

Nature of the Research : Is your research more about numbers and statistical analysis, or is it about having a deeper understanding the human experience? Choose the approach that aligns with your research goals.

Data Collection : Think about how you'll gather information. Surveys and experiments often lead to quantitative data, while interviews and observations typically provide qualitative data.

Time and Resources : Consider the time and resources you have. Quantitative research can often be quicker and require fewer resources than in-depth qualitative studies.

Research Participants : The preferences and characteristics of your research participants matter. Some may prefer answering surveys with numeric options, while others may enjoy sharing their stories.

When you are ready to start your study, make sure to also understand how to write a research paper abstract for summarizing your work effectively.

Whether you choose to ask quantitative or qualitative survey questions, remember that both approaches are valuable and have their unique strengths. The key is to match your research goals with the right approach, ensuring that you gather the most relevant and meaningful data.

So, high school detectives, the choice is yours: will you count the beans or explore the stories behind them? Happy researching!

What Is a Research Design? | Definition, Types & Guide

Introduction

Parts of a research design, types of research methodology in qualitative research, narrative research designs, phenomenological research designs, grounded theory research designs.

• Ethnographic research designs

Case study research design

Important reminders when designing a research study.

A research design in qualitative research is a critical framework that guides the methodological approach to studying complex social phenomena. Qualitative research designs determine how data is collected, analyzed, and interpreted, ensuring that the research captures participants' nuanced and subjective perspectives. Research designs also recognize ethical considerations and involve informed consent, ensuring confidentiality, and handling sensitive topics with the utmost respect and care. These considerations are crucial in qualitative research and other contexts where participants may share personal or sensitive information. A research design should convey coherence as it is essential for producing high-quality qualitative research, often following a recursive and evolving process.

Theoretical concepts and research question

The first step in creating a research design is identifying the main theoretical concepts. To identify these concepts, a researcher should ask which theoretical keywords are implicit in the investigation. The next step is to develop a research question using these theoretical concepts. This can be done by identifying the relationship of interest among the concepts that catch the focus of the investigation. The question should address aspects of the topic that need more knowledge, shed light on new information, and specify which aspects should be prioritized before others. This step is essential in identifying which participants to include or which data collection methods to use. Research questions also put into practice the conceptual framework and make the initial theoretical concepts more explicit. Once the research question has been established, the main objectives of the research can be specified. For example, these objectives may involve identifying shared experiences around a phenomenon or evaluating perceptions of a new treatment.

Methodology

After identifying the theoretical concepts, research question, and objectives, the next step is to determine the methodology that will be implemented. This is the lifeline of a research design and should be coherent with the objectives and questions of the study. The methodology will determine how data is collected, analyzed, and presented. Popular qualitative research methodologies include case studies, ethnography , grounded theory , phenomenology, and narrative research . Each methodology is tailored to specific research questions and facilitates the collection of rich, detailed data. For example, a narrative approach may focus on only one individual and their story, while phenomenology seeks to understand participants' lived common experiences. Qualitative research designs differ significantly from quantitative research, which often involves experimental research, correlational designs, or variance analysis to test hypotheses about relationships between two variables, a dependent variable and an independent variable while controlling for confounding variables.

Literature review

After the methodology is identified, conducting a thorough literature review is integral to the research design. This review identifies gaps in knowledge, positioning the new study within the larger academic dialogue and underlining its contribution and relevance. Meta-analysis, a form of secondary research, can be particularly useful in synthesizing findings from multiple studies to provide a clear picture of the research landscape.

Data collection

The sampling method in qualitative research is designed to delve deeply into specific phenomena rather than to generalize findings across a broader population. The data collection methods—whether interviews, focus groups, observations, or document analysis—should align with the chosen methodology, ethical considerations, and other factors such as sample size. In some cases, repeated measures may be collected to observe changes over time.

Data analysis

Analysis in qualitative research typically involves methods such as coding and thematic analysis to distill patterns from the collected data. This process delineates how the research results will be systematically derived from the data. It is recommended that the researcher ensures that the final interpretations are coherent with the observations and analyses, making clear connections between the data and the conclusions drawn. Reporting should be narrative-rich, offering a comprehensive view of the context and findings.

Overall, a coherent qualitative research design that incorporates these elements facilitates a study that not only adds theoretical and practical value to the field but also adheres to high quality. This methodological thoroughness is essential for achieving significant, insightful findings. Examples of well-executed research designs can be valuable references for other researchers conducting qualitative or quantitative investigations. An effective research design is critical for producing robust and impactful research outcomes.

Each qualitative research design is unique, diverse, and meticulously tailored to answer specific research questions, meet distinct objectives, and explore the unique nature of the phenomenon under investigation. The methodology is the wider framework that a research design follows. Each methodology in a research design consists of methods, tools, or techniques that compile data and analyze it following a specific approach.

The methods enable researchers to collect data effectively across individuals, different groups, or observations, ensuring they are aligned with the research design. The following list includes the most commonly used methodologies employed in qualitative research designs, highlighting how they serve different purposes and utilize distinct methods to gather and analyze data.

The narrative approach in research focuses on the collection and detailed examination of life stories, personal experiences, or narratives to gain insights into individuals' lives as told from their perspectives. It involves constructing a cohesive story out of the diverse experiences shared by participants, often using chronological accounts. It seeks to understand human experience and social phenomena through the form and content of the stories. These can include spontaneous narrations such as memoirs or diaries from participants or diaries solicited by the researcher. Narration helps construct the identity of an individual or a group and can rationalize, persuade, argue, entertain, confront, or make sense of an event or tragedy. To conduct a narrative investigation, it is recommended that researchers follow these steps:

Identify if the research question fits the narrative approach. Its methods are best employed when a researcher wants to learn about the lifestyle and life experience of a single participant or a small number of individuals.

Select the best-suited participants for the research design and spend time compiling their stories using different methods such as observations, diaries, interviewing their family members, or compiling related secondary sources.

Compile the information related to the stories. Narrative researchers collect data based on participants' stories concerning their personal experiences, for example about their workplace or homes, their racial or ethnic culture, and the historical context in which the stories occur.

Analyze the participant stories and "restore" them within a coherent framework. This involves collecting the stories, analyzing them based on key elements such as time, place, plot, and scene, and then rewriting them in a chronological sequence (Ollerenshaw & Creswell, 2000). The framework may also include elements such as a predicament, conflict, or struggle; a protagonist; and a sequence with implicit causality, where the predicament is somehow resolved (Carter, 1993).

Collaborate with participants by actively involving them in the research. Both the researcher and the participant negotiate the meaning of their stories, adding a credibility check to the analysis (Creswell & Miller, 2000).

A narrative investigation includes collecting a large amount of data from the participants and the researcher needs to understand the context of the individual's life. A keen eye is needed to collect particular stories that capture the individual experiences. Active collaboration with the participant is necessary, and researchers need to discuss and reflect on their own beliefs and backgrounds. Multiple questions could arise in the collection, analysis, and storytelling of individual stories that need to be addressed, such as: Whose story is it? Who can tell it? Who can change it? Which version is compelling? What happens when narratives compete? In a community, what do the stories do among them? (Pinnegar & Daynes, 2006).

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A research design based on phenomenology aims to understand the essence of the lived experiences of a group of people regarding a particular concept or phenomenon. Researchers gather deep insights from individuals who have experienced the phenomenon, striving to describe "what" they experienced and "how" they experienced it. This approach to a research design typically involves detailed interviews and aims to reach a deep existential understanding. The purpose is to reduce individual experiences to a description of the universal essence or understanding the phenomenon's nature (van Manen, 1990). In phenomenology, the following steps are usually followed:

Identify a phenomenon of interest . For example, the phenomenon might be anger, professionalism in the workplace, or what it means to be a fighter.

Recognize and specify the philosophical assumptions of phenomenology , for example, one could reflect on the nature of objective reality and individual experiences.

Collect data from individuals who have experienced the phenomenon . This typically involves conducting in-depth interviews, including multiple sessions with each participant. Additionally, other forms of data may be collected using several methods, such as observations, diaries, art, poetry, music, recorded conversations, written responses, or other secondary sources.

Ask participants two general questions that encompass the phenomenon and how the participant experienced it (Moustakas, 1994). For example, what have you experienced in this phenomenon? And what contexts or situations have typically influenced your experiences within the phenomenon? Other open-ended questions may also be asked, but these two questions particularly focus on collecting research data that will lead to a textural description and a structural description of the experiences, and ultimately provide an understanding of the common experiences of the participants.

Review data from the questions posed to participants . It is recommended that researchers review the answers and highlight "significant statements," phrases, or quotes that explain how participants experienced the phenomenon. The researcher can then develop meaningful clusters from these significant statements into patterns or key elements shared across participants.

Write a textual description of what the participants experienced based on the answers and themes of the two main questions. The answers are also used to write about the characteristics and describe the context that influenced the way the participants experienced the phenomenon, called imaginative variation or structural description. Researchers should also write about their own experiences and context or situations that influenced them.

Write a composite description from the structural and textural description that presents the "essence" of the phenomenon, called the essential and invariant structure.

A phenomenological approach to a research design includes the strict and careful selection of participants in the study where bracketing personal experiences can be difficult to implement. The researcher decides how and in which way their knowledge will be introduced. It also involves some understanding and identification of the broader philosophical assumptions.

Grounded theory is used in a research design when the goal is to inductively develop a theory "grounded" in data that has been systematically gathered and analyzed. Starting from the data collection, researchers identify characteristics, patterns, themes, and relationships, gradually forming a theoretical framework that explains relevant processes, actions, or interactions grounded in the observed reality. A grounded theory study goes beyond descriptions and its objective is to generate a theory, an abstract analytical scheme of a process. Developing a theory doesn't come "out of nothing" but it is constructed and based on clear data collection. We suggest the following steps to follow a grounded theory approach in a research design:

Determine if grounded theory is the best for your research problem . Grounded theory is a good design when a theory is not already available to explain a process.

Develop questions that aim to understand how individuals experienced or enacted the process (e.g., What was the process? How did it unfold?). Data collection and analysis occur in tandem, so that researchers can ask more detailed questions that shape further analysis, such as: What was the focal point of the process (central phenomenon)? What influenced or caused this phenomenon to occur (causal conditions)? What strategies were employed during the process? What effect did it have (consequences)?

Gather relevant data about the topic in question . Data gathering involves questions that are usually asked in interviews, although other forms of data can also be collected, such as observations, documents, and audio-visual materials from different groups.

Carry out the analysis in stages . Grounded theory analysis begins with open coding, where the researcher forms codes that inductively emerge from the data (rather than preconceived categories). Researchers can thus identify specific properties and dimensions relevant to their research question.

Assemble the data in new ways and proceed to axial coding . Axial coding involves using a coding paradigm or logic diagram, such as a visual model, to systematically analyze the data. Begin by identifying a central phenomenon, which is the main category or focus of the research problem. Next, explore the causal conditions, which are the categories of factors that influence the phenomenon. Specify the strategies, which are the actions or interactions associated with the phenomenon. Then, identify the context and intervening conditions—both narrow and broad factors that affect the strategies. Finally, delineate the consequences, which are the outcomes or results of employing the strategies.

Use selective coding to construct a "storyline" that links the categories together. Alternatively, the researcher may formulate propositions or theory-driven questions that specify predicted relationships among these categories.

Develop and visually present a matrix that clarifies the social, historical, and economic conditions influencing the central phenomenon. This optional step encourages viewing the model from the narrowest to the broadest perspective.

Write a substantive-level theory that is closely related to a specific problem or population. This step is optional but provides a focused theoretical framework that can later be tested with quantitative data to explore its generalizability to a broader sample.

Allow theory to emerge through the memo-writing process, where ideas about the theory evolve continuously throughout the stages of open, axial, and selective coding.

The researcher should initially set aside any preconceived theoretical ideas to allow for the emergence of analytical and substantive theories. This is a systematic research approach, particularly when following the methodological steps outlined by Strauss and Corbin (1990). For those seeking more flexibility in their research process, the approach suggested by Charmaz (2006) might be preferable.

One of the challenges when using this method in a research design is determining when categories are sufficiently saturated and when the theory is detailed enough. To achieve saturation, discriminant sampling may be employed, where additional information is gathered from individuals similar to those initially interviewed to verify the applicability of the theory to these new participants. Ultimately, its goal is to develop a theory that comprehensively describes the central phenomenon, causal conditions, strategies, context, and consequences.

