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1. Some Key Interoperability Standards in Digital Health 1. HL7: Health Level Seven It is one of the oldest and most commonly used messaging standards. Defines the rules for sending data like Admissions, Discharges, Transfers, Lab Results, Billings among others. Most of the legacy HMIS/HIS systems iRead more
1. Some Key Interoperability Standards in Digital Health
1. HL7: Health Level Seven
- It is one of the oldest and most commonly used messaging standards.
- Defines the rules for sending data like Admissions, Discharges, Transfers, Lab Results, Billings among others.
- Most of the legacy HMIS/HIS systems in South Asia are still heavily dependent on HL7 v2.x messages.
Why it matters:
That is, it makes sure that basic workflows like registration, laboratory orders, and radiology requests can be shared across systems even though they might be 20 years old.
2. FHIR: Fast Healthcare Interoperability Resources
- The modern standard. The future of digital health.
- FHIR is lightweight, API-driven, mobile-friendly, and cloud-ready.
It organizes health data into simple modules called Resources, for example, Patient, Encounter, Observation.
Why it matters today:
- Allows real-time transactions via REST APIs
- Perfect for digital apps, telemedicine, and patient portals.
- Required for modern national health stacks – ABDM, NHS etc
FHIR is also very extensible, meaning a country or state can adapt it without breaking global compatibility.
3. DICOM stands for Digital Imaging and Communications in Medicine
- The global standard for storing and sharing medical images.
- Everything uses DICOM: radiology, CT scans, MRI, ultrasound.
Why it matters:
Ensures that images from Philips, GE, Siemens, or any PACS viewer remain accessible across platforms.
4. LOINC – Logical Observation Identifiers Names and Codes
Standardizes laboratory tests.
- Example: Glucose fasting test has one universal LOINC code — even when hospitals call it by different names.
This prevents mismatched lab data when aggregating or analyzing results.
5. SNOMED CT
- Standardized clinical terminology of symptoms, diagnoses, findings.
Why it matters:
Instead of each doctor writing different terms, for example (“BP high”, “HTN”, “hypertension”), SNOMED CT assigns one code — making analytics, AI, and dashboards possible.
6. ICD-10/ICD-11
- Used for diagnoses, billing, insurance claims, financial reporting, etc.
7. National Frameworks: Example – ABDM in India
ABDM enforces:
- Health ID (ABHA)
- Facility Registry
- Professional Registry
- FHIR-based Health Information Exchange
- Gateway for permission-based data sharing
Why it matters:
It becomes the bridge between state systems, private hospitals, labs, and insurance systems without forcing everyone to replace their software.
2. Why Health Systems Are Often Siloed
Real-world health IT systems are fragmented because:
- Each hospital or state bought different software over the years.
- Legacy systems were never designed for interoperability.
- Vendors lock data inside proprietary formats
- Paper-based processes were never fully migrated to digital.
- For many years, there was no unified national standard.
- Stakeholders fear data breaches or loss of control.
- IT budgets are limited, especially for public health.
The result?
Even with the intention to serve the same patient population, data sit isolated like islands.
3. How Health Systems Can Overcome Siloed Systems & Enable Real-Time Data Exchange
This requires a combination of technology, governance, standards, culture, and incentives.
A. Adopt FHIR-Based APIs as a Common Language
- This is the single most important step.
- Use FHIR adapters to wrap legacy systems, instead of replacing old systems.
- Establish a central Health Information Exchange layer.
- Use resources like Patient, Encounter, Observation, Claim, Medication, etc.
Think of FHIR as the “Google Translate” for all health systems.
B. Creating Master Patient Identity: For example, ABHA ID
- Without a universal patient identifier, interoperability falls apart.
- Ensures the same patient is recognized across hospital chains, labs, insurance systems.
- Reduces duplicate records, mismatched reports, fragmented history.
C. Use a Federated Architecture Instead of One Big Central Database
Modern systems do not pool all data in one place.
They:
- Keep data where it is (hospital, lab, insurer)
- Only move data when consent is given
- Exchange data with secure real-time APIs
- Use gateways for interoperability, as ABDM does.
This increases scalability and ensures privacy.
D. Require Vocabulary Standards
To get clean analytics:
- SNOMED CT for clinical terms
- LOINC for labs
- ICD-10/11 for diagnoses
- DICOM for images
This ensures uniformity, even when the systems are developed by different vendors.
E. Enable vendor-neutral platforms and open APIs
Health systems must shift from:
- Vendor-Locked Applications
- to
- open platforms where any verified application can plug in.
This increases competition, innovation, and accountability.
F. Modernize Legacy Systems Gradually
Not everything needs replacement.
Practical approach:
- Identify key data points
- Build middleware or API gateways
- Enable incremental migration
Bring systems to ABDM Level-3 compliance (Indian context)
G. Organizational Interoperability Framework Implementation
Interoperability is not only technical it is cultural.
Hospitals and state health departments should:
- Define governance structures
- Establish data-sharing policies
- Establish committees that ensure interoperability compliance.
