How an AI Agent Automated FHIR Template Creation: Cutting Days of Work to Hours
How I Built an AI Agent That Writes FHIR Templates in Hours Instead of Days
Summary
The author, working on health data interoperability in Indonesia, implemented an AI agent to automate the creation of complex FHIR templates. This significantly reduces the time required for building massive JSON templates (up to 320 resources), which previously took several days or even a week.
Details
The author is a software engineer addressing data interoperability challenges in Indonesian district health systems, integrating data from over a dozen disparate sources (ePuskesmas, Kobo Toolbox, etc.). The core technical challenge lies not in the conversion pipeline itself, but in creating the necessary 'template'—a JSON definition file that maps incoming CSV fields to structured FHIR resources. FHIR models data as discrete resources (Patient, Encounter, Observation, Condition). For complex records like maternal health forms, templates can reach 320 resources and thousands of lines of JSON. Manually creating these templates required days of effort, even with a pre-built CSV data dictionary provided by clinical informatics teams. The article details the technical complexity: mapping raw fields (e.g., `berat_badan`) to specific FHIR resource types, selecting correct LOINC codes (e.g., 85354-9 for blood pressure), and implementing complex logic like fuzzy patient ID matching. The introduction of an AI agent aims to solve this massive time sink, transforming a multi-day manual process into one that can be completed in hours.
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