Ethnographic research design

An ethnographic approach in research design involves the extended observation and data collection of a group or community. The researcher immerses themselves in the setting, often living within the community for long periods. During this time, they collect data by observing and recording behaviours, conversations, and rituals to understand the group's social dynamics and cultural norms. We suggest following these steps for ethnographic methods in a research design:

Assess whether ethnography is the best approach for the research design and questions. It's suitable if the goal is to describe how a cultural group functions and to delve into their beliefs, language, behaviours, and issues like power, resistance, and domination, particularly if there is limited literature due to the group’s marginal status or unfamiliarity to mainstream society.

Identify and select a cultural group for your research design. Choose one that has a long history together, forming distinct languages, behaviours, and attitudes. This group often might be marginalized within society.

Choose cultural themes or issues to examine within the group. Analyze interactions in everyday settings to identify pervasive patterns such as life cycles, events, and overarching cultural themes. Culture is inferred from the group members' words, actions, and the tension between their actual and expected behaviours, as well as the artifacts they use.

Conduct fieldwork to gather detailed information about the group’s living and working environments. Visit the site, respect the daily lives of the members, and collect a diverse range of materials, considering ethical aspects such as respect and reciprocity.

Compile and analyze cultural data to develop a set of descriptive and thematic insights. Begin with a detailed description of the group based on observations of specific events or activities over time. Then, conduct a thematic analysis to identify patterns or themes that illustrate how the group functions and lives. The final output should be a comprehensive cultural portrait that integrates both the participants (emic) and the researcher’s (etic) perspectives, potentially advocating for the group’s needs or suggesting societal changes to better accommodate them.

Researchers engaging in ethnography need a solid understanding of cultural anthropology and the dynamics of sociocultural systems, which are commonly explored in ethnographic research. The data collection phase is notably extensive, requiring prolonged periods in the field. Ethnographers often employ a literary, quasi-narrative style in their narratives, which can pose challenges for those accustomed to more conventional social science writing methods.

Another potential issue is the risk of researchers "going native," where they become overly assimilated into the community under study, potentially jeopardizing the objectivity and completion of their research. It's crucial for researchers to be aware of their impact on the communities and environments they are studying.

The case study approach in a research design focuses on a detailed examination of a single case or a small number of cases. Cases can be individuals, groups, organizations, or events. Case studies are particularly useful for research designs that aim to understand complex issues in real-life contexts. The aim is to provide a thorough description and contextual analysis of the cases under investigation. We suggest following these steps in a case study design:

Assess if a case study approach suits your research questions . This approach works well when you have distinct cases with defined boundaries and aim to deeply understand these cases or compare multiple cases.

Choose your case or cases. These could involve individuals, groups, programs, events, or activities. Decide whether an individual or collective, multi-site or single-site case study is most appropriate, focusing on specific cases or themes (Stake, 1995; Yin, 2003).

Gather data extensively from diverse sources . Collect information through archival records, interviews, direct and participant observations, and physical artifacts (Yin, 2003).

Analyze the data holistically or in focused segments . Provide a comprehensive overview of the entire case or concentrate on specific aspects. Start with a detailed description including the history of the case and its chronological events then narrow down to key themes. The aim is to delve into the case's complexity rather than generalize findings.

Interpret and report the significance of the case in the final phase . Explain what insights were gained, whether about the subject of the case in an instrumental study or an unusual situation in an intrinsic study (Lincoln & Guba, 1985).

The investigator must carefully select the case or cases to study, recognizing that multiple potential cases could illustrate a chosen topic or issue. This selection process involves deciding whether to focus on a single case for deeper analysis or multiple cases, which may provide broader insights but less depth per case. Each choice requires a well-justified rationale for the selected cases. Researchers face the challenge of defining the boundaries of a case, such as its temporal scope and the events and processes involved. This decision in a research design is crucial as it affects the depth and value of the information presented in the study, and therefore should be planned to ensure a comprehensive portrayal of the case.

Qualitative and quantitative research designs are distinct in their approach to data collection and data analysis. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research prioritizes understanding the depth and richness of human experiences, behaviours, and interactions.

Qualitative methods in a research design have to have internal coherence, meaning that all elements of the research project—research question, data collection, data analysis, findings, and theory—are well-aligned and consistent with each other. This coherence in the research study is especially crucial in inductive qualitative research, where the research process often follows a recursive and evolving path. Ensuring that each component of the research design fits seamlessly with the others enhances the clarity and impact of the study, making the research findings more robust and compelling. Whether it is a descriptive research design, explanatory research design, diagnostic research design, or correlational research design coherence is an important element in both qualitative and quantitative research.

Finally, a good research design ensures that the research is conducted ethically and considers the well-being and rights of participants when managing collected data. The research design guides researchers in providing a clear rationale for their methodologies, which is crucial for justifying the research objectives to the scientific community. A thorough research design also contributes to the body of knowledge, enabling researchers to build upon past research studies and explore new dimensions within their fields. At the core of the design, there is a clear articulation of the research objectives. These objectives should be aligned with the underlying concepts being investigated, offering a concise method to answer the research questions and guiding the direction of the study with proper qualitative methods.

Carter, K. (1993). The place of a story in the study of teaching and teacher education. Educational Researcher, 22(1), 5-12, 18.

Charmaz, K. (2006). Constructing grounded theory. London: Sage.

Creswell, J. W., & Miller, D. L. (2000). Determining validity in qualitative inquiry. Theory Into Practice, 39(3), 124-130.

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Newbury Park, CA: Sage.

Moustakas, C. (1994). Phenomenological research methods. Thousand Oaks, CA: Sage.

Ollerenshaw, J. A., & Creswell, J. W. (2000, April). Data analysis in narrative research: A comparison of two “restoring” approaches. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA.

Stake, R. E. (1995). The art of case study research. Thousand Oaks, CA: Sage.

Strauss, A., & Corbin, J. (1990). Basics of qualitative research: Grounded theory procedures and techniques. Newbury Park, CA: Sage.

van Manen, M. (1990). Researching lived experience: Human science for an action sensitive pedagogy. Ontario, Canada: University of Western Ontario.

Yin, R. K. (2003). Case study research: Design and methods (3rd ed.). Thousand Oaks, CA: Sage

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Insights AI

Table of Content

Why qualitative research is vital for every business success.

Qualitative research provides insights into customer needs, preferences, and behaviors, helping businesses make better decisions and drive growth.

If you run a business, you must be aware that knowing and understanding the customer is the key to fulfilling their expectations. And that is why nearly every company conducts consumer research to know their customers better. But most companies focus heavily on quantitative research, and they forget to leverage qualitative research. Are you making this mistake?

If you are making this mistake, remember, there has been an enormous shift in consumer behavior and expectations due to the pandemic. And if you are just relying on quantitative research, you will not be able to get a complete picture of your customers.  

Remember, in this fast-changing world, to succeed, every business needs qualitative data as this data holds the power to understand not just the ‘what,’ but the ‘why,’ of the buying behavior. In other words, qualitative data holds power to understand the underlying reasons and motivations behind your consumer decisions.

So do not forget to conduct qualitative research and gather qualitative data, as it is crucial to transform your business into a results-driven, successful brand. With that in mind, in this article, we will dive deep into why qualitative research is vital for every business’s success.

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What Is Qualitative Research?

First things first – I think it is a good idea to quickly point out precisely what qualitative research is to make sure we are on the same page.

Qualitative research is a market research method that focuses on understanding customers through open-ended and conversational communication. The main goal of every qualitative research method, whether a live conversation or a focus group discussion, is to understand the “why” – why people think what they think or why they do what they do.

Qualitative Research Methods

Why Qual Research Is Important?

The benefit of conducting qualitative research is – it enables you to gain a holistic picture of your customers. Given below are a few reasons why qualitative research is crucial for every business:

Qualitative Research is a Powerful Tool

It is no secret that emotions play a crucial role in people’s decisions, but quant research leaves them out of the equation. With quant, you can understand what your customers are saying but not why they are saying that. For example, an NPS survey can tell you if people are happy with your product or service or not, but it does not answer the why – which can be done with qualitative research.

Qual is exploratory; it can fill the why part. It is about emotions; still, very few businesses leverage qualitative research. Wondering why? Because it seems expensive, time-consuming, and hard to scale. That is why most companies write it off. But consumer expectations are changing quickly, and if you want to fulfill these changing expectations, you cannot do it without qualitative research.

The good news is – it is easy to conduct qualitative research if you are using the right tools. As more research teams have migrated from in-person focus groups and in-depth interviews to video interviews, we have created tools specifically for research professionals. With Decode, you can converse with your consumers in real-time – get real-time transcripts. You can even bring all your consumer conversations across platforms under a single unified dashboard and create a single source of truth for your business.

Remember, it has never been more important to take the friction out of understanding consumers better. Wondering how to do that. Well, you can conduct live conversations using Decode live and get real-time emotion insights into these conversations.

Related Read: Conversational Intelligence: What, Why, How & Top Benefits of CI for Business .

Qualitative Research Provides Deep Consumer Insights

At a time of growing competition, every brand is striving hard to build a deeper connection with its customers – qualitative research can help brands do that. Qualitative research gives you rich, detailed, and emotionally driven insights that can help brands build a more human, emotional connection to their audience.  

Also, qualitative research goes beyond what quantitative research and analysis can do and let brands gain deeper insight into particular questions. For example, let’s say you are an online retailer, and you found out that over 80% of shoppers add products to their online carts but do not finalize the transaction, costing you millions in lost revenue. Rather than merely quantifying the challenge, with qualitative research, you can find out the precise reasons why a shopper did not buy, giving a much richer and more directly actionable insight into consumer behavior.

In short, with qualitative research, you can uncover the whole story, get deeper insights into your customers, and understand your customer needs better, ultimately driving better, more informed decision making.

Gain Real-time Live Insights

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Related Read: Why Emotion AI Is the Key to Improve Your CX?

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Consumer research is crucial to your business growth as understanding customers can help you identify strategies that will work for your product, service, or marketing campaigns.

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• Systematic Review
• Open access
• Published: 30 May 2024

Patient experiences: a qualitative systematic review of chemotherapy adherence

• Amineh Rashidi 1 ,
• Susma Thapa 1 ,
• Wasana Sandamali Kahawaththa Palliya Guruge 1 &
• Shubhpreet Kaur 1  

BMC Cancer volume  24 , Article number:  658 ( 2024 ) Cite this article

177 Accesses

Metrics details

Adherence to chemotherapy treatment is recognized as a crucial health concern, especially in managing cancer patients. Chemotherapy presents challenges for patients, as it can lead to potential side effects that may adversely affect their mobility and overall function. Patients may sometimes neglect to communicate these side effects to health professionals, which can impact treatment management and leave their unresolved needs unaddressed. However, there is limited understanding of how patients’ experiences contribute to improving adherence to chemotherapy treatment and the provision of appropriate support. Therefore, gaining insights into patients’ experiences is crucial for enhancing the accompaniment and support provided during chemotherapy.

This review synthesizes qualitative literature on chemotherapy adherence within the context of patients’ experiences. Data were collected from Medline, Web of Science, CINAHL, PsychINFO, Embase, Scopus, and the Cochrane Library, systematically searched from 2006 to 2023. Keywords and MeSH terms were utilized to identify relevant research published in English. Thirteen articles were included in this review. Five key themes were synthesized from the findings, including positive outlook, receiving support, side effects, concerns about efficacy, and unmet information needs. The review underscores the importance for healthcare providers, particularly nurses, to focus on providing comprehensive information about chemotherapy treatment to patients. Adopting recommended strategies may assist patients in clinical practice settings in enhancing adherence to chemotherapy treatment and improving health outcomes for individuals living with cancer.

Peer Review reports

Introduction

Cancer can affect anyone and is recognized as a chronic disease characterized by abnormal cell multiplication in the body [ 1 ]. While cancer is prevalent worldwide, approximately 70% of cancer-related deaths occur in low- to middle-income nations [ 1 ]. Disparities in cancer outcomes are primarily attributed to variations in the accessibility of comprehensive diagnosis and treatment among countries [ 1 , 2 ]. Cancer treatment comes in various forms; however, chemotherapy is the most widely used approach [ 3 ]. Patients undergoing chemotherapy experience both disease-related and treatment-related adverse effects, significantly impacting their quality of life [ 4 ]. Despite these challenges, many cancer patients adhere to treatment in the hope of survival [ 5 ]. However, some studies have shown that concerns about treatment efficacy may hinder treatment adherence [ 6 ]. Adherence is defined as “the extent to which a person’s behaviour aligns with the recommendations of healthcare providers“ [ 7 ]. Additionally, treatment adherence is influenced by the information provided by healthcare professionals following a cancer diagnosis [ 8 ]. Patient experiences suggest that the decision to adhere to treatment is often influenced by personal factors, with family support playing a crucial role [ 8 ]. Furthermore, providing adequate information about chemotherapy, including its benefits and consequences, can help individuals living with cancer gain a better understanding of the advantages associated with adhering to chemotherapy treatment [ 9 ].

