Design and Development of a Summarization Service Prototype of Personal Health Record Medical Data with Fast Healthcare Interoperability Resources Structure Using Large Language Models
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As the scope of healthcare data expands beyond hospital-generated electronic medical records (EMRs) to include personal health records (PHRs), there is a growing need for automated methods to efficiently summarize large volumes of patient-specific information. In this study, we propose a summarization approach that leverages large language models (LLMs) and standardized data formats to improve accessibility and usability of patient data. Specifically, we developed a prototype system that summarizes patient Bundles formatted in accordance with the Fast Healthcare Interoperability Resources (FHIR) standard. Using the ChatGPT API and document processing techniques, we generated summaries and evaluated their accuracy using a checklist based on clinical criteria. The summarization model achieved an accuracy of 81.6%, suggesting its potential for real-world application. Our findings indicate that healthcare professionals can more quickly and effectively review a patient’s primary conditions using summarized PHR data, particularly as FHIR adoption increases. However, the results also highlight certain limitations, including the generalization of summaries and the absence of domain-specific fine-tuning. These findings underscore the importance of future research involving multidisciplinary clinical evaluations, targeted fine-tuning strategies, and question-driven summarization to enhance accuracy and clinical relevance. Overall, this study demonstrates the feasibility of integrating LLM-based summarization into healthcare workflows, contributing to improved interoperability and decision-making in clinical settings.
Abstract
As the scope of healthcare data expands beyond hospital-generated electronic medical records (EMRs) to include personal health records (PHRs), there is a growing need for automated methods to efficiently summarize large volumes of patient-specific information. In this study, we propose a summarization approach that leverages large language models (LLMs) and standardized data formats to improve accessibility and usability of patient data. Specifically, we developed a prototype system that summarizes patient Bundles formatted in accordance with the Fast Healthcare Interoperability Resources (FHIR) standard. Using the ChatGPT API and document processing techniques, we generated summaries and evaluated their accuracy using a checklist based on clinical criteria. The summarization model achieved an accuracy of 81.6%, suggesting its potential for real-world application. Our findings indicate that healthcare professionals can more quickly and effectively review a patient’s primary conditions using summarized PHR data, particularly as FHIR adoption increases. However, the results also highlight certain limitations, including the generalization of summaries and the absence of domain-specific fine-tuning. These findings underscore the importance of future research involving multidisciplinary clinical evaluations, targeted fine-tuning strategies, and question-driven summarization to enhance accuracy and clinical relevance. Overall, this study demonstrates the feasibility of integrating LLM-based summarization into healthcare workflows, contributing to improved interoperability and decision-making in clinical settings.