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Development of a FHIR-based Korean IPS Data Pipeline and User-Centered UI Design

2026·0 Zitationen·Scientific ReportsOpen Access
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2026

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Abstract

The International Patient Summary (IPS) is a minimal data standard enabling rapid access to essential health information across institutions and borders. Korea provides Fast Healthcare Interoperability Resources (FHIR) data through the "My Health Record" application, which utilizes an implementation guide (IG) inheriting from the KR Core FHIR profiles. However, a standardized workflow for transforming these domestic FHIR resources into IPS-compliant data has not yet been established. This study aimed to assess the feasibility of implementing an IPS-compliant patient summary in Korea using existing FHIR-based resources and national profiles. First, a literature review confirmed IPS as a global standard supporting interoperability and patient-centered care. Second, a gap analysis revealed that six of the seven IPS-required and recommended components successfully mapped to ten KR Core profiles. However, the Device component and the MedicationStatement profile remained unmapped due to the lack of corresponding definitions in the KR Core. Third, real-world FHIR data from three individuals were transformed using ChatGPT-4o into IPS-compatible formats and validated via HAPI FHIR and SMART FRED tools. Fourth, user requirements were identified through personas and expert consultations, highlighting the need for summary and timeline-based UI elements. Fifth, a user interface was developed using Figma based on these requirements.Overall, approximately 86% of required IPS data elements were represented using existing Korean FHIR-based resources. These findings demonstrate the technical feasibility of IPS implementation in Korea, while also highlighting current gaps in terminology coverage and profile alignment. Future work should focus on multi-site validation, increased automation of mapping processes, and governance frameworks to support scalable and reproducible IPS deployment.

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Electronic Health Records SystemsMachine Learning in HealthcareArtificial Intelligence in Healthcare and Education
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