Filling in the pitfalls between EHRs and CDSSs

Improving interoperability of Clinical Decision Support System with Information Extraction and Semantic Search through Generative AI

Abstract

Backgrounds: Clinical Decision Support systems (CDSS) are useful for improved diagnostic quality. However, their operation has issues (pitfalls), such as fragmented workflows and a lack of interoperability. Objectives: This study proposes an improved method to overcome these issues. The proposed methods are 1) Information Extraction using Natural Language Processing, 2) Semantic search for medical coding, and 3) EHR-CDSS real-time interoperability using HL7 FHIR. Method: Information extraction and semantic search use Google's Public Cloud Services. Results and Discussion: The information extraction capability is comparable to experienced clinicians. The coding performance by semantic search is sufficiently practical for supporting the input of information such as symptoms with the granularity required by CDSS. Conclusion: This study has shown that information extraction, semantic search, and EHR-CDSS interoperability using HL7 FHIR are useful for improving CDSS usability. This method can also be applied to other CDSSs, making it easy to collaborate with various EHRs.