인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Abstract Background Regular monitoring of healthcare quality and equity is crucial for informing decision-makers and clinicians. This study explores the application of generative AI, more specifically large language models (LLMs), to facilitate standardized monitoring of healthcare quality using the established framework Analysis of Individual Heterogeneity and Discriminatory Accuracy (AIHDA). The study investigates whether a customized GPT can effectively apply the AIHDA-framework to assess healthcare quality in a simulated dataset. Population and methods Using simulated data modelled on real-world healthcare information, we evaluated the quality indicator of potentially inappropriate medication (PIM). A customized GPT built on ChatGPT 4o was prompted via the principle TREF (Task, Requirement, Expectation, Format) to perform the analysis. Results were compared to a traditional analysis performed with Stata to evaluate accuracy and reliability. Results The GPT successfully conducted the AIHDA analysis, producing results equal to those of the Stata analysis. The GPT provides useful visualizations and structured reports as well as interactive dialog with the end-user in real-time. However, occasional variations in the results occurred in some iterations of the analysis, highlighting potential issues with reliability. The analysis requires close supervision, as the GPT presents both errors and correct results with confidence. Conclusions Generative AI and LLMs show promise in supporting standardized monitoring of healthcare quality and equity using the AIHDA-framework. It enables accessible analysis but requires oversight to address limitations such as occasional inaccuracies. Future and more reliable models of LLMs and local deployment on secure servers may further enhance the utility for routine healthcare monitoring.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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