India’s healthcare system generates massive volumes of information every day – symptoms, diagnoses, prescriptions, lab results, referrals, admissions, discharges, and public-health reports. Yet when clinicians need a patient’s history, districts need credible trends, or policymakers need decision grade indicators, the same reality shows up: data is incomplete, delayed, inconsistent, or trapped in silos.
The core problem is not “lack of data” or even “lack of software”. It is a systems issue: fragmented care pathways, weak point-of-care capture, inconsistent standards, parallel reporting burdens, and uneven governance. Digitization can help sometimes dramatically but only when it improves capture at the source, enables interoperable exchange, and builds trust through privacy-by-design.
What’s broken
- Silos across care. Patients move between clinics, labs, pharmacies, district hospitals, tertiary centres, and telemedicine. Each creates partial records, often unlinked.
- Parallel reporting. Multiple programmes and dashboards request overlapping entries, increasing workload and reducing data quality.
- Weak quality controls. Evidence from HMIS research in India highlights risks of misreporting and incentives that can distort administrative data, especially when performance monitoring is punitive or target-driven. (OUP Academic) A national study comparing HMIS coverage indicators with NFHS-5 also flags gaps in external consistency underscoring why validation and governance matter. (PMC)
- No common meaning. Even when records are digital, unstandardised fields (free-text diagnoses, variable lab formats) make data hard to aggregate or exchange.
- Trust risks. Health data is highly sensitive. Without clear lawful processing, minimisation, security safeguards, and accountability, digitisation can erode trust and participation. The DPDP Act, 2023 provides the legal backbone for digital personal data processing in India. (MeitY)
What digitization is already improving
Digitisation helps most when it reduces duplication and makes routine capture “the default” rather than an extra burden.
Digital service scale – India’s national telemedicine platform eSanjeevani shows that large-scale digital delivery can generate high-frequency service and encounter data. MoHFW reported that as of 6 April 2025, e-Sanjeevani had served over 36 crore patients via teleconsultations since 2020. (Ministry of Health and Family Welfare)
Stronger privacy operating environment – The Government notified the DPDP Rules, 2025 (reported as notified on 14 November 2025), which operationalize the DPDP Act and tighten expectations around responsible processing. (Press Information Bureau)
Why ABDM is the real lever for fixing data collection
ABDM (Ayushman Bharat Digital Mission) is best understood as national digital rails, not a single app. Its design targets the root causes of poor data collection: fragmentation, duplication, and non-standard capture.
- ABHA: linking records across providers. The ABDM ecosystem includes ABHA as a health account/identifier layer that can help link encounters and documents across facilities over time reducing duplicate registrations and “lost history” (subject to consent and participation). (Ayushman Bharat Digital Mission)
- HFR and HPR: cleaner master data. ABDM’s Health Facility Registry (HFR) and Healthcare Professionals Registry (HPR) strengthen “who/where” accuracy critical for deduplication, analytics, and accountability. (Ayushman Bharat Digital Mission)
- HIE-CM: consent-based exchange instead of ad-hoc copies. ABDM’s exchange approach is designed to reduce informal, untracked sharing (paper printouts, WhatsApp photos) by enabling interoperable sharing through governed pathways so data becomes portable and auditable. (Ministry of Health and Family Welfare)
- UHI: expanding digital access and structured utilisation data. ABDM also includes the Unified Health Interface (UHI) as part of its ecosystem stack, supporting discovery and service access across apps, which can improve digital footprints of care and service utilisation capture. (Ayushman Bharat Digital Mission)
Put simply: ABDM turns digitisation from isolated “facility IT projects” into an ecosystem approach where data captured once can be reused safely, linked longitudinally, and exchanged with consent.
What to prioritise next
If India wants digitisation to measurably improve data collection quality, three priorities matter.
- Capture. Standardise and simplify point-of-care capture (minimum necessary structured data), reduce duplicate entry, support offline-first workflows where required.
- Connect. Make interoperability and consent-based exchange real ABHA linkage, registry hygiene, and routine exchange pathways that fit clinical workflows.
- Govern. Institutionalise data-quality audits (completeness, consistency, plausibility), role-based access, logging, and breach readiness aligned with DPDP obligations and the operational expectations in the DPDP Rules. (MeitY)
Risks to manage
- Exclusion – Digitisation must preserve assisted access (front desks, community facilitation, call centres) so care does not become “app-only”.
- Over-collection – Health systems should operationalise purpose limitation and minimisation collect what is needed, for a defined purpose, with retention discipline consistent with the DPDP framework. (MeitY)
- Digital silos – Procurement and certification should reward interoperability and penalise closed ecosystems that recreate fragmentation digitally.
Conclusion
India’s healthcare data collection challenge is structural: fragmented delivery, uneven last-mile readiness, inconsistent standards, and weak validation. Digitisation helps when it is implemented as infrastructure identity, registries, consented exchange, and standards rather than as disconnected applications. ABDM provides that architecture through ABHA, registries, and ecosystem gateways, while e-Sanjeevani demonstrates India’s ability to run national-scale digital health systems that generate routine service data. (Ayushman Bharat Digital Mission) If India executes the next phase with disciplined workflow redesign and governance, digitization can convert health data from “reported numbers” into a trusted, decision-grade public good.
The views and opinions expressed here are solely my own and do not necessarily reflect those of any organization or institution.