Establish KPIs: for example, % of digital prescriptions shared, % of facilities integrated
H. Use Consent Management & Strong Security
Real-time exchange works only when trust exists.
Key elements:
- Consent-driven sharing
- Encryption (at rest & in transit)
- Log auditing
- Role-based access
- Continuous monitoring
- Zero-trust architecture
A good example of this model is ABDM’s consent manager.
4. What Real-Time Data Exchange Enables
Once the silos are removed, the effect is huge:
- For Patients
- Unified medical history available anywhere
- Faster and safer treatment
- Reduced duplicate tests and costs
- For Doctors
- Complete 360° patient view
- Faster clinical decision-making
- Reduced documentation burden with AI
- For Hospitals & Health Departments
- Real-time dashboards like PMJAY, HMIS, RI dashboards
- Predictive analytics
- Better resource allocation
Fraud detection Policy level insights For Governments Data-driven health policies Better surveillance State–central alignment Care continuity across programmes
5. In One Line
Interoperability is not a technology project; it’s the foundation for safe, efficient, and patient-centric healthcare. FHIR provides the language, national frameworks provide the rules, and the cultural/organizational changes enable real-world adoption.
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1. Begin with a common vision of “one patient, one record.” Interoperability begins with alignment, not with software. Different stakeholders like hospitals, insurers, public health departments, state schemes, and technology vendors have to agree on one single principle: Every patient is entitled toRead more
1. Begin with a common vision of “one patient, one record.”
Interoperability begins with alignment, not with software.
Different stakeholders like hospitals, insurers, public health departments, state schemes, and technology vendors have to agree on one single principle:
Every patient is entitled to a unified, longitudinal, lifetime health record, available securely whenever required.
Without this shared vision:
A patient should not carry duplicate files, repeat diagnostics, or explain their medical history again and again simply because systems cannot talk to each other.
2. Adopt standards, not custom formats: HL7 FHIR, SNOMED CT, ICD, LOINC, DICOM.
When everyone agrees on the same vocabulary and structure, interoperability then becomes possible.
This means:
Data flows naturally when everyone speaks the same language.
A blood test from a rural PHC should look identical – digitally – to one from a corporate hospital; only then can information from dashboards, analytics engines, and EHRs be combined without manual cleaning.
This reduces clinical errors, improves analytics quality, and lowers the burden on IT teams.
3. Build APIs-first systems, not locked databases.
Modern health systems need to be designed with APIs as the backbone, not after the fact.
APIs enable:
An APIs-first architecture converts a health system from a silo into an ecosystem.
But critically, these APIs must be:
Otherwise, interoperability becomes risky, instead of empowering.
4. Strengthen data governance, consent, and privacy frameworks.
Without trust, there is no interoperability.
And there will not be trust unless the patients and providers feel protected.
To this end:
If people feel that their data will be misused, they will resist digital health adoption.
What is needed is humanized policymaking: the patient must be treated with respect, not exposed.
5. Gradual, not forced migration of legacy systems.
Many public hospitals and programs still rely on legacy HMIS, paper-based processes, or outdated software.
Trying to forcibly fit old systems into modern frameworks overnight, interoperability fails.
A pragmatic, human-centered approach is:
Digital transformation only succeeds when clinicians and health workers feel supported and not overwhelmed.
6. Invest in change management and workforce capacity-building.
Health systems are, after all, run by people: doctors, nurses, health facility managers, data entry operators, and administrators.
Even the most advanced interoperability framework will fail if:
Interoperability becomes real when people understand why data needs to flow and how it improves care.
Humanized interventions:
The human factor is the hinge on which interoperability swings.
7. Establish health data platforms that are centralized, federated, or hybrid.
Countries and states must choose models that suit their scale and complexity:
Centralized model
All information is maintained within one large, single national or state-based database.
Federated model
Data remains with the data originators; only metadata or results are shared
Hybrid model (most common)
The key to long-term sustainability is choosing the right architecture.
8. Establish HIEs that organize the exchange of information.
HIEs are the “highways” for health data exchange.
They:
This avoids point-to-point integrations, which are expensive and fragile.
The India’s ABDM, UK’s NHS Spine, and US HIE work on this principle.
Humanized impact: clinicians can access what they need without navigating multiple systems.
9. Assure vendor neutrality and prevent monopolies.
When interoperability dies:
Procurement policies should clearly stipulate:
A balanced ecosystem enables innovation and discourages exploitation.
10. Use continuous monitoring, audit trails and data quality frameworks.
Interoperability is not a “set-and-forget” achievement.
Data should be:
Data quality translates directly to clinical quality.
Conclusion Interoperability is a human undertaking before it is a technical one.
In a nutshell
seamless data integration across health systems requires bringing together:
Continuous Monitoring In the end, interoperability succeeds when it enhances the human experience:
Interoperability is more than just a technology upgrade.
It is a foundational investment in safer, more equitable, and more efficient health systems.
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