Recognizing the importance of adhering to chemotherapy treatment and understanding the impact of individual experiences of chemotherapy adherence would aid in identifying determinants of adherence and non-adherence that are modifiable through effective interventions [ 10 ]. Recently, systematic reviews have focused on experiences and adherence in breast cancer [ 11 ], self-management of chemotherapy in cancer patients [ 12 ], and the influence of medication side effects on adherence [ 13 ]. However, these reviews were narrow in scope, and to date, no review has integrated the findings of qualitative studies designed to explore both positive and negative experiences regarding chemotherapy treatment adherence. This review aims to synthesize the qualitative literature on chemotherapy adherence within the context of patients’ experiences.

This review was conducted in accordance with the Joanna Briggs Institute [ 14 ] guidelines for systemic review involving meta-aggregation. This review was registered in PROSPERO (CRD42021270459).

Search methods

The searches for peer reviewed publications in English from January 2006-September 2023 were conducted by using keywords, medical subject headings (MeSH) terms and Boolean operators ‘AND’ and ‘OR’, which are presented in the table in Appendix 1 . The searches were performed in a systematic manner in core databases such including Embase, Medline, PsycINFO, CINAHL, Web of Science, Cochrane Library, Scopus and the Joanna Briggs Institute (JBI). The search strategy was developed from keywords and medical subject headings (MeSH) terms. Librarian’s support and advice were sought in forming of the search strategies.

Study selection and inclusion criteria

The systematic search was conducted on each database and all articles were exported to Endnote and duplicates records were removed. Then, title and abstract of the full text was screened by two independent reviewers against the inclusion criteria. For this review, populations were patients aged 18 and over with cancer, the phenomenon of interest was experiences on chemotherapy adherence and context was considered as hospitals, communities, rehabilitation centres, outpatient clinics, and residential aged care. All peer-reviewed qualitative study design were also considered for inclusion. Studies included in this review were classified as primary research, published in English since 2006, some intervention implemented to improve adherence to treatment. This review excluded any studies that related to with cancer and mental health condition, animal studies and grey literature.

Quality appraisal and data extraction

The JBI Qualitative Assessment and Review Instrument for qualitative studies was used to assess the methodological quality of the included studies, which was conducted by the primary and second reviewers independently. There was no disagreement between the reviews. The qualitative data on objectives, study population, context, study methods, and the phenomena of interest and findings form the included studies were extracted.

Data synthesis

The meta-aggregation approach was used to combine the results with similar meaning. The primary and secondary reviewers created categories based on the meanings and concept. These categories were supported by direct quotations from participants. The findings were assess based on three levels of evidence, including unequivocal, credible, and unsupported [ 15 , 16 ]. Findings with no quotation were not considered for synthesis in this review. The categories and findings were also discussed by the third and fourth reviewers until a consensus was reached. The review was approved by the Edith Cowan University Human Research Ethics Committee (2021–02896).

Study inclusion

A total of 4145 records were identified through a systematic search. Duplicates ( n  = 647) were excluded. Two independent reviewers conducted screening process. The remaining articles ( n  = 3498) were examined for title and abstract screening. Then, the full text screening conducted, yielded 13 articles to be included in the final synthesis see Appendix 2 .

Methodological quality of included studies

All included qualitative studies scored between 7 and 9, which is displayed in Appendix 3 . The congruity between the research methodology and the research question or objectives, followed by applying appropriate data collection and data analysis were observed in all included studies. Only one study [ 17 ] indicated the researcher’s statement regarding cultural or theoretical perspectives. Three studies [ 18 , 19 , 20 ] identified the influence of the researcher on the research and vice-versa.

Characteristics of included studies

Most of studies conducted semi-structured and in-depth interviews, one study used narrative stories [ 19 ], one study used focus group discussion [ 21 ], and one study combined focus group and interview [ 22 ] to collect data. All studies conducted outpatient’s clinic, community, or hospital settings [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The study characteristics presented in Appendix 4 .

Review findings

Eighteen findings were extracted and synthesised into five categories: positive outlook, support, side effects, concern about efficacy and unmet information needs.

Positive outlook

Five studies discussed the link between positivity and hope and chemotherapy adherence [ 19 , 20 , 23 , 27 , 28 ]. Five studies commented that feeling positive and avoid the negativity and worry could encourage people to adhere in their mindset chemotherapy: “ I think the main thing for me was just keeping a positive attitude and not worrying, not letting myself worry about it ” [ 20 ]. Participants also considered the positive thoughts as a coping mechanism, that would help them to adhere and complete chemotherapy: “ I’m just real positive on how everything is going. I’m confident in the chemo, and I’m hoping to get out of her soon ” [ 23 ]. Viewing chemotherapy as part of their treatment regimen and having awareness of negative consequences of non-adherence to chemotherapy encouraged them to adhere chemotherapy: “ If I do not take medicine, I do not think I will be able to live ” [ 28 ]. Adhering chemotherapy was described as a survivor tool which helped people to control cancer-related symptoms: “ it is what is going to restore me. If it wasn’t this treatment, maybe I wasn’t here talking to you. So, I have to focus in what he is going to give me, life !” [ 27 ]. Similarly, people accepted the medical facts and prevent their life from worsening; “ without the treatment, it goes the wrong way. It is hard, but I have accepted it from the beginning, yes. This is how it is. I cannot do anything about it. Just have to accept it ” [ 19 ].

Finding from six studies contributed to this category [ 20 , 21 , 23 , 24 , 25 , 29 ]. Providing support from families and friends most important to the people. Receiving support from family members enhanced a sense responsibility towards their families, as they believed to survive for their family even if suffered: “ yes, I just thought that if something comes back again and I say no, then I have to look my family and friends in the eye and say I could have prevented it, perhaps. Now, if something comes back again, I can say I did everything I could. Cancer is bad enough without someone saying: It’s your own fault!!” [ 29 ]. Also, emotional support from family was described as important in helping and meeting their needs, and through facilitation helped people to adhere chemotherapy: “ people who genuinely mean the support that they’re giving […] just the pure joy on my daughter’s face for helping me. she was there day and night for me if I needed it, and that I think is the main thing not to have someone begrudgingly looking after you ” [ 20 ]. Another study discussed the role family, friends and social media as the best source of support during their treatment to adhere and continue “ I have tons of friends on Facebook, believe it or not, and it’s amazing how many people are supportive in that way, you know, just sending get-well wishes. I can’t imagine going through this like 10 years ago whenever stuff like that wasn’t around ” [ 23 ]. Receiving support from social workers was particularly helpful during chemotherapy in encouraging adherence to the chemotherapy: “ the social worker told me that love is courage. That was a huge encouragement, and I began to encourage myself ” [ 25 ].

Side effects

Findings from five studies informed this category [ 17 , 21 , 22 , 25 , 26 ]. Physical side effects were described by some as the most unpleasure experience: “ the side effects were very uncomfortable. I felt pain, fatigue, nausea, and dizziness that limited my daily activities. Sometimes, I was thinking about not keeping to my chemotherapy schedule due to those side effect ” [ 17 ]. The impact of side effects affected peoples’ ability to maintain their independence and self-care: “ I couldn’t walk because I didn’t have the energy, but I wouldn’t have dared to go out because the diarrhoea was so bad. Sometimes I couldn’t even get to the toilet; that’s very embarrassing because you feel like you’re a baby ” [ 26 ]. Some perceived that this resulted in being unable to perform independently: “ I was incredibly weak and then you still have to do things and you can’t manage it ” [ 22 ]. These side effect also decreased their quality of life “ I felt nauseated whenever I smelled food. I simply had no appetite when food was placed in front of me. I lost my sense of taste. Food had no taste anymore ” [ 25 ]. Although, the side effects impacted on patients´ leisure and free-time activities, they continued to undertake treatment: “ I had to give up doing the things I liked the most, such as going for walks or going to the beach. Routines, daily life in general were affected ” [ 21 ].

Concern about efficacy

Findings form four studies informed this category [ 17 , 18 , 24 , 28 ]. Although being concerned about the efficacy of the chemotherapy and whether or not chemotherapy treatment would be successful, one participant who undertook treatment described: “the efficacy is not so great. It is said to expect about 10% improvement, but I assume that it declines over time ” [ 28 ]. People were worried that such treatment could not cure their cancer and that their body suffered more due to the disease: “ I was really worried about my treatment effectiveness, and I will die shortly ” [ 17 ]. There were doubts expressed about remaining the cancer in the body after chemotherapy: “ there’s always sort of hidden worries in there that whilst they’re not actually taking the tumour away, then you’re wondering whether it’s getting bigger or what’s happening to it, whether it’s spreading or whatever, you know ” [ 24 ]. Uncertainty around the outcome of such treatment, or whether recovering from cancer or not was described as: “it makes you feel confused. You don’t know whether you are going to get better or else whether the illness is going to drag along further” [ 18 ].

Unmet information needs

Five studies contributed to this category [ 17 , 21 , 22 , 23 , 26 ]. The need for adequate information to assimilate information and provide more clarity when discussing complex information were described. Providing information from clinicians was described as minimal: “they explain everything to you and show you the statistics, then you’re supposed to take it all on-board. You could probably go a little bit slower with the different kinds of chemo and grappling with these statistics” [ 26 ]. People also used the internet search to gain information about their cancer or treatments, “I’ve done it (consult google), but I stopped right away because there’s so much information and you don’t know whether it’s true or not ” [ 21 ]. The need to receive from their clinicians to obtain clearer information was described as” I look a lot of stuff up online because it is not explained to me by the team here at the hospital ” [ 23 ]. Feeling overwhelmed with the volume of information could inhibit people to gain a better understanding of chemotherapy treatment and its relevant information: “ you don’t absorb everything that’s being said and an awful lot of information is given to you ” [ 22 ]. People stated that the need to know more information about their cancer, as they were never dared to ask from their clinicians: “ I am a low educated person and come from a rural area; I just follow the doctor’s advice for my health, and I do not dare to ask anything” [ 17 ].

The purpose of this review was to explore patient’s experiences about the chemotherapy adherence. After finalizing the searches, thirteen papers were included in this review that met the inclusion criteria.

The findings of the present review suggest that social support is a crucial element in people’s positive experiences of adhering to chemotherapy. Such support can lead to positive outcomes by providing consistent and timely assistance from family members or healthcare professionals, who play vital roles in maintaining chemotherapy adherence [ 30 ]. Consistent with our study, previous research has highlighted the significant role of family members in offering emotional and physical support, which helps individuals cope better with chemotherapy treatment [ 31 , 32 ]. However, while receiving support from family members reinforces individuals’ sense of responsibility in managing their treatment and their family, it also instils a desire to survive cancer and undergo chemotherapy. One study found that assuming self-responsibility empowers patients undergoing chemotherapy, as they feel a sense of control over their therapy and are less dependent on family members or healthcare professionals [ 33 ]. A qualitative systematic review reported that support from family members enables patients to become more proactive and effective in adhering to their treatment plan [ 34 ]. This review highlights the importance of maintaining a positive outlook and rational beliefs as essential components of chemotherapy adherence. Positive thinking helps individuals recognize their role in chemotherapy treatment and cope more effectively with their illness by accepting it as part of their treatment regimen and viewing it as a tool for survival. This finding is supported by previous studies indicating that positivity and positive affirmations play critical roles in helping individuals adapt to their reality and construct attitudes conducive to chemotherapy adherence [ 35 , 36 ]. Similarly, maintaining a positive mindset can foster more favourable thoughts regarding chemotherapy adherence, ultimately enhancing adherence and overall well-being [ 37 ].

