Zikang Chen , Qinchuan Wang , Yaoqian Sun , Hailing Cai , Xudong Lu
{"title":"Chat-ePRO：基于 ChatGPT 的电子患者报告结果系统的开发和试点研究。","authors":"Zikang Chen , Qinchuan Wang , Yaoqian Sun , Hailing Cai , Xudong Lu","doi":"10.1016/j.jbi.2024.104651","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Chatbots have the potential to improve user compliance in electronic Patient-Reported Outcome (ePRO) system. Compared to rule-based chatbots, Large Language Model (LLM) offers advantages such as simplifying the development process and increasing conversational flexibility. However, there is currently a lack of practical applications of LLMs in ePRO systems. Therefore, this study utilized ChatGPT to develop the Chat-ePRO system and designed a pilot study to explore the feasibility of building an ePRO system based on LLM.</p></div><div><h3>Materials and Methods</h3><p>This study employed prompt engineering and offline knowledge distillation to design a dialogue algorithm and built the Chat-ePRO system on the WeChat Mini Program platform. In order to compare Chat-ePRO with the form-based ePRO and rule-based chatbot ePRO used in previous studies, we conducted a pilot study applying the three ePRO systems sequentially at the Sir Run Run Shaw Hospital to collect patients’ PRO data.</p></div><div><h3>Result</h3><p>Chat-ePRO is capable of correctly generating conversation based on PRO forms (success rate: 95.7 %) and accurately extracting the PRO data instantaneously from conversation (Macro-F1: 0.95). The majority of subjective evaluations from doctors (>70 %) suggest that Chat-ePRO is able to comprehend questions and consistently generate responses. Pilot study shows that Chat-ePRO demonstrates higher response rate (9/10, 90 %) and longer interaction time (10.86 s/turn) compared to the other two methods.</p></div><div><h3>Conclusion</h3><p>Our study demonstrated the feasibility of utilizing algorithms such as prompt engineering to drive LLM in completing ePRO data collection tasks, and validated that the Chat-ePRO system can effectively enhance patient compliance.</p></div>","PeriodicalId":15263,"journal":{"name":"Journal of Biomedical Informatics","volume":"154 ","pages":"Article 104651"},"PeriodicalIF":4.0000,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Chat-ePRO: Development and pilot study of an electronic patient-reported outcomes system based on ChatGPT\",\"authors\":\"Zikang Chen , Qinchuan Wang , Yaoqian Sun , Hailing Cai , Xudong Lu\",\"doi\":\"10.1016/j.jbi.2024.104651\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>Chatbots have the potential to improve user compliance in electronic Patient-Reported Outcome (ePRO) system. Compared to rule-based chatbots, Large Language Model (LLM) offers advantages such as simplifying the development process and increasing conversational flexibility. However, there is currently a lack of practical applications of LLMs in ePRO systems. Therefore, this study utilized ChatGPT to develop the Chat-ePRO system and designed a pilot study to explore the feasibility of building an ePRO system based on LLM.</p></div><div><h3>Materials and Methods</h3><p>This study employed prompt engineering and offline knowledge distillation to design a dialogue algorithm and built the Chat-ePRO system on the WeChat Mini Program platform. In order to compare Chat-ePRO with the form-based ePRO and rule-based chatbot ePRO used in previous studies, we conducted a pilot study applying the three ePRO systems sequentially at the Sir Run Run Shaw Hospital to collect patients’ PRO data.</p></div><div><h3>Result</h3><p>Chat-ePRO is capable of correctly generating conversation based on PRO forms (success rate: 95.7 %) and accurately extracting the PRO data instantaneously from conversation (Macro-F1: 0.95). The majority of subjective evaluations from doctors (>70 %) suggest that Chat-ePRO is able to comprehend questions and consistently generate responses. Pilot study shows that Chat-ePRO demonstrates higher response rate (9/10, 90 %) and longer interaction time (10.86 s/turn) compared to the other two methods.</p></div><div><h3>Conclusion</h3><p>Our study demonstrated the feasibility of utilizing algorithms such as prompt engineering to drive LLM in completing ePRO data collection tasks, and validated that the Chat-ePRO system can effectively enhance patient compliance.</p></div>\",\"PeriodicalId\":15263,\"journal\":{\"name\":\"Journal of Biomedical Informatics\",\"volume\":\"154 \",\"pages\":\"Article 104651\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Biomedical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1532046424000698\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Biomedical Informatics","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1532046424000698","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Chat-ePRO: Development and pilot study of an electronic patient-reported outcomes system based on ChatGPT
Objective
Chatbots have the potential to improve user compliance in electronic Patient-Reported Outcome (ePRO) system. Compared to rule-based chatbots, Large Language Model (LLM) offers advantages such as simplifying the development process and increasing conversational flexibility. However, there is currently a lack of practical applications of LLMs in ePRO systems. Therefore, this study utilized ChatGPT to develop the Chat-ePRO system and designed a pilot study to explore the feasibility of building an ePRO system based on LLM.
Materials and Methods
This study employed prompt engineering and offline knowledge distillation to design a dialogue algorithm and built the Chat-ePRO system on the WeChat Mini Program platform. In order to compare Chat-ePRO with the form-based ePRO and rule-based chatbot ePRO used in previous studies, we conducted a pilot study applying the three ePRO systems sequentially at the Sir Run Run Shaw Hospital to collect patients’ PRO data.
Result
Chat-ePRO is capable of correctly generating conversation based on PRO forms (success rate: 95.7 %) and accurately extracting the PRO data instantaneously from conversation (Macro-F1: 0.95). The majority of subjective evaluations from doctors (>70 %) suggest that Chat-ePRO is able to comprehend questions and consistently generate responses. Pilot study shows that Chat-ePRO demonstrates higher response rate (9/10, 90 %) and longer interaction time (10.86 s/turn) compared to the other two methods.
Conclusion
Our study demonstrated the feasibility of utilizing algorithms such as prompt engineering to drive LLM in completing ePRO data collection tasks, and validated that the Chat-ePRO system can effectively enhance patient compliance.
期刊介绍：
The Journal of Biomedical Informatics reflects a commitment to high-quality original research papers, reviews, and commentaries in the area of biomedical informatics methodology. Although we publish articles motivated by applications in the biomedical sciences (for example, clinical medicine, health care, population health, and translational bioinformatics), the journal emphasizes reports of new methodologies and techniques that have general applicability and that form the basis for the evolving science of biomedical informatics. Articles on medical devices; evaluations of implemented systems (including clinical trials of information technologies); or papers that provide insight into a biological process, a specific disease, or treatment options would generally be more suitable for publication in other venues. Papers on applications of signal processing and image analysis are often more suitable for biomedical engineering journals or other informatics journals, although we do publish papers that emphasize the information management and knowledge representation/modeling issues that arise in the storage and use of biological signals and images. System descriptions are welcome if they illustrate and substantiate the underlying methodology that is the principal focus of the report and an effort is made to address the generalizability and/or range of application of that methodology. Note also that, given the international nature of JBI, papers that deal with specific languages other than English, or with country-specific health systems or approaches, are acceptable for JBI only if they offer generalizable lessons that are relevant to the broad JBI readership, regardless of their country, language, culture, or health system.
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