FRAGMENTATION: How to Avoid It

1. Adopt Open Standards: FHIR, SNOMED, ICD, LOINC

• The basis of any interoperable system is a shared language.
• When every module speaks a different “dialect,” the integration becomes expensive and unreliable.

Use open global standards:

• FHIR: Fast Healthcare Interoperability Resources for APIs and data exchange.
• SNOMED CT for clinical terminology.
• ICD-10/ ICD-11 for disease coding.
• LOINC for lab results.

Example: A lab report from a rural PHC, using FHIR + LOINC, can automatically populate the patient’s record in the state HMIS dashboard or PMJAY claim portal without any manual entry.

2. Design Modular, API-Driven Architecture

Instead of creating monolithic applications, design microservices to expose data through standardized APIs.

Each service, such as Beneficiary Identification, Preauthorization, Claim Submission, and Wallet Management, now becomes:

• Interconnected via APIs and authentication tokens.
• Easier to upgrade without breaking the whole system.

3. Establish a Federated Data Architecture

Centralized databases may be seductive yet are hazardous in that they build points of failure and reduce autonomy.

Instead, employ a federated model:

• Each institution maintains its own data (sovereignty retained).
• Using common registries (facility, health worker, patient) ensures that all users are referring to the same record master.

Example: A Rajasthan-based hospital keeps the patient data locally, but shares the anonymized claim details to a central PM-JAY database through consented APIs.

4. Creating a Unified Health ID and Registry Layer.

The common cause of fragmentation is inconsistency in identity systems: patient names spelled differently, missing IDs, or duplicate records.

Solutions:

• Implement unique digital health IDs, such as India’s Ayushman Bharat Health Account-ABHA.
• Maintain linked registries: patient, provider, facility, and payer.

 Result: Every patient, provider, and facility can be uniquely identified across systems, enabling longitudinal tracking and analytics.

5. Governance Over Technology

• Even perfect APIs will fail if institutions don’t trust or coordinate.
• Strong digital health governance makes sure alignment across stakeholders:
• National/state-level health data councils
• Memoranda of Understanding between agencies.
• Data-sharing protocols backed legally and ethically.
• Periodic interoperability audits.

 Example: The National Health Authority (NHA) in India mandates ABDM compliance audits to ensure systems aren’t diverging into new silos.

6. Consent and Trust Frameworks

• To prevent “shadow silos”-organizations hoarding data out of fear that it will be misused-you need transparent consent mechanisms:
• Explain what data is being shared and why.
• Allow patients to easily view, permit, or revoke consent.
• Use tokenized time-bound data access, for example, ABDM’s consent manager.

 Human Impact: A patient feels in control and not exposed while sharing data across hospitals or schemes.

7. Encourage Vendor Interoperability

Most health systems are stuck with proprietary systems built by vendors.

Governments and large institutions should:

• Demand open APIs and data export capabilities in all contracts.
• Discourage vendor lock-in by making interoperability a tender requirement.

Example: The RFP for Haryana’s Health Data Lake explicitly laid down the requirement of ABDM Level 3 compliance and API openness, which can be emulated by other states.

8. Unified Dashboards, Diverse Sources

• Dashboards should aggregate data from many systems, but with consistent schemas.
• Harmonize diverse data using ETL pipelines and data lakes.
• Build metadata layers that define what each metric means.
• Always show data provenance – so decision-makers know where the number came from.

Example: Your PM-JAY convergence dashboard housing metrics relating to hospital claims, BIS enrollments, and health scheme coverages is just a perfect example of “one view, many sources.”

9. Invest in Capacity Building

• Technology integration fails when people do not understand the “why.”
• How interoperability works.
• Why consistent data entry matters.

Impact: better adoption, fewer mismatched fields, and reduced duplication.

10. Iterative Implementation, Not One Big Bang

Avoiding fragmentation is not about changing all the systems overnight.

It’s about gradual convergence:

• Identify key connectors, such as patient registry APIs.
• Integrate one module at a time.

Example: First, implement the integration of BIS → Preauthorization → Claims, and then embark on Wallet, FWA, and Hospital Analytics modules.

 The Human Side of Integration

• Technology alone does not bridge silos – people do.
• A doctor needs to trust the data coming from another hospital.
• A policymaker needs to see better insights, not more numbers.

Building that trust means showing real benefits:

• Fewer duplicate entries.
• Faster claim approvals.
• Better patient outcomes.

That’s where the “why” of integration becomes real, and fragmentation starts to fall away.

Imagine a national “digital health highway”:

• Think of each hospital, lab, insurer, and public health scheme as a vehicle.
• APIs are the standardized lanes.
• The governance framework is the traffic law.
• The goal isn’t one app for all; it’s many apps linked by shared DNA.

The Takeaway

Avoiding fragmentation isn’t just about integration; it’s about coherence, continuity, and compassion. A truly connected health system views every patient as one person across many touchpoints, not many records across many databases. They create a single, trusted heartbeat for an entire healthcare ecosystem.