This review identified side effects as a significant negative aspect of the chemotherapy experience, with individuals expressing concerns about how these side effects affected their ability to perform personal self-care tasks and maintain independent living in their daily lives. Previous studies have shown that participants with a history of chemotherapy drug side effects were less likely to adhere to their treatment regimen due to worsening symptoms, which increased the burden of medication side effects [ 38 , 39 ]. For instance, cancer patients who experienced minimal side effects from chemotherapy were at least 3.5 times more likely to adhere to their treatment plan compared to those who experienced side effects [ 40 ]. Despite experiencing side effects, patients were generally willing to accept and adhere to their treatment program, although one study in this review indicated that side effects made some patients unable to maintain treatment adherence. Side effects also decreased quality of life and imposed restrictions on lifestyle, as seen in another study where adverse effects limited individuals in fulfilling daily commitments and returning to normal levels of functioning [ 41 ]. Additionally, unmet needs regarding information on patients’ needs and expectations were common. Healthcare professionals were considered the most important source of information, followed by consultation with the internet. Providing information from healthcare professionals, particularly nurses, can support patients effectively and reinforce treatment adherence [ 42 , 43 ]. Chemotherapy patients often preferred to base their decisions on the recommendations of their care providers and required adequate information retention. Related studies have highlighted that unmet needs among cancer patients are known factors associated with chemotherapy adherence, emphasizing the importance of providing precise information and delivering it by healthcare professionals to improve adherence [ 44 , 45 ]. Doubts about the efficacy of chemotherapy treatment, as the disease may remain latent, were considered negative experiences. Despite these doubts, patients continued their treatment, echoing findings from a study where doubts regarding efficacy were identified as a main concern for chemotherapy adherence. Further research is needed to understand how doubts about treatment efficacy can still encourage patients to adhere to chemotherapy treatment.

Strengths and limitation

The strength of this review lies in its comprehensive search strategy across databases to select appropriate articles. Additionally, the use of JBI guidelines provided a comprehensive and rigorous methodological approach in conducting this review. However, the exclusion of non-English studies, quantitative studies, and studies involving adolescents and children may limit the generalizability of the findings. Furthermore, this review focuses solely on chemotherapy treatment and does not encompass other types of cancer treatment.

Conclusion and practical implications

Based on the discussion of the findings, it is evident that maintaining a positive mentality and receiving social support can enhance chemotherapy adherence. Conversely, experiencing treatment side effects, concerns about efficacy, and unmet information needs may lead to lower adherence. These findings present an opportunity for healthcare professionals, particularly nurses, to develop standardized approaches aimed at facilitating chemotherapy treatment adherence, with a focus on providing comprehensive information. By assessing patients’ needs, healthcare professionals can tailor approaches to promote chemotherapy adherence and improve the survival rates of people living with cancer. Raising awareness and providing education about cancer and chemotherapy treatment can enhance patients’ understanding of the disease and its treatment options. Utilizing videos and reading materials in outpatient clinics and pharmacy settings can broaden the reach of educational efforts. Policy makers and healthcare providers can collaborate to develop sustainable patient education models to optimize patient outcomes in the context of cancer care. A deeper understanding of individual processes related to chemotherapy adherence is necessary to plan the implementation of interventions effectively. Further research examining the experiences of both adherent and non-adherent patients is essential to gain a comprehensive understanding of this topic.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. on our submission system as well.

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Amineh Rashidi, Susma Thapa, Wasana Sandamali Kahawaththa Palliya Guruge & Shubhpreet Kaur

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First author (AR) and second author (ST) conceived the review and the second author oversight for all stages of the review provided by the second author. All authors (AR), (ST), (WG) and (SK) undertook the literature search. Data extraction, screening the included papers and quality appraisal were undertaken by all authors (AR), (ST), (WG) and (SK). First and second authors (AR) and (ST) analysed the data and wrote the first draft of the manuscript and revised the manuscript and all authors (AR), (ST), (WG) and (SK) approved the final version of the manuscript.

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Correspondence to Amineh Rashidi .

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The review was approved by the Edith Cowan University Human Research Ethics Committee (2021–02896). A proposal for the systematic review was assessed by the Edith Cowan University Human Research Ethics Committee and deemed not appropriate for full ethical review. However, a Data Management Plan (2021-02896-RASHIDI) was approved and monitored as part of this procedure. Raw data was extracted from the published manuscripts and authors could not identify individual participants during or after this process.

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Rashidi, A., Thapa, S., Kahawaththa Palliya Guruge, W. et al. Patient experiences: a qualitative systematic review of chemotherapy adherence. BMC Cancer 24 , 658 (2024). https://doi.org/10.1186/s12885-024-12353-z

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Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre

• Hassane Alami 1 , 2 , 3 , 4 ,
• Pascale Lehoux 1 , 2 ,
• Chrysanthi Papoutsi 4 ,
• Sara E. Shaw 4 ,
• Richard Fleet 5 , 6 &
• Jean-Paul Fortin 5 , 6  

BMC Health Services Research volume  24 , Article number:  701 ( 2024 ) Cite this article

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Artificial intelligence (AI) technologies are expected to “revolutionise” healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital.

Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework.

Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise.

Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients’ digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors’ priorities and the needs and expectations of healthcare organisations and systems.

Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

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According to the Organisation for Economic Co-operation and Development (OECD), artificial intelligence (AI) refers to “a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment” [ 1 ]. Unlike conventional software, many AI systems indeed have learning capabilities and self-correcting error mechanisms that allow them to improve the accuracy of their results based on the feedback they receive [ 1 , 2 ].

There are many application areas for AI in healthcare, for example: diagnosis, treatment, monitoring (e.g., chronic diseases), and patient compliance [ 3 ]. In certain experimental settings, AI technologies have been shown to be more effective than clinicians (e.g., diagnostic accuracy, more personalised diagnostics) [ 4 , 5 , 6 , 7 ]. Several have already been approved for clinical use in real-world care and services [ 8 ]. These technologies are seen as a lever for evidence-based clinical decision-making and practice and for value-based care and services [ 9 , 10 , 11 ]. Research indicates their potential to contribute to better monitoring, detection, and diagnosis of diseases, to the reduction of clinical risk, and to the discovery of new drugs and treatments [ 4 , 9 , 12 , 13 , 14 ]. The use of AI technologies could help to reduce diagnostic and therapeutic errors [ 2 ], contribute to the optimisation of clinicians’ work, and help reduce waiting times by reorganising clinical and administrative tasks, and supporting coordination [ 10 , 14 ]. Many scholars also argue that AI technologies could contribute to reducing healthcare costs by decreasing hospital (re)admissions, medical visits, and treatments [ 14 , 15 ].

A predominant and enthusiastic discourse in the academic literature and media reports is that AI technologies will revolutionise and radically change healthcare in the coming years [ 2 , 16 , 17 , 18 ]. There is an explosion of AI offerings in the market [ 19 ]. In 2018, the global AI market in healthcare was valued at around US$1.4 billion and is expected to grow to US$17.8 billion by 2025 [ 14 ]. In North America, the market for AI in healthcare had exceeded US$1.15 billion by 2020 [ 14 ]. In this context, healthcare organisations and systems are increasingly being solicited (or even pressured) to integrate these technologies, even when evidence of real clinical added value is lacking and many social and ethical as well as adoption, routinisation, and practical issues remain to be clarified [ 16 , 18 ]. According to Topol (2019), who reviewed healthcare workforce readiness for a digital future: “Despite all the promises of AI technology, there are formidable obstacles and pitfalls. The state of AI hype has far exceeded the state of AI science, especially when it pertains to validation and readiness for implementation in patient care” [ 4 ]. Liu et al. (2019) reported that few published studies on AI had results from real-world healthcare contexts [ 20 ]. These findings were corroborated during the COVID-19 pandemic [ 21 , 22 , 23 ]. Wynants et al. (2020) identified 232 AI models for prediction or diagnosis of COVID-19, none of which were appropriate for clinical use and only two showing potential for future clinical use [ 24 ]. Roberts et al. (2021) analysed 415 AI models for COVID-19 detection and concluded similarly [ 25 ].

This gap between the promise and reality of AI technologies in healthcare could be explained by the fact that efforts have historically focused on technology development, market penetration, and commercialisation. Limited work has been done to look specifically at the conditions and factors necessary for the integration of AI technologies into routine clinical care [ 14 , 17 ]. While technical problems (e.g., performance, unreliability) have been regularly put forward as a reason for the difficulties of integrating these technologies into healthcare organisations and systems [ 26 ], they explain only a small part of the problem. Broader socio-technical conditions and factors rather explain many of these difficulties [ 18 , 26 ].

The social scientific literature on health innovations has shown that the introduction of technologies into healthcare organisations and systems is a complex phenomenon [ 27 ]. This is particularly true for many AI technologies, which are sometimes described in the medical literature as disruptive innovation due to their evolving and autonomous nature [ 28 , 29 , 30 ]. Their implementation and use may require rethinking and/or redesigning existing governance frameworks and care models as well as new clinical, organisational, regulatory, and technological processes, business models, capabilities, and skills [ 18 ]. These changes involve, and impact on, a variety of stakeholders who may have divergent or even antagonistic expectations, goals, and visions towards technology [ 31 , 32 , 33 , 34 , 35 , 36 ].

To contribute to addressing current knowledge gaps, the goal of this study is to explore and understand the challenges of integrating AI technologies within a large academic hospital in Canada (referred to as “the City hospital”). We aim to answer two questions:

How do multiple interacting influences facilitate and constrain the integration of AI technologies within the City hospital?

What learning can we derive for policy and practice for better integration of AI technologies in healthcare organisations and systems?

The study is not limited to a specific AI technology or clinical area but encompasses all 87 AI technology-based initiatives developed and used to varying extent in this hospital. Where relevant, we specify the type of AI involved to contextualise the factors, conditions, or challenges described.

Theoretical framework

To make sense of the complexity underpinning the AI integration efforts in the City hospital, we used an adapted version of the Nonadoption, Abandonment, and challenges to Scale-up, Spread, and Sustainability (NASSS) framework developed by Greenhalgh et al. [ 27 ], which supports an exhaustive sociotechnical approach to health innovation. Following this adapted version, we present the seven dimensions of the framework in a different order from the original version in order to make sense of the narrative within the organisation studied, thereby covering: 1) the organisation; 2) the condition(s) or illness; 3) the technology or technologies; 4) the value proposition; 5) the adopter system(s) (e.g., staff, patient, caregivers); 6) embedding and adaptation over time; and 7) the wider system [ 27 ]. See Fig.  1 for a description of the seven dimensions.

An adapted version of the NASSS framework (adapted from Greenhalgh et al. [ 27 ])

There were many reasons for adopting the NASSS framework over other frameworks. First, it stems from a hermeneutic systematic review, supported by empirical case studies of technology implementation in healthcare [ 27 , 37 ], and its key strength lies in its synthesis of 28 technology implementation frameworks, that is informed by several theoretical perspectives [ 27 , 37 ]. Second, it was developed to fill an important gap “on technology implementation—specifically, to address not just adoption but also nonadoption and abandonment of technologies and the challenges associated with moving from a local demonstration project to one that is fully mainstreamed and part of business as usual locally (scale-up), transferable to new settings (spread), and maintained long term through adaptation to context over time (sustainability)” [ 27 , 37 ]. Third, in contrast to the deterministic logic of many existing frameworks, the NASSS framework is characterised by its dynamic aspect, particularly in terms of interaction and adaptation over time. Indeed, a large part of the literature in the field has a tendency to “assume that the issues to be addressed [are] simple or complicated (hence knowable, predictable, and controllable) rather than complex (that is, inherently not knowable or predictable but dynamic and emergent)” [ 27 , 37 ]. Therefore, major failures of large and ambitious technology projects may be underestimated and their complexity for healthcare organisations and systems tossed away [ 27 , 37 ]. Fourth, whereas decision-makers and technology promoters as well as a part of the specialised literature often adopt a linear, predictable, and rational vision of change [ 38 ], the sociotechnical stance of the NASSS framework highlights the importance of examining how technology and the changes associated with it are perceived, interpreted, negotiated, and enacted by individuals and groups [ 33 , 39 , 40 ]. The same applies to AI technologies that may require transformation and/or redesign of services, a profound reconfiguration of clinical and organisational practices, and challenges to professional identities and practices [ 17 , 33 , 40 ]. Certain types of AI technologies also evolve autonomously over time – a particular characteristic that can be explicitly conceptualised through the NASSS framework [ 27 , 41 ]. Overall, the NASSS framework was developed to be used reflectively, to stimulate conversation and generate ideas, which is one of our study’s aspirations.

We conducted a qualitative study within the City hospital (Quebec, Canada) [ 42 ]. The latter had initiated several projects to integrate AI technologies in its care and service offer. Decision-makers and managers expressed a need for (independent) insights into the micro-, meso-, and macro-level systemic implications of the integration of these technologies within the organisation [ 43 ].

