Natural Language Processing Enhanced Qualitative Methods: An Opportunity to Improve Health Outcomes
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Background Electronic health systems contain large amounts of unstructured data (UD) which are often unanalyzed due to the time and costs involved. Unanalyzed data creates missed opportunities to improve health outcomes. Natural language processing (NLP) is the foundation of generative artificial intelligence (GAI), which is the basis for large language models, such as ChatGPT. NLP and GAI are machine learning methods that analyze large amounts of data in a short time at minimal cost. The ability of NLP to conduct qualitative analyses is increasing, yet the results can lack context and nuance in their findings, requiring human intervention. Methods Our study compared outcomes, time, and costs of a previously published qualitative study. Our approach partnered an NLP model and a qualitative researcher (NLP+). UD from behavioral health patients were analyzed using NLP and a Latent Dirichlet allocation to identify the topics using probability of word coherence scores. The topics were then analyzed by a qualitative researcher, translated into themes, and compared with the original findings. Results The NLP + method results aligned with the original, qualitative derived themes. Our model also identified two additional themes which were not originally detected. The NLP + method required 6 hours of labor, 3 minutes for transcription, and a transcription cost of $1.17. The original, qualitative researcher only method required more than 36 hours ($2,250) of time and $1,100 for transcription. Conclusions While natural language processing analyzes voluminous amounts of data in seconds, context and nuance in human language are regularly missed. Combining a qualitative researcher with NLP + could be deployed in many settings, reducing time and costs, and improving context. Until large language models are more prevalent, a human interaction can help translate the patient experience by contextualizing data rich in social determinant indicators which may otherwise go unanalyzed.Keywords:
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Abstract Recently, there have been a variety of arguments voiced to encourage that more attention be given to the role qualitative methods can play in mixed methods research in public policy and public administration. This article discusses these claims and describes the benefits of qualitative approaches, and how qualitative research methods can be leveraged to strengthen mixed methods research in public administration. We also provide a guide for improving the credibility of mixed methods research through increasing transparency and discussions of all methodological decisions. This study is based on a systematic content analysis of 186 mixed methods studies published in public policy and public administration journals between 2010 and 2018. We found that findings from the quantitative methods dominated the mixed methods studies, little diversity in data collection and analysis methods, and frequent failure to integrate insights from both methods. We also analyzed the 36 qualitative‐dominant studies in the sample, and illuminated seven different ways that authors of qualitative‐dominant studies leveraged the qualitative strand to strengthen mixed methods research. We developed lessons from our analysis of the qualitative‐dominant articles on how to incorporate qualitative methods in a thoughtful manner, articulate a role for each strand, and effectively support findings with one or more strands.
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The use of qualitative methods is growing in social policy research, and the contributions of qualitative research for policy studies have increasingly been recognised. Despite these trends, quantitative studies continue to account for a higher proportion of policy research. Although the existing literature is concerned primarily with how to conduct qualitative research, some scholars have examined pedagogical approaches to teaching qualitative research. Literature on teaching qualitative research in comparative social policy is uncommon, however. Likewise, whilst international organisations have been actively producing and publishing data on welfare institutions in various countries, that data has largely been generated through quantitative methods, providing statistical evidence. International comparative data based on qualitative research is harder to find, although some qualitative data are available from the UK Data Services. Against this backdrop, this chapter highlights the main contributions of qualitative methods in policy research, and key issues and challenges relating to teaching qualitative research in comparative social policy.
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This paper aims at exploring the definition, characteristics and stages of qualitative data analysis in tourism research. The paper is based on the writer’s experience in implementing qualitative data analysis as well as literature review on qualitative research methodology. Qualitative researchers may deal with different and rich data from qualitative methods. However, many qualitative researchers do not understand how to produce knowledge from qualitative data. For this reason, the result of a qualitative research tends to be descriptive without contributing to the advancement of knowledge. Thematic analysis is considered as a dominant form of analysis in qualitative research. Its stages consist of data reduction, organization and interpretation. Coding is the key for succeeding the qualitative data analysis. Hence, examples of qualitative data analysis in tourism research are provided to give practical stages of how to analyze qualitative data.
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Abstract Qualitative research focuses on in-depth data to understand people, processes, and phenomena. It often involves smaller datasets that do not intend to produce generalizable results but instead illuminate nuances, counterexamples, or underrepresented voices. Qualitative data can produce theory and allow for in-depth analyses. Recent methodological developments have created opportunities for large mixed methods projects to produce voluminous amounts of qualitative data that can and sometimes are shared as secondary datasets. The growing use of big data has also produced new possibilities for large qualitative datasets. Additionally, longitudinal and comparative qualitative research can produce significant data. This symposium presents four studies that examine opportunities and challenges in large qualitative datasets in gerontology. The first article uses multiple qualitative datasets to examine how long-term care staff balance resident rights of safety and autonomy. The second article uses multiple qualitative methods in a larger mixed methods Friendship Study to examine meaningful connections among people living with dementia in long-term care. Through a multi-site community university partnership, the third study extracts qualitative data from over 500 patient records to examine experiences of terminally ill hospice patients from residential homes for the dying. The fourth study examines nearly ten years of qualitative data from over 20 communities to examine successful aging from an Alaska Native perspective. Altogether, these four studies illuminate diverse strategies for identifying qualitative data, using data software, managing data, addressing ethical dilemmas, and employing research teams for coding and analyzing large qualitative datasets in gerontological research. This is a Qualitative Research Interest Group Sponsored Symposium.
