Quick Summary
ABDM and FHIR help systems exchange data in a standard format. But real interoperability fails when the data is inconsistent: duplicate identities, messy codes, missing units, unclear provenance, and mismatched workflows. Fix data quality first and integrations become dramatically easier.
Why APIs are not the hard part
Many teams treat interoperability as an API project. They focus on endpoints, tokens, and payloads. Those are important, but they are not where projects usually stall.
Projects stall when data is inconsistent and cannot be reliably merged into a longitudinal record.
The four failure points
- Identity matching: the same patient appears under multiple identifiers across labs, hospitals, and portals.
- Terminology mapping: tests and diagnoses use different naming, codes, and local conventions.
- Units and ranges: values arrive without units, reference ranges, or specimen context.
- Provenance: systems cannot show where a value came from or when it was captured.
What to fix first
If you want interoperability to work, start with a practical data quality baseline:
- Canonical patient identity strategy (with governance)
- Minimum terminology mapping rules for top tests and conditions
- Strict rules for units, ranges, and timestamps
- Source links and audit logs for traceability
Why this matters for ABDM
ABDM creates a national pathway for standardized exchange. The institution that wins will be the one that can deliver clean, reliable data into that pathway and consume it safely.
Interoperability is a product and operations problem, not only a protocol problem.
Where Aether fits
Aether is built around longitudinal continuity. That forces the hard work: matching identities, harmonizing data, and preserving provenance. Once the data is clean, standards based exchange becomes much easier to implement.
Sources and further reading
- ABDM: Official site and ecosystem overview
- HL7 FHIR: Overview
- ONC: Interoperability overview (data quality and exchange context)
Information only. Not medical advice.
Next steps
- Define data quality as a readiness requirement, not an afterthought.
- Start with top workflows and top tests, not everything at once.
- Measure success in longitudinal merge rate, not only API uptime.