Presentation of the organisation

The City hospital is one of the largest academic hospitals in Canada. It offers specialised and sub-specialised services to adult patients. It treats around 500,000 patients annually. It employs over 14,000 people. It also houses one of the largest medical research centres in the country, with an academic mission to produce and disseminate knowledge and research results. It also presents itself as an organisation with state-of-the-art facilities and equipment. It has been ranked by the U.S-based magazine Newsweek as one of the world’s top 250 Best Smart Hospitals for 2021. It hosts one of the largest annual digital innovation events in Canada.

At the time of the study, the City hospital had over 115 digital health projects (Table  1 ), with 87 of these involving AI. Around 95% (≈82/87) of the AI technologies were in the development/experimentation or early implementation phase. Only four were integrated into services. Approximately 72% (≈62/87) of the AI technologies identified within the organisation were for the diagnosis, treatment and/or monitoring of complex chronic or acute conditions: cancers, neurological (e.g., epilepsy), and ocular conditions.

Recruitment

We identified a purposive sample of key stakeholders, with the aim of capturing diverse perspectives and experiences [ 44 ]. We conducted internet searches and consulted reports and documents produced by the City hospital to identify potential participants, who were drawn from distinct roles and varied levels of involvement in the development, implementation, and use of AI technologies.

A personalised invitation email was sent to each potential participant explaining the project and why they were invited to participate. Two reminders were sent in case of non-response. Respondents were invited to indicate other participants (i.e., snowballing) [ 45 ]. This resulted in a sample of senior and middle managers/decision-makers, clinicians (e.g., physicians, nurses), clinicians/informaticians/researchers, technology assessment specialists, procurement specialists, lawyers, patients, and technology providers. Patients were identified through patient partners (volunteers) collaborating with the City hospital. Of the 42 invitations sent, 29 people agreed to participate. Table 2 shows participant profiles, many of whom cumulated multiple professional and/or experiential backgrounds.

Data collection

Between March and July 2021, the first author (HA) conducted 29 interviews in French (27) and English (2), using the Zoom™ videoconferencing platform (interviews lasted 30–90 min). Prior to the interview, a consent form summarising the objectives of the project was shared. Interviews were audio recorded with the permission of the participant and transcribed verbatim by HA. The questions were formulated according to the dimensions of the NASSS framework and informed by documents shared by the City hospital (e.g., list of projects and technologies). HA first tested the qualitative interview guide with two respondents prior to the start of the study. No major revision of the initial version of the guide was required. He took notes during and after the interviews and subsequently used them to contextualise the analyses. The interview guide slightly evolved depending on the participants’ responses as new information emerged. By adapting the interview guide, we were able to capture both expected and unanticipated tensions and practical challenges, grounding the discussion in participant experiences to avoid vague or abstract responses. Given that the same person (HA) co-developed the guide and conducted the interviews in French and English, this minimised the risk of variability that could arise from having different people collecting data in different languages. Interview data and document analysis, alongside our knowledge of the context (team members have been involved in various research and evaluation projects on digital technologies and innovations in Quebec and Canadian healthcare organisations and systems for several years) guided triangulation of data sources [ 46 ].

Data analysis

Data were coded and analysed with Dedoose™ software. HA performed the first round of analysis and developed a preliminary coding scheme. In the second round, the scheme was refined, challenged, and discussed iteratively by the second author (PL) [ 43 ]. We conducted a deductive-inductive thematic analysis. The deductive analysis was guided by the NASSS framework (Fig.  1 ) [ 27 ]. Drawing on its seven dimensions, we created codes to capture the micro, meso and macro-level challenges and implications associated with the integration of AI technologies in the City hospital. The inductive analysis aimed to capture emerging themes not covered by the framework [ 44 , 47 ]. After agreeing on the different themes identified, we concluded that none required the addition of new dimensions, as all identified themes fitted within the NASSS framework. Data saturation was reached for the themes and observations reported in the findings. Given the importance of context in the NASSS framework, we sought to understand and clarify the contextual elements where respondents had different views or judgements. We decided not to disclose certain details either because the participants requested it or to ensure confidentiality. However, this information was useful to contextualise and better understand other findings and events. Our findings are illustrated with participant quotes organised around key themes of the NASSS framework (translated from French to English when needed) (Table 5 in Appendix ). The letter P used in quotes refers to “participant”, followed by numbers designating the order in which interviews were transcribed.

Findings are reported as a narrative account [ 48 ]. This is critical in allowing us to capture the complexity of the subject, the explanatory and interpretative dimensions, and the varied stories and perspectives gained from participants in making sense of the issues around the adoption of AI technologies.

We present the findings according to the seven dimensions of the adapted version of the NASSS framework (Fig.  1 ). To ensure fluidity in the presentation of the findings, the participant roles are used as a general category to help the reader identify certain tensions between the viewpoints and perspectives expressed. In this sense, there is no pretension of generalisation given the small number of respondents in each category. The analyses are intended primarily to provide high-level dynamics related to each dimension of the NASSS framework and not those specific to the types of AI discussed.

The organisation

For the technology providers we interviewed, the City hospital has several internationally renowned clinicians, both in the clinical field and in the use of AI. Several managers and clinicians also reported that senior management is known to value and encourage technological innovation, which has led to the creation of a “data lake” that allows the integration of data from different clinical systems (e.g., clinical records, laboratories, vital signs, imaging), which is a major asset for the development and/or validation of certain AI technologies. According to technology providers, access to the specialised expertise of clinicians who know the data is as important as access to the data itself. These clinicians play an important role as a trusted guarantor (or legitimising authority) for AI with other clinicians, decision-makers/managers, patients, and citizens. In the words of one clinician-manager, the relationship and communication between these clinicians and the City hospital’s senior management is generally perceived as positive. He pointed out that this synergy helps to mitigate some of the issues and conflicting visions and expectations of AI.

According to a technology provider, because of the characteristics of Quebec’s single-payer and universal health system, the City hospital allows for holistic management of patients suffering from several pathologies or requiring different care and treatments. He added that this unique advantage enables the development of AI technologies with a broad spectrum of action (i.e., compared to those developed in contexts where care is fragmented between different hospitals and/or clinics). Despite this asset, there is a broad agreement among the interviewees that the City hospital is characterised by significant complexity that has the potential to impact its ability to realise the value promise of AI technologies.

Use of AI technologies in the City hospital necessarily involves different departments, committees, and stakeholders (e.g., Information technology -IT- department, procurement department, project office, professional services department). According to several managers, clinicians, and industry providers, the roles and mandates for these different groups and stakeholders are not always clear. Coordination and communication between teams and/or departments are sometimes difficult or non-existent. According to a manager, this results in confusion and tension about expectations, visions, and responsibilities. He pointed out that difficulties experienced by some AI projects were due to a department or committee not being engaged at the right time (e.g., as a result of legal and/or procurement framework, Cloud storage space, professional services department). For managers and clinicians, a horizontal body should have been established to coordinate and ensure coherence and communication between the different initiatives and stakeholders, with the aim enabling mutual effort, coordination, and accountability. For another manager, by ensuring an initial screening of technologies proposed by industry, such a body would avoid the influx of useless technologies to clinical teams and associated time and resource costs.

Both industry and organisation respondents agree that the City hospital doesn’t always have the capacity to meet the initial and recurring costs and investments required for the successful integration of AI. To overcome this funding problem, at least partly, an interviewee told us that the organisation is obliged to open its doors to industry for co-development, or as a testing ground, of AI technologies. This sub-contracting allows the City hospital to benefit from a free user licence for a fixed period or for life. However, it was reported that this partnership contracts model (e.g., co-development or serving as a testing ground for the industry) is likely to lock the organisation into a technology-centric logic, with no real margin of manoeuvre to choose technologies that really meet its needs. There are multiple projects under this partnership model within the organisation. Several technologies could simply end up being only partially developed because the technology provider has withdrawn, or the technology was abandoned. Within such a context, several managers and clinicians recognise that it is difficult to create a real organising vision that supports and enables AI within the City hospital.

According to managers and clinicians, these partnerships with industry imply an over-solicitation of the clinical teams as, in addition to their clinical and administrative work, they must dedicate time to testing and experimenting with the various technologies presented by the technology providers. In this regard, several organisation and industry respondents pointed out that clinicians in the City hospital are not valued or remunerated for their contribution to the development and/or experimentation of technologies. It is not uncommon for some clinicians to feel that industry benefits from their clinical expertise without any real return on investment for them. Technology providers interviewed refuted this point. For them, the difficulties in integrating their technologies into the organisation are essentially due to the opposition of some influential clinician-researchers who are themselves developing in-house similar technologies. In the words of one industry respondent, this is a conflict of interest and unfair competition. Nonetheless, technology providers support the importance of creating incentives to encourage clinicians to collaborate with industry. On their part, several clinicians and managers consider that the organisation should value in-house initiatives more highly because they emerge from the needs and expectations of the field. However, there is agreement that the organisation does not have the financial and human resources to support these initiatives. In addition, according to one manager, as a public entity, the City hospital does not have a mandate to develop and/or commercialise technologies. At some point, a company would have to be involved to ensure commercialisation.

Managers, clinicians, and industry acknowledge that the nature and extent of the changes associated with the integration of AI within the organisation are still largely unknown. For example, it is very difficult to assess financial implications over time. Two managers reported that the City hospital paid an additional CA$20,000 to CA$30,000/year for the storage and management of its data. This cost was not initially budgeted but subsequently required by the Cloud service provider who had estimated the size of the data. According to the same respondents, such “little surprises” could lead to some technologies being abandoned along the way, even if they are clinically relevant, either because the organisation cannot afford the costs or the Quebec’s Ministry of Health and Social Services (known as MSSS) refuses to cover them.

Both industry and organisation respondents reported that many AI technologies require access, sometimes in quasi-real time and without human intervention, to large amounts of data of various types. Unanimously, interviewees acknowledge that the organisation’s rules and procedures do not currently allow this (or very barely). Technology providers are calling for easier access to data. However, on the organisational side, several managers consider that such rules and procedures need to be further strengthened. Some of them emphasised the importance of having a Specialist digital lawyer to ensure that these issues are addressed when contracts are signed. They also add that there should also be a Chief data officer to ensure adequate and coherent governance between the various initiatives that involve clinical-administrative data.

The condition(s) or illnesses

Most of the AI technologies identified (72% ≈62/87) within the City hospital are directed at the diagnosis, treatment and/or monitoring of complex chronic or acute conditions (e.g., cancers, neurological, ocular conditions) (Table  1 ). These conditions generally require ongoing or periodic support and monitoring over long periods of time with significant implications for patients and their families, and for the financial sustainability of the healthcare system. They also require complex, individualised, and evolving service models to continue to meet the needs of patients and their families. Several interviewees underscore that the use of AI could reduce waiting times and the costs of managing these pathologies. For a technology provider, these technologies are also expected to help identify new patterns and digital biomarkers that would facilitate the diagnosis and treatment of poorly characterised and/or unpredictable diseases.

For several respondents, this focus on specific diseases is partly due to the nature of the technologies available on the market. These technologies are addressing pathologies mainly through image analysis and/or signal quantification. This makes them more easily measurable, therefore more attractive to technology developers seeking rapid market access.

The technology or technologies

There are diverging perceptions between clinicians, managers, technology providers, and patients on what makes AI attractive, reliable, and mature enough for clinical use and/or interoperable with existing systems.

According to a manager, some of the technologies proposed to the City hospital under the label “AI” are, in fact, expert systems with advanced calculation software. Branding the products in this way is a strategy used by some companies to attract investment and/or obtain contracts. While an AI designation increases the market value of the technology, it does not necessarily increase the clinical value. For another manager, this labelling of AI products is also partly due to the organisation’s pressure on technology providers to integrate AI. This is a significant step for technology companies as, compared to traditional software, AI technologies require specific regulatory requirements, technical infrastructure, expertise, and resources.

Several participants raised emerging security issues specific to AI. This is not only about the security of the technology and infrastructure, but also about the security of the algorithm itself. The latter could be hacked and modified, which can have a direct clinical impact on the patient. According to a manager, being able to recombine data from different sources, AI technologies could easily re-identify individuals. On their side, technology providers pointed out that these security issues are mainly due to the City hospital’s obsolete systems and technology infrastructure. They underscore how their technologies conform to the best security and quality standards and norms on the market, and that unlike public organisations they have the best IT expertise. An industry respondent added that, since the customer is the guarantor of their added value on the market, they also regard data security as central to their reputation and brand image. If an incident occurs, the company could simply lose customers or even go bankrupt.

