Background: The potential for the secondary use of electronic health records (EHRs) is underused due to restrictions in national legislation. For privacy purposes, legislative restrictions limit the availability and content of EHR data provided to secondary users. These limitations do not encourage healthcare organisations to develop procedures to promote the secondary use of EHRs.
Objective: The objective of this study is to identify factors that restrict the secondary use of unstructured EHRs in academic research in Finland and Sweden.
Method: A study was conducted to identify these availability-restricting issues that pertain to the academic secondary use of unstructured EHRs. Using semi-structured interviews, 14 domain experts in science, hospital management and business were interviewed to evaluate the efficiency of procedures and technologies that are implemented in secondary use processes.
Results: The results demonstrate three aspects that restrict the availability of unstructured EHRs for secondary purposes: (i) the management and (ii) privacy preservation of such data as well as (iii) potential secondary users.
Conclusion: Based on these categories, two approaches for the secondary use of unstructured EHRs are identified: the protected processing environment and altered data.
Implications: The protected processing environment ensures patient privacy by providing unstructured EHRs for exclusive user groups that have preferred use intentions. Compared to the use of such processing environments, data alteration enables the secondary use of unstructured EHRs for a larger user group with various use intentions but that yield less valuable content.
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Article Synopsis
- Large language models (LLMs) like ChatGPT struggle with private data interpretation, specifically electronic health records (EHRs), but prompt engineering could improve their accuracy.
- Through systematic testing of prompt techniques on 490 EHR notes, the study found that structured prompts significantly enhanced LLM accuracy from 64.3% to 91.4%, outperforming traditional natural language processing methods.
- The results indicate that LLMs, with proper prompt strategies, can effectively identify clinical insights from EHRs without requiring expert knowledge, suggesting potential applications in other fields for automated data analysis.
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