AI / Solutions
Indonesian hospitals sit at 16% EHR adoption today, against a Ministry of Health target of 87% by end-2026. SATUSEHAT is integrating 36,000+ facilities. The documents haven't digitized themselves, handwritten discharge summaries, faxed referrals, multi-format lab reports, Bahasa clinical notes. We build medical-records AI that extracts, structures, and aligns this content to FHIR, with UU PDP access controls, clinician-in-the-loop review, and SATUSEHAT-ready exports.
The gap between 16% and 87% doesn't close by typing faster. Discharge summaries, admission records, referrals, lab reports, imaging metadata, it all has to be digitized, structured, aligned to FHIR schemas, and posted into SATUSEHAT-compatible endpoints while respecting UU PDP access controls. We build clinical extraction that reads Bahasa and handwriting, FHIR mapping that passes your integration tests, and human-in-the-loop review for every record a clinician would rightly want to see before it hits the shared platform.
Four phases from fragmented sources to a SATUSEHAT-ready, UU PDP-compliant records backbone.
We map your record landscape. Which sources, paper, fax, legacy HIS, lab systems, imaging metadata? Which document types drive the most re-keying hours? Which clinical fields matter most for SATUSEHAT integration priority? Where does UU PDP access control apply, and where do clinicians still want a human in the loop?
A six-week pilot on one record type, typically discharge summaries, admission records, or referrals. We build extraction + FHIR alignment + clinician review queue + SATUSEHAT export. Pass/fail on extraction accuracy against clinician-graded ground truth, FHIR validation pass rate, and audit-log completeness.
We engineer the evaluation harness, clinical extraction accuracy, FHIR conformance testing, access-control test suite, clinician-override logging. UU PDP compliance documented for your data-protection officer. SATUSEHAT integration tested against real endpoint contracts.
Handover to your medical records team, IT, and clinical governance. New record types added as modules. You get dashboards, retraining cadence for the extraction models, and the clinician-review workflow as an ongoing quality control, not a launch-day artifact.
Four capabilities that together close the gap between your document pile and SATUSEHAT.
Bahasa NER for diagnoses, procedures, medications (with Indonesian generic-name mapping), allergies, and clinical observations. Handwriting recognition on discharge summaries. Multi-format input (PDF, scan, fax, photo). Confidence scores and source-region citations on every extracted field.
Extracted fields mapped to FHIR R4 resources (Patient, Encounter, Observation, Condition, MedicationRequest, AllergyIntolerance). Conformance testing against real SATUSEHAT endpoint contracts. Version management as FHIR profiles evolve.
Authenticated, idempotent, retry-safe integration with SATUSEHAT-compatible endpoints. Error handling for partial failures. Monitoring and alerting so integration issues surface before they become a compliance finding.
Low-confidence records routed to clinician review with the ambiguity already highlighted. Every extraction, override, and record access logged. Export formats that satisfy UU PDP data-subject requests and internal audit needs.
The deadline is real, the scale is national, and the market has picked a direction.
Indonesia's Ministry of Health has set a national hospital EHR adoption target of 87% by end-2026, against a current baseline of roughly 16%. The delta cannot close without document-digitization AI. Hospitals that start now finish on time.
74% of Indonesian hospital leaders surveyed in the 2024 Philips Future Health Index plan to adopt Gen AI within 18 months. The intent has landed. The execution gap, integration, compliance, clinician trust, is where well-built medical records AI either earns its place or doesn't.
SATUSEHAT is integrating more than 36,000 healthcare facilities into a shared data platform, the kind of scale where a single non-compliant integration becomes a national visibility issue. Medical records AI built for SATUSEHAT treats the platform's contract as a design input.
How access controls, data-subject requests, and SATUSEHAT endpoint contracts fit together in practice. A CMIO-oriented walkthrough of what good looks like.
Indonesian medication generic names, Bahasa diagnosis phrasing, handwritten discharge notes. Where global medical-AI models break and what it takes to build the extraction layer Indonesian hospitals actually need.
A playbook for mid-size private hospital groups, where to start, what to pilot first, how to sequence the SATUSEHAT integration, and the six-week path from paper records to structured data.
Tell us the document type that costs the most clinical time, discharge summaries, admission records, referrals, lab results, imaging metadata. We'll scope a six-week pilot with real records (de-identified), with clinician ground-truthing and SATUSEHAT endpoint conformance testing.
Start a project