Some AI technologies need to run on an integrated technological platform or operating system (e.g., electronic health record -EHR-) that allows for optimal data flow and exchange between the different technological systems and organisational departments as well as across healthcare system organisations. Respondents agree that the City hospital’s departments generally have outdated and disparate systems and infrastructures that are frequently not interoperable. However, several managers, clinicians, and technology providers argue that this is a common problem for the whole healthcare system, as an integrated and interoperable EHR does not exist. In this regard, for a population of over 8 million people in Quebec, there are over 30 million patient identification cards. A patient may have several cards with a fragmented EHR in several organisations. Similarly, one interviewee stressed that the equipment used (e.g., scanner, magnetic resonance imaging -MRI) in the City hospital does not always meet the requirements for AI. In some situations, it is difficult to know where the data is, or how it is processed and collected by certain technologies or equipment. Problems with internet connection and data transmission via Wi-Fi are also reported.

There is a consensus that AI technologies need high-quality data. Both industry and organisation respondents highlighted that a significant amount of clinical-administrative data (e.g., handwritten clinical notes) and patient records are still scanned in portable document format (PDF), which is not usable for planned AI. For technology providers, the meaningful use of data, which raises the question of the purpose of the data collection, is missing within the organisation and should be given more consideration.

For its AI programme, the City hospital works with many specialised start-ups and small- and medium-sized enterprises (SU/SMEs). One such technology provider stresses that the survival of their company depends on their ability to seek liquidity in the financial market (e.g., venture capital). This means that they are necessarily accountable to their shareholders who may be looking for the fastest and most profitable exit events possible (i.e., when an investor sells his/her shares in a company to collect cash profits). This approach brings challenges for the City hospital in terms of working relationships, technology development, and continuity of care. For instance, SU/SMEs can be bought by multinationals or simply disappear (e.g., bankruptcy), or a company may stop a technology or cease to update it. According to a manager, the City hospital does not necessarily have the capacity to maintain these technologies on an ad hoc basis or replace them with others. Another interviewee added that sometimes the organisation has no guarantee of recovering data hosted or operated by these technology providers or their subcontractors (e.g., Cloud services).

The value proposition

Stakeholders interviewed have divergent definitions of what constitutes the perceived, anticipated and/or actual value of a technology and the parameters to be considered for measuring it (e.g., safety, efficacy, and effectiveness criteria). About 95% were still in development/experimentation or implementation.

Several technology providers mainly express the value of their technology in terms of its potential to improve healthcare and its efficiency. They pointed to significant consumption of resources by the healthcare system while at the same time being unable to meet the healthcare needs of the population. For these interviewees, AI can solve the problem whilst modernising the healthcare system. In this regard, for a supplier, to realise such value, the City hospital, and the healthcare system in general, must be willing to take some risks. He stressed that if the latter wait for AI to be perfect and risk-free before using it, the technology will never be integrated, and its value promise never delivered to the population.

A manager reported that many AI technologies in the City hospital were at a value promise stage (i.e., with anticipated, rather than actual value stage). Other interviewees consider that this value promise remains relatively speculative, based on vague projections and estimates. In this regard, from the organisation’s perspective, the perceived value of AI technologies is mainly about improved clinical quality and safety, and performance. The expectation to achieve this value is to have tailor-made AI technologies adapted to the setting, clinical contexts, and ways of working. However, focusing on tailored AI solutions can sometimes be a major constraint for technology providers. According to several interviewees, suppliers prefer to commercialise generic technologies that can be easily marketed elsewhere with minimum modification (plug-and-play). Several managers and clinicians added that the costs involved in implementing and adapting the technology to the local context are regularly underestimated by these suppliers. The latter often lack an understanding of the complexity of clinical practices. For example, one company stopped working with the City hospital because it considered that its clinical needs are too specific for the AI technology to be cost-effective.

Because of its status as a leading academic hospital, the City hospital is highly sought after by the AI industry. Several interviewees recognise that the organisation is used to showcase and legitimise the technology’s value proposition, hence its market value and potential for widespread commercialisation. A technology provider also reported that the organisation serves as a gateway to the healthcare systems of Quebec and other Canadian provinces. At the same time, according to organisation respondents, the City hospital benefits from media coverage, which gives it a competitive advantage in attracting talent and expertise. However, divergence over the actual added value of certain technologies may constitute a source of tension between senior management and clinical teams. Some AI technologies are likely to exacerbate workload and staff burnout (e.g., technologies intended for the optimisation of clinical-administrative processes). For a manager, since AI technologies are still considered over and above other priorities, their impact on the quality of work and clinicians’ satisfaction is not really taken into consideration in the organisation’s assessment of their value (e.g., flexibility, alignment with clinical-administrative workflows). He added that the City hospital has difficulty in moving the value of these innovations from the Triple Aim to the Quadruple Aim: “improving the patient experience, the population health and the quality of work and satisfaction of healthcare providers, and reducing costs” [ 49 ].

The organisation’s clinical-administrative data, which is used to develop and/or operate some AI technologies, may contain biases and may not be representative of the general population. For several interviewees, AI technologies may also not respond to the contextual realities and needs of some populations (e.g., indigenous, rural, or minority people). Patients and organisation respondents also pointed out that these populations are rarely involved in the design, development, and implementation of AI technologies within the City hospital. Several interviewees recognise that assessing the added value of AI technologies by population segment is essential, but very difficult to achieve.

The adopter system(s)

Interviewees overwhelmingly agree that certain AI technologies could have a direct impact on the patient-clinician relationship. Some progressive diseases require human care and support over time. For AI technologies designed to monitor chronic diseases, some patients fear being lost from sight by their healthcare providers. According to several patients, it is important to ensure that they always have the possibility of in-person meeting with their clinician. As a patient pointed out, technology could never understand their subjective experience with the disease better than the clinician. For this and another patient, listening and empathy are sometimes more important in a care pathway than medication and technology. They mentioned that the therapeutic relationship goes beyond the simple dimension of the disease.

According to a patient, some patients registered with the City hospital can have up to 5 technology applications, sometimes non-interoperable. Some of these technologies do not operate on older Apple- or Android-supported smartphones, making it hard for several patients to use them unless they upgrade their hardware. Technologies may also require access to patient-generated data at home. Patients, clinicians, and managers stressed that patients may not have the technology and equipment and/or a good internet connection, but also the social and cultural capital (e.g., literacy, family network) to fully benefit from the potential of these technologies. They recognise that these technologies could lead to additional costs and expenses for these patients. Even when they have the technology, they may need technical support at any time of the day (24/7) as the disease “has no working days”, as a patient notes. This support is not automatically provided by the organisation and not all patients have a family/friend network that can be mobilised when needed. Paradoxically, technology could exacerbate the disease burden for these patients.

Several respondents reported that the adoption and use of certain AI technologies typically requires a reorganisation, or even a redesign, of clinical practices, of the organisation of services, and of the modes of governance and control within the City hospital. According to clinicians and managers, these changes could be associated with a feeling of loss of professional autonomy, identities, values, and skills. In the words of a manager, AI technologies could cause an erosion of information asymmetry (in favour of the organisation and the MSSS) and challenge clinicians’ autonomy of practice. The erosion and reduction of the scope of expertise due to the replacement of part of the clinical activity by AI was also pointed. However, several respondents relativised these fears, stressing that it is rather the clinicians trained in AI (e.g., clinician-informatician, clinician-data scientist) who will replace the others. This new expertise will have to be institutionalised and valued. This could imply a revision of the boundaries of professional jurisdictions (e.g., reserved acts) and of certain negotiated orders and privileges, and therefore of powers (e.g., nurse vs. general practitioner; general practitioner vs. specialist physician). Managers and technology providers pointed out that a technology that provides real added value for patients will never be integrated into practice if clinicians perceive it as a threat to them.

It was reported that the effort to integrate AI within the City hospital is occurring in a context where clinicians are under great pressure with high workloads. Some emphasised that they have no time to waste on these technologies, particularly those imposed on them by senior management and/or industry. They also expressed a feeling of innovation fatigue. Managers and clinicians acknowledge that this lack of time, but also of engagement, has a negative impact on the success of technology training and promotion initiatives within the organisation, and therefore its subsequent adoption and use. In addition, clinicians involved in technology integration efforts are mainly volunteers (e.g., champions, super-users). As the contribution to innovation is not considered a clinical activity, it is not remunerated nor recognised in their performance indicators. According to several clinicians and managers, this point is a significant barrier to clinicians’ engagement, especially to embrace the necessary changes and adaptations, and to construct meaning and develop new identities with regards to AI.

There is agreement that the need for continuous monitoring and follow-up of some AI technologies in everyday clinical practice made the role of IT teams more critical to clinical practice. According to a manager, this is a major change as clinical and IT teams have historically evolved in silos. In this regard, it is difficult to align cultures and languages within the City hospital in the midst of developing AI technologies and services. For some clinicians, the increasing adoption of AI in their practice may make them dependent on IT teams (potentially conflicting with their autonomy of practice). To address this issue, an interviewee emphasised the importance of the presence of translators or boundary spanners with a hybrid clinical-IT profile to bridge and build a healthy collaborative space between clinical and IT teams. These translators could also act as a bridge between clinical teams and technology providers. The same respondent reported that such a role is already played by members of the City hospital’s Innovation Pole team and several clinicians.

Several managers and clinicians, acknowledge that the blind confidence and lack of critical distance could affect the use of certain AI technologies in clinical decision-making. In this regard, they see the problem of transparency and explainability of AI decisions (black box). According to an interviewee, the problems of data quality and bias are serious enough to be doubly vigilant on this point. A technology provider recognised the importance of clinicians being able to understand how the decision is made by the AI (e.g., parameters retained or excluded) and whether such a decision is right or wrong. To do so, clinicians may need technical support from AI experts, which the City hospital does not necessarily have. According to several respondents, it is difficult for public organisations to recruit AI experts, as the latter are more attracted by the private sector where working conditions and remuneration are very advantageous.

Embedding and adaptation over time

The City hospital’s IT systems are theoretically well secured for AI or associated technologies needed for its functioning. Indeed, any new technology for clinical-administrative use should meet strict criteria for safety and effectiveness. They should be licensed and/or authorised by the IT department or regulatory agencies. However, several managers and clinicians recognise that, once implemented, numerous technologies are not necessarily monitored and controlled over time. The result is a complex, fragmented, and non-interoperable technology environment that is difficult to manage and update, but also vulnerable to cyber-attacks. Some AI technologies are likely to dysfunction and/or operate and evolve awkwardly in such an environment, which could pose patient safety issues.

According to industry, clinicians, and managers, the lifecycle of AI technologies (i.e., the period during which they can function adequately without major upgrades and avoid replacement by new and better technologies) is often very short, and potentially only a few months. The City hospital should be able to upgrade its technology systems and equipment continuously. The costs can be significant. In this regard, equipment and devices (e.g., scanner, MRI) required for the functioning of certain AIs may be considered obsolete after only five years of use. The data they generate is no longer usable, which has a direct impact on their clinical reliability (e.g., ability to detect cancer). To remedy this problem, some technology providers offer to lease equipment. According to the latter, City hospital could then benefit from the latest equipment, with embedded AI, with no obligation to purchase. A technology provider explained that such a model involves the organisation to engage in service contracts over varying periods of time with the supplier. Such contracts usually include the implementation, maintenance, and upgrading of the equipment and associated technologies. The same respondent emphasised that this proximity model would also allow for a feedback process, necessary to adapt to the evolving needs and expectations of clinical teams. However, for several managers, this model raises concerns about the risk of locking the City hospital into a dependency relationship with a single supplier. They reported that this “chaining” could, among other things, increase the supplier’s control of the organisation’s data. To illustrate this point, an interviewee indicated that a technology provider has already “forced” the City hospital to pay for access to its own data (hosted/stored on the supplier’s servers). The same person reported that suppliers want to benefit from an annuity/rent, i.e., a continuous flow of money over time.

The wider system

A gap exists between those who call for a pragmatic approach (e.g., test-and-error, sandbox logic) and those who call for the consolidation of the precautionary principle (i.e., decision-makers adopt precautionary measures when scientific evidence about a human health or environmental hazard is uncertain and the stakes high) [ 50 ]. For several suppliers, the precautionary principle is a major obstacle to the integration of these technologies into the healthcare system. They stressed that regulation should be made more flexible, because zero risk does not exist in healthcare. An interviewee pointed out that the autonomous and evolving nature of some AI technologies will inevitably lead to failures and unforeseen incidents. Instead, lessons should be learned from these malfunctions and incidents to improve the technology. The Post-Market Approval/Post Market Surveillance model adopted in the USA was given as an example. This approach is rejected by other several managers and clinicians who consider that the lives and safety of patients cannot be subject to “hazardous test-and-error”.