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Scientific research is based upon finding a solution to a particular problem one can identify. There are various methods of formulating a research design for the study. Two broad approaches of data collection and interpretation in research are qualitative and quantitative research. The elementary method of conducting research was quantitative, but recently, qualitative method of research has also gained momentum among researchers.
Qualitative research focuses in understanding a research query as a humanistic or idealistic approach. Though quantitative approach is a more reliable method as it is based upon numeric and methods that can be made objectively and propagated by other researchers. Qualitative method is used to understand people's beliefs, experiences, attitudes, behavior, and interactions. It generates non-numerical data. The integration of qualitative research into intervention studies is a research strategy that is gaining increased attention across disciplines. Although once viewed as philosophically incongruent with experimental research, qualitative research is now recognized for its ability to add a new dimension to interventional studies that cannot be obtained through measurement of variables alone.[1] Qualitative research was initially used in psychological studies when researchers found it tedious to evaluate human behavior in numeric. Since then, qualitative research is used in other research fields as well. In clinical research, qualitative approach can help view the data more extensively. It strengthens clinical trials by enhancing user involvement in it.
In this paper, The nature of qualitative research, Qualitative research designs, Methods of collecting qualitative data, Handling qualitative research data, Analysing qualitative results and Presenting the results of qualitative research are explained in depth.
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Over the last two decades there has been an increase in the use of qualitative research, particularly in the human sciences. Such a move has resulted in an increasing number of researchers across disciplines using various types of qualitative software specially designed for managing text and facilitating analysis of qualitative data. However, we feel that limited information is available on the nature and practical use of these programmes in the Irish context. This has led to various misconceptions regarding the use of such programmes. In addition, international literature has highlighted the importance of making the qualitative research process more transparent in terms of describing in detail the analytical procedures applied in qualitative research. This article seeks to open up the debate surrounding qualitative data analysis and provoke discussion about the use of qualitative software packages in educational research. Based on a mixed-methods educational research project involving a substantive qualitative component, this article explores the joy and despair associated with using Qualitative Solutions Research (QSR) Non-numerical Unstructured Data: Indexing, Searching and Theorizing Version 6 (‘N6 software’), and aims to dispel some of the myths that exist around using such software.
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Abstract Two myths about qualitative research are that real qualitative researchers do not count and cannot count. These antinumber myths have led to the underutilization of numbers in qualitative research and to the simplistic view of qualitative research as non‐ or antinumber. Yet numbers are integral to qualitative research, as meaning depends, in part, on number. As in quantitative research, numbers are used in qualitative research to establish the significance of a research project, to document what is known about a problem, and to describe a sample. But they are also useful for showcasing the labor and complexity of qualitative work and to generate meaning from qualitative data; to document, verify, and test researcher interpretations or conclusions; and to re‐present target events and experiences. Although numbers are important in the treatment of qualitative data, qualitative researchers should avoid the counting pitfalls of verbal counting, overcounting, misleading counting, and acontextual counting. © 2001 John Wiley & Sons, Inc. Res Nurs Health 24: 230–240, 2001
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This chapter deals with qualitative data. While everyone is familiar with the idea of interviewing and observing, actually collecting qualitative data is not as easy as it might first appear to be. In fact, when doing qualitative work, it is easy to become overwhelmed by the amount of information collected. However, with some purposeful planning, piloting, and practice, the student can avoid some of the pitfalls associated with qualitative data collection. Focusing on qualitative interviews and participant observation, the chapter introduces some of the common issues that arise when gathering qualitative data and offers useful advice concerning the planning and practice of collecting data ‘in the field’.
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Scientific research is based upon finding a solution to a particular problem one can identify. There are various methods of formulating a research design for the study. Two broad approaches of data collection and interpretation in research are qualitative and quantitative research. The elementary method of conducting research was quantitative, but recently, qualitative method of research has also gained momentum among researchers. Qualitative research focuses in understanding a research query as a humanistic or idealistic approach. Though quantitative approach is a more reliable method as it is based upon numeric and methods that can be made objectively and propagated by other researchers. Qualitative method is used to understand people's beliefs, experiences, attitudes, behavior, and interactions. It generates non-numerical data. The integration of qualitative research into intervention studies is a research strategy that is gaining increased attention across disciplines. Although once viewed as philosophically incongruent with experimental research, qualitative research is now recognized for its ability to add a new dimension to interventional studies that cannot be obtained through measurement of variables alone.[1] Qualitative research was initially used in psychological studies when researchers found it tedious to evaluate human behavior in numeric. Since then, qualitative research is used in other research fields as well. In clinical research, qualitative approach can help view the data more extensively. It strengthens clinical trials by enhancing user involvement in it. In this paper, The nature of qualitative research, Qualitative research designs, Methods of collecting qualitative data, Handling qualitative research data, Analysing qualitative results and Presenting the results of qualitative research are explained in depth.
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