Umi Khultsum
Universitas Bina Sarana Informatika, Yogyakarta

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Pengembangan Aplikasi Rekamedi Untuk Mendukung Interoperabilitas Data Pelayanan Rawat Jalan Menggunakan Standar FHIR Christina Yuli Anggita; Restu Ardi Putranto; Evapras Kurniawan Panji Saputra; Noor Hasan; Umi Khultsum
Bulletin of Computer Science Research Vol. 6 No. 6 (2026): October 2026
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v6i6.1386

Abstract

The mandatory transition toward Electronic Medical Records (EMR) integrated with the SATUSEHAT platform confronts healthcare facilities with data interoperability issues, as most hospital and primary care information systems are developed independently with siloed database structures. The most difficult technical challenge is mapping operational data stored in tabular form into nested Fast Healthcare Interoperability Resources (FHIR) R4 structures, complete with ICD-10, ICD-9 CM, LOINC, and SNOMED-CT coding and the inter-resource references that must be satisfied before SATUSEHAT accepts the data. Unlike previous studies that stopped at conceptual reviews or performance testing, this research aims to design and build RekaMedi, a web-based application that addresses this challenge through automatic transformation of data into FHokIR resources, such as Patient, Encounter, Condition, and Observation, along the 28-step outpatient service flow of the SATUSEHAT Playbook, supported by medical code validation and synchronization through a RESTful API. The software was developed using the Waterfall method with PostgreSQL, Directus, Astro, and React. Blackbox Testing showed that all tested core functions performed according to specification, while Usability Testing involving nine participants yielded a Success Rate of 93.2% and a System Usability Scale (SUS) score of 75.6 (Grade B), above the reference average of 68. These results indicate that automatic data transformation can be delivered through an interface that users learn effectively, although API-level integration testing and evaluation by healthcare workers in real facilities remain for future work.