Respondents are unanimous in stating that the authorisation, contracting, and financing process of AI technologies by the MSSS, which mainly focuses on the initial purchase price (capital equipment, which results in the procurement of technology with a fixed price, often the lowest, of which the organisation becomes the owner), is no longer adapted to the reality of AI technologies (Table  3 ). Firstly, many AIs operate with a “Software as a Service (SaaS)” business model. It is a monthly or yearly subscription for the organisation. According to technology providers, this model is justified by the fact that these technologies require continuous monitoring, control, and maintenance over time. Some respondents also called for the adoption of the “Value-Based Procurement (VBP)” business model. In this case, the suppliers are paid according to the value generated by their technology (e.g., 10% of the savings made over a patient’s entire care and service cycle). As these technologies are not cheap, there is a risk that they could be excluded from current tendering processes. According to several managers, the tender model does not consider the costs required for the implementation and adaptation of the technology to the local context. Examples where additional costs were required at the time of implementation, not initially foreseen, are relatively common. However, interviewees recognise that VBP is still difficult to implement. Because of the evolving nature of certain AIs, their value could change over time. Currently, it is difficult to ensure their continuous evaluation and monitoring due to the fragmentation of services and the lack of an integrated EHR, as well as trained and qualified human resources (e.g., collection, organisation, structuring, visualisation, and analysis of AI technology usage data), among other things.

According to several managers, the difficulty of acquiring certain AI technologies through the tendering process is another reason why the City hospital prioritises partnership contracts (e.g., co-development or serving as a testing ground) over service contracts (e.g., procurement of technology and/or associated services) with suppliers. In the words of a manager, as long as the organisation does not incur expenses (e.g., having the technology at no cost for a given period or forever) from its operating budgets, it does not have to justify its actions to the MSSS. This strategy also allows the City hospital to accelerate the integration of these technologies into its care and service offer by avoiding the complex bureaucratic process of the MSSS. However, some interviewees reported that partnership contracts do not always allow for the sustainable use of the technology beyond the free-of-charge period. In some situations, the organisation would have to incur expenses after this period and sign a service contract. It would then have to go through the tendering process again. If the latter is won by a different supplier, the initial technology should then be withdrawn, which condemns the City hospital to a kind of eternal restart.

Several technology providers argue that the tendering model is a barrier to entry into healthcare for SU/SMEs, although they could offer AI technologies with real added value. Unlike large companies, SU/SMEs do not have sufficient financial and marketing capacity to offer low prices.

Several respondents, both in the City hospital and industry, pointed out that the Act on the protection of personal information is also seen as a major obstacle to AI in the healthcare system. Typically, when a patient is treated in a public healthcare organisation, his/her consent does not include the secondary use of his/her data for research or other purposes. Legally, AI technologies developed or tested with this data cannot be used and/or commercialised, at least theoretically. According to an organisation interviewee, overcoming this barrier would entail considering that once a patient is treated in a public healthcare organisation, he/she automatically consents to the secondary use of his/her data for service improvement and research purposes. Several patients interviewed agree with this approach. However, they insisted that patients should always be able to withdraw their consent if they so want (opt-out).

Also concerning data, several interviewees highlighted the central role and necessity of Cloud services (e.g., data storage, exchange, and management) for optimal and effective use of AI technologies. According to a manager, Cloud services providers are mainly multi/transnational companies. The latter have servers and relay points all over the world, which means that data could travel across national borders. This challenges regulatory sovereignty. The same interviewee reported that Quebec legislation requires that data be hosted on servers located on its territory. However, the City hospital does not always have the levers to verify and ensure that the providers really respect this requirement. Nor does it always have the possibility of knowing whether an incident (e.g., security breach, data leakage) has occurred if the company does not communicate the information to it. In the words of another manager, “[The City hospital] does not always have the capacity to [ensure the security and reliability of the technologies], so it is forced to trust [the suppliers]”. In the same vein, it does not always have the levers and means to ensure that the technology provider has destroyed and/or deleted the dataset when requested to do so. In addition, according to another interviewee, the definition of responsibilities in the event of a patient harm incident is a not fully resolved issue yet. The latter highlighted that compensation could involve large sums of money that neither the supplier nor the City hospital would want to pay. In this regard, by simply being identified as a potential liable party in the event of an incident, the organisation or company could see the amount of its insurance contract increase considerably because of the risks involved.

Many AI technologies used in clinical decision making are considered as “Software as a Medical Device (SaMD)”. There is still no clear framework for their assessment and approval in Quebec and Canada. In addition, professional federations and colleges, and medical insurance bodies have not yet taken clear positions on their use in clinical practice. According to several interviewees, the absence of solid clinical practice guidelines, protocols, remuneration models, and professional responsibility frameworks limits the possibility of clinicians using these technologies. As an illustration, a manager pointed to the complexity of identifying responsibilities in the event of an AI error (e.g., misdiagnosis or mistreatment). Since certain technologies can decide autonomously, part of the responsibility of the clinician is transferred to them. For the same interviewee, numerous questions have yet to be answered: to what extent does the technology replace the clinician (totally or partially) or not? With the “black box” problem, AI does not always allow for tracing and understanding the decision-making process. Even when it is possible, technology providers might refuse to give access to their algorithm for commercial confidentiality and market competitiveness reasons. It is then difficult to know the nature and/or origin of the fault. Moreover, there is also the question of whether AI should imply an obligation of results, instead of the obligation of means to which clinicians are presently committed. According to another manager, technology providers prefer to classify their technologies outside the SaMD category. In this way, the clinician remains solely responsible in the event of harm. Then, the supplier avoids paying damages that may be substantial. Indeed, compared to a clinician’s error, which is usually limited to a single patient, an AI technology’s error could affect many patients. However, providers explained this choice by the fact that technology approval processes, such as SaMD, are time-consuming and very expensive.

Other regulatory constraints are pointed out by several interviewees. AI technologies never arrive ready for clinical use (plug-and-play). There is often adaptation and alignment work to be done. Some changes and/or adaptations are made informally (e.g., bricolage, workarounds) by clinicians. According to a clinician and a manager, these modifications are sometimes crucial in their decisions to use the technology or not. However, from a regulatory perspective, once licensed and authorised, a technology should not generally be modified, at least theoretically. Currently, any changes require the approval of the City hospital’s IT teams or of a governmental regulatory agency. Although justified in terms of financial and safety risks, there is a consensus among interviewees that this process is rigid, time consuming, and inadequate for the reality of AI. In this regard, updates to AI technologies should be quasi-automatic and continuous, in the spirit of how the iPhone works, often without human intervention. In the words of a clinician, any delay or blockage could have a direct impact on the diagnosis or treatment of patients.

According to a manager, aspects related to the organisations’ performance criteria and, therefore of their funding by the government are not yet fully defined for AI. In Quebec, the activity-based funding model is being deployed to complement the dominant historical budget model. This new model generally considers the activity of physicians (e.g., diagnosis, treatment, surgery), paid essentially on a fee-for-service basis, in the calculation of the budget the organisation will receive from the MSSS. The activity of other healthcare professionals, mainly salaried by the organisation (e.g., nurses), is not considered the same way (or only slightly) in these calculations. Numerous AI technologies intended for (or assisting in) diagnosis or treatment could be supervised by healthcare professionals other than physicians. The impact of this development on the funding of healthcare organisations remains unknown. In the same vein, the respondent highlighted the problem of the fragmentation of funding between medical, medico-social, and social services in Quebec. For example, some AI technologies have a clinical added value and are therefore covered by the MSSS. However, the latter does not cover other aspects such as the improvement of the patient’s quality of life (e.g., Quality-adjusted life year -QALY-). As a result, the City hospital could be required to solicit different departments, ministries and/or agencies to capture the different value components of the same AI technology.

According to several interviewees, funding from the federal government would have a direct impact on the integration of AI technologies into the City hospital. They report that federal programmes make it possible to fund expensive infrastructure projects, from several hundred thousand to several million CA$. However, implementation and sustainability are mainly under the responsibility of the Quebec MSSS because health falls under provincial authority in Canada. There is sometimes a gap between federal funding and provincial priorities. According to a manager, the Quebec MSSS does not automatically fund the implementation and sustainability of federally funded technologies. As a result, several technologies could eventually be abandoned. For another interviewee, one of the important limitations is that federal funding is often very targeted and specific to particular technologies and/or clinical areas. It does not provide sufficient flexibility for organisations to use it according to local needs and contingencies.

Lastly, several respondents recognise that inter-organisational collaboration for sharing expertise and experience is essential for AI. However, the fragmentation, lack of communication and coordination across public healthcare organisations make it difficult to establish such a collaborative environment. For example, according to a clinician, to develop AI technologies with real added value, it would be necessary to have access to large amounts of patient data. She explained that the way to do this, while competing with other technologies from other countries, is to pool the databases of different healthcare organisations in Quebec and Canada. Such an inter-organisational network is essential in the evaluation and approval process of AI technologies, as they are to be tested on data from different healthcare organisations (e.g., urban and rural hospitals, primary care clinics). For the same respondent, such multicentre testing would ensure reliability and effectiveness in different clinical and technological settings across the country.

Summary of key lessons

Our study aimed to generate a better understanding of the conditions that facilitate or constrain the integration of AI technologies in a large healthcare organisation in Canada. By analysing a rich corpus of data using the NASSS framework, the study highlights seven lessons:

Firstly, an organisational culture and leadership that creates favourable conditions for AI is essential as well as the presence of clinical champions who act as ambassadors for AI. This is a lever to attract clinical and/or technical talent and expertise, but also companies in the field. The strategic alignment of the organisation’s clinical-administrative processes and infrastructures with AI technologies remains a major challenge. A lack of alignment could lead to partial integration of technologies or their abandonment, resulting in innovation fatigue among clinical and administrative teams. In a context where clinicians are over-solicited, they should be given the time needed to integrate the change, but also develop the professional expertise and identities that AI could require. It is also important that the technologies proposed to them are supported by evidence of improvements in patient care and services as well as in their work conditions and quality. The integration of AI within a hospital also involves a multitude of stakeholders whose activities and actions should be coherent and synergistic. Communication is fundamental to clarify roles, responsibilities, and mandates and requires a horizontal structure capable of coordinating actions and shaping a consistent organisational story about AI. The technologies proposed by the industry should be filtered so that those that really meet the needs on the ground are prioritised.

Secondly, financial and other incentives are needed to encourage clinicians to experiment and adapt these technologies to their practices. Investments in the development of AI technologies have so far focused on specific complex pathologies that present a great burden to patients and their families as well as to the healthcare system. To address these pathologies, AI mainly exploits image analysis and/or signal quantification, which makes it easier for suppliers to develop technologies and introduce them more quickly to the market. Yet, the sensitivity of safety and data protection issues implies that the hospital hires a lawyer specialising in digital technologies (to ensure that contracts are properly made) and a Chief data officer (for adequate and consistent data governance). Upgrading IT systems and infrastructure and recruiting new expertise hence require planning for both initial and recurring investments and expenditures.

Thirdly, the interoperability of AI technologies and the organisation’s systems and infrastructure are major obstacles to their routine use. Some technologies need quasi-real time access to data, which requires an integrated platform to ensure optimal data circulation between different IT systems and departments of the organisation, or even other organisations involved in the patient’s treatment. The qualification of some advanced software as AI could have financial and legal implications for the organisation. In addition to traditional clinical safety issues, the AI algorithm itself could be hacked and modified, resulting in harm to patients. By recombining data from various sources, individuals could be easily re-identified. These technologies could also require high-tech equipment with very short lifecycles, which the organisation may not have. Furthermore, many AI technologies are driven by SU/SMEs that could disappear from the market at any time. Hence, organisations should have the capacity to maintain the technology on an ad hoc basis or find an alternative and be able to recover and/or ensure the deletion of data by the initial supplier.

Fourth, the definition of the value of AI technologies is far from consensual as well as the expectations regarding what they can or should do. The ability to measure this value is of considerable complexity given the great contrast between the value proposition stated by suppliers, and sometimes by managers, and the actual value to clinicians and patients. The value of AI is not self-evident. Indeed, even if it has shown great performance in a laboratory context, this may not materialise in the real-world context of care and services. The value of some AI technologies also contrasts with the risks they raise given their evolutionary and autonomous nature. There are trade-offs between the precautionary principle, the need for some risk tolerance, and its clinical potential. Moreover, clinical practice may require very specific AI technologies, whereas suppliers tend to prioritise plug-and-play technologies with a potential for widespread commercialisation. The global value of AI could vary widely depending on the balance of the changes and transformations it requires and what it actually provides. This value may also change over time. Evaluating and monitoring AI’s value on an ongoing basis requires resources and expertise the organisation may lack, especially in view of the (re)production of bias across sub-groups of the population.

