A Novel Approach for Mixed-Methods Research Using Large Language Models : A Report Using Patients' Perspectives on Barriers to Arthroplasty
© 2024 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology..
OBJECTIVE: Mixed-methods research is valuable in health care to gain insights into patient perceptions. However, analyzing textual data from interviews can be time-consuming and require multiple analysts for investigator triangulation. This study aims to explore a novel approach to investigator triangulation in mixed-methods research by employing a large language model (LLM) for analyzing data from patient interviews.
METHODS: This study compared the thematic analysis and survey generation performed by human investigators and ChatGPT-4, which uses GPT-4 as its backbone model, using data from an existing study that explored patient perceptions of barriers to arthroplasty. The human- and ChatGPT-4-generated themes and surveys were compared and evaluated based on their representation of salient themes from a predetermined topic guide.
RESULTS: ChatGPT-4 generated analogous dominant themes and a comprehensive corresponding survey as the human investigators but in significantly less time. The survey questions generated by ChatGPT-4 were less precise than those developed by human investigators. The mixed-methods flowchart proposes integrating LLMs and human investigators as a supplementary tool for the preliminary thematic analysis of qualitative data and survey generation.
CONCLUSION: By utilizing a combination of LLMs and human investigators through investigator triangulation, researchers may be able to conduct more efficient mixed-methods research to better understand patient perspectives. Ethical and qualitative implications of using LLMs should be considered.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
ACR open rheumatology - (2024) vom: 07. März |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Mannstadt, Insa [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Revised 07.03.2024 published: Print-Electronic Citation Status Publisher |
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| doi: |
10.1002/acr2.11662 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM369439163 |
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| 520 | |a © 2024 The Authors. ACR Open Rheumatology published by Wiley Periodicals LLC on behalf of American College of Rheumatology. | ||
| 520 | |a OBJECTIVE: Mixed-methods research is valuable in health care to gain insights into patient perceptions. However, analyzing textual data from interviews can be time-consuming and require multiple analysts for investigator triangulation. This study aims to explore a novel approach to investigator triangulation in mixed-methods research by employing a large language model (LLM) for analyzing data from patient interviews | ||
| 520 | |a METHODS: This study compared the thematic analysis and survey generation performed by human investigators and ChatGPT-4, which uses GPT-4 as its backbone model, using data from an existing study that explored patient perceptions of barriers to arthroplasty. The human- and ChatGPT-4-generated themes and surveys were compared and evaluated based on their representation of salient themes from a predetermined topic guide | ||
| 520 | |a RESULTS: ChatGPT-4 generated analogous dominant themes and a comprehensive corresponding survey as the human investigators but in significantly less time. The survey questions generated by ChatGPT-4 were less precise than those developed by human investigators. The mixed-methods flowchart proposes integrating LLMs and human investigators as a supplementary tool for the preliminary thematic analysis of qualitative data and survey generation | ||
| 520 | |a CONCLUSION: By utilizing a combination of LLMs and human investigators through investigator triangulation, researchers may be able to conduct more efficient mixed-methods research to better understand patient perspectives. Ethical and qualitative implications of using LLMs should be considered | ||
| 650 | 4 | |a Journal Article | |
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| 700 | 1 | |a Young, Sarah R |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Fei |e verfasserin |4 aut | |
| 700 | 1 | |a Navarro-Millán, Iris |e verfasserin |4 aut | |
| 700 | 1 | |a Mehta, Bella |e verfasserin |4 aut | |
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