Fifth, contrary to the rhetoric about their potential to humanise care, some AI technologies raise concerns about the patient-clinician relationship and, therefore, about quality of care. The risk of mechanisation of care and the difficulty of physically accessing healthcare providers is palpable. Digital literacy, technical support, and change management for clinicians and patients using these technologies are essential. For clinicians, AI technologies may imply redesigning clinical practice and service organisation, but also new governance and control strategies within the organisation. Although improbable, there is a real concern that AI could partially or totally replace the activity of clinicians. Hyper-dependence on technology raises concerns about the erosion of clinicians’ expertise and the risk of blind trust in the decisions made by AI. As a result, clinicians may worry about being subordinated to the IT teams that would play a central role in the production of care. This new reality highlights the central role of translators or boundary spanners in building bridges and trust between clinical and IT teams, but also with industry. On a larger scale, the technology-driven approach to AI could cause a deterioration in clinicians’ work conditions and quality.

Sixth, the evolving and self-learning nature of some AI technologies makes time critical, distinguishing them from previous licensed technologies that do not generally require a new approval review. IT teams should approve and validate any changes or adaptations, and this becomes difficult with some AI technologies that evolve autonomously and update themselves. Any delay or blockage could threaten the diagnostic or treatment quality of patients. Continuous monitoring and control over time is required to avoid malfunctions and incidents, but also to make the necessary improvements. In this regard, the increasingly short lifecycle of software and hardware challenges the technical and financial capacity of the organisation to adapt and evolve its systems, equipment, and infrastructure at the right pace. Evolutionary AI technologies create the need for close and sustainable relationships between the organisation and the technology providers, a new relationship that: 1) requires solid frameworks to identify and resolve conflicts of interest as they arise over time; and 2) must avoid lock-in and dependence upon a single provider.

Seventh, many socio-political, economic, and regulatory factors are decisive in the integration of AI technologies, which are mainly offered under SaaS and/or VBP business models. These models are in opposition to the current tender model in Quebec that emphasises the cheapest technology (capital equipment). The legal framework of the current model constitutes a barrier to entry for SU/SMEs, some with high value-added technologies. Established bureaucratic acquisition processes are inadequate for the very short lifecycle of AI technologies. Consent requirements for the use of patient data are misaligned with this new reality and are prompting consideration of an opt-out consent model. AI technologies increasing rely on Cloud services mainly offered by multinational companies with servers and relay points all over the world. Data governance is even more important as healthcare organisations and systems have limited resources and tools to ensure that data management and storage comply with applicable laws. Identifying liability in the event of harm could therefore be very complex. AI technologies classified as SaMD, on the other hand, have specific requirements for quality, efficiency, and clinical reliability. To date, the lack of reference technologies makes it difficult for regulatory agencies to assess and approve them. Established mechanisms and processes are not adapted to the complexity and very short lifecycle of AI. Ongoing evaluation and monitoring mechanisms in the real-world context seem necessary, but the high degree of uncertainty associated with them requires a balance between the precautionary principle and a laissez-faire integration in clinical routine. Beyond the lack of clear frameworks and directives from the MSSS and other regulatory bodies regarding the use of these technologies by clinicians, inter-organisational networks facilitating the sharing of expertise and experience are essential. The current context is characterised by fragmentation, and poor communication and coordination between organisations and government agencies, which hinders an integrated and coherent vision of AI at the healthcare system: provincial- and federal-level of governance.

Contribution to the existing literature

The results of this study contribute to knowledge in several ways. They shed a new and different light on the trend of recent years where the literature has mainly focused on the technical and promissory dimensions of AI. Our findings are consistent with those of Pumplun et al. (2021) and Petersson et al. (2022) who analysed implementation issues raised by AI technologies in healthcare in Germany and Sweden, respectively [ 3 , 51 ]. Studies on telehealth and EHR also reported results that corroborate ours on AI [ 26 , 31 , 32 , 34 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ]. In this regard, several authors pointed out the major contrast between the techno-optimistic discourse on the performance and efficiency of technology and the reality of services that are difficult to transform [ 56 , 57 , 58 ]. These experiences have shown that the difficulties encountered in the deployment of digital technologies are mainly due to the historical lack of attention paid to the sociotechnical factors and conditions necessary for their integration into healthcare organisations and systems. Hence, our study adds to the growing literature that considers technology in a complex sociotechnical transformation perspective that requires not only technological but also human, clinical, professional, organisational, socio-political, economic, regulatory, legal, and cultural changes [ 27 , 40 , 41 , 56 , 59 , 60 , 61 ]. Very limited attention has been paid to this perspective in examining AI to date, whereas our study clarifies its contribution and indicates some avenues for future research (Table  4 ) [ 3 , 18 , 26 , 51 ].

From a theoretical standpoint, our study provides an original contribution to the literature on health innovations. It is one of the first to demonstrate that the NASSS framework is relevant for the analysis of the integration of AI technologies in healthcare organisations and systems [ 51 ]. The study contributes to the knowledge on the importance of a sociotechnical perspective to understand the complexity and unpredictability of transformations related to disruptive innovations such as AI [ 27 , 51 , 62 ].

Implication for practice and policy

Our study provides new insights for decision-making and practice on the conditions required but also on the pitfalls to be avoided to ensure successful integration of AI technologies into healthcare organisations and systems. It shows that the pitfalls of the technocentric vision of digital health of the last thirty years in Quebec (and elsewhere too) could easily be repeated with AI technologies, but this time with more profound repercussions [ 31 , 32 , 33 , 35 , 36 , 63 ]. As Matheny et al. (2020) highlighted: “Disconnects between reality and expectations have led to prior precipitous declines in use of the technology, termed AI winters, and another such event is possible, especially in health care” [ 64 ]. In this regard, the various stakeholders must be aware that AI is more an object of transformation at all levels of healthcare system governance, than a simple “intrinsically good/bad” tool. Its successful integration depends on several structural conditions, namely, appropriate: regulatory and governance frameworks; funding, business, and remuneration models; definition of the value proposition; management of conflicts of interest; governance of data; cybersecurity strategies; training and expertise, models of care and service delivery; inter-professional collaboration; and up-to-date infrastructure and equipment.

Specifically, AI highlights the importance of rethinking the collaboration between healthcare organisations and systems, on the one hand, and technology providers, on the other hand. Indeed, their interests sometimes represent competing financial and political objectives between which a difficult balance must be established [ 65 ]. Given their disruptive nature at all levels of the healthcare system, IA technologies could generate tensions and require trade-offs between perceptions, expectations, interests, and agendas that may be divergent or even antagonistic (ex. industry and venture capital, decision-makers, managers, clinicians, patients). These dynamics and power relations influence the trajectory of AI technologies in healthcare, either positively or negatively [ 59 , 66 ]. Thus, if healthcare organisations and systems are not sufficiently equipped and prepared, “the AI landscape risks being shaped by early established companies and decisions made with insufficient evaluations in place due to pressures to embrace technology” [ 67 ].

In addition, one of the fundamental issues remains the lack of digital literacy and culture, and AI technology skills among healthcare professionals [ 68 ]. Currently, initial and continuing training programmes do not sufficiently integrate these technologies into the expertise that trainees (e.g., physicians, nurses) need to achieve to be authorised to practice. As reported in our study, without appropriate training, clinicians are unlikely to adopt in an appropriate way these technologies. Indeed, training is required to adapt provider protocols, administrative workflows, pathways, and business processes [ 67 ]. According to Mistry (2019), for such change to take place, healthcare professionals will need:1) to have access to education content enabling them to learn new skills as AI users and work differently; 2) to be able to train AI systems themselves for setting them up to perform specified tasks, which implies knowing what data to select and its quality; 3) to develop abilities to interpret AI outputs, including a solid understanding of its limitations and bounds of function; and 4) to know “how the system learns and what constitutes appropriate use, so that ethical norms are upheld and any introduction of biases is avoided” [ 67 ].

Strengths and limitations

This study offers one of the first holistic and multilevel analyses of the complexity of the changes and transformations associated with the integration of AI technologies into clinical routine, beyond technical issues. It is also part of the few studies that go beyond looking at one single AI technology and delves into the organisational and systemic complexity of integrating multiple AI technologies concurrently.

However, the study has limitations. By its qualitative nature, it has a high level of internal validity, but the transferability (or generalisability) of its findings is limited to similar healthcare organisations and systems. In other contexts, it can increase the awareness of different stakeholders regarding the importance of taking better account of the sociotechnical dimension of AI. Healthcare organisations and systems can vary considerably, hence the importance of contextualising the results.

The number of interviewees ( n  = 29) is relatively low in view of the large number of AI technologies covered in this study. Although we made great efforts to include a wide range of stakeholders, several people were unable to participate due to the COVID-19 context. This is the case for women heading technology companies, whereas decision-makers, managers, and clinicians were unable to participate because of their direct involvement in the management of the pandemic. However, the people who participated, through their expertise and experience, provided us with rich data, necessary for a detailed understanding of the challenges of integrating AI in healthcare organisations and systems. The application of a rigorous research approach, guided by best methodological practices and an exhaustive theoretical framework, has reinforced the reliability of our results.

Conclusions

AI in healthcare is still in its infancy. There are huge expectations that it will provide answers to major contemporary challenges in healthcare organisations and systems. This is reflected in the funding it receives from governments, but also in the interest of the financial and venture capital sector. The COVID-19 pandemic was a test case for AI, and it did not fully deliver. However, the pandemic has served as an accelerator for its experimentation, for example, through the relaxation of regulatory requirements and less resistance from some stakeholders. AI represents as much a logistical, psychological, cultural, and philosophical change, particularly in terms of what it could and should do in healthcare organisations and systems. It is a “new era” that requires a real critical examination to learn from the many past experiences with the digitalisation of healthcare organisations and systems. With AI, the nature, scale and complexity of the changes and transformations are at such a level and intensity that the implications could be profound for society. At present, little is known about how such an announced revolution may take shape and under what conditions. This study provides a unique learning base for analysing AI technologies in healthcare organisations and systems from a sociotechnical perspective using the NASSS framework. It adds to the existing literature and can better inform decision-making towards the judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.

Availability of data and materials

The data that support the findings of this study are available from the corresponding author (HA) upon reasonable request. The data are not publicly available due to information that could compromise the privacy of the research participants.

Abbreviations

• Artificial intelligence

Canadian Dollar

Coronavirus Disease 2019

Quebec’s Ministry of Health and Social Services Information Technology Division

Electronic Health Record

International Organization for Standardization

Information Technology

Act on Contracting by Public Bodies

Magnetic Resonance Imaging

Quebec’s Ministry of Health and Social Services

Non-Adoption, Abandonment, Scale-up, Spread, Sustainability

Picture Archiving and Communication System

Portable Document Format

Quality-Adjusted Life Year

Software as a Service

Software as a Medical Device

Start-ups and Small- and Medium-sized Enterprises

United States Dollar

United States of America

Value-Based Procurement

Organisation for Economic Co-operation and Development (OECD). Recommendation of the Council on Artificial Intelligence. OECD; 2019. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449#mainText .

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Acknowledgements

We thank the interviewees and the City hospital personnel for their availability throughout the study, even in the midst of the COVID-19 pandemic. The findings and conclusions presented in the text are those of the authors. They do not necessarily reflect the position of their organisations.

HA was supported by the In Fieri research programme (led by P), the International Observatory on the Societal Impacts of Artificial Intelligence and Digital Technologies, and the Institute for Data Valorization (IVADO), (Canada).

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Hassane Alami, Chrysanthi Papoutsi & Sara E. Shaw

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HA and PL conceived and designed the study plan. HA and PL were responsible for data collection, analysis, and interpretation of results. HA, PL, CP, SES, RF and JPF were engaged in the drafting of the manuscript, and they all read and approved the final manuscript.

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The study was approved by the City hospital Research Ethics Committee (Number: Comité d’éthique de la recherche- City hospital: 20.399). (Address is anonymised for confidentiality reasons). All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all subjects and/or their legal guardian(s).

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Alami, H., Lehoux, P., Papoutsi, C. et al. Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre. BMC Health Serv Res 24 , 701 (2024). https://doi.org/10.1186/s12913-024-11112-x

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Received : 03 February 2023

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Published : 03 June 2024

DOI : https://doi.org/10.1186/s12913-024-11112-x

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