India’s ABDM series so far has traced an arc from architecture to philosophy: how the infrastructure was designed [Articles 1–2], what the HFR and HPR registries require of providers [Article 3], how clinical data flows through the HIE-CM and FHIR layers [Article 4], and why the consent architecture represents a deliberate governance choice rather than a technical default [Article 5]. What those articles examined at the national level, this one examines at the level where implementation takes place: the state.
Because public health is constitutionally a state subject, and the delivery of healthcare largely rests with the states [7], the Ayushman Bharat Digital Mission’s (ABDM) national architecture can only be as effective as the state systems that connect to it. And those systems are not uniform. Academic assessment of ABDM has documented significant inter-state variation, particularly in health facility and health professional registration, with the Healthcare Professionals Registry (HPR) showing especially wide disparity across states; that assessment draws on public dashboard data through 2023, and is best read as evidence that variation is structural rather than as a current ranking [8].
Understanding what drives that variation and what it means for the ecosystem is the next layer of this series.
Why States, Not Just the Centre, Determine ABDM’s Reach
ABDM is a centrally designed programme, but its implementation is federally distributed. The National Health Authority (NHA), operating under the Ministry of Health and Family Welfare, develops, oversees, and coordinates the national infrastructure: the ABHA (Ayushman Bharat Health Account) system, the HFR (Health Facility Registry) and HPR gateways, the HIE-CM (Health Information Exchange and Consent Manager) consent layer, and the NHCX (National Health Claims Exchange). But because the delivery of healthcare rests with states and union territories, they take the lead in implementing the different components of the scheme [9].
This is not administrative detail. The HIE-CM consent architecture described in Article 5 patient-level control over data sharing, works in practice only where participating providers and systems are connected to ABDM. HFR registration and HPR verification strengthen provider discoverability, identity assurance, and implementation readiness, but registry registration alone does not create an exchange. A state that has not built out those connected layers presents its patients with an infrastructure that exists in principle but not yet in practice.
The NHA recognised this structural reality in its state office guidelines, allocating a total of Rs. 500 crore across states and union territories over five years (FY 2021–22 to FY 2025–26) to fund state-level ABDM offices, technical consultants, and implementation teams, with support structured to reflect differences in state size and implementation needs rather than a uniform starting point [2].
The Numbers: A Qualitative Three-State Comparison
Three states offer a useful frame for understanding how different starting points, policy priorities, and infrastructure investments produce different ABDM outcomes. One clarification matters before the comparison begins: this is a qualitative comparison, not a like-for-like current ranking. Uttar Pradesh can be described through a precise, dated 2026 record-linkage figure, while Tamil Nadu and Kerala are characterised largely through the histories and academic assessments of their digital-health programmes. A note later in this article explains why symmetric, same-date figures for every indicator are not currently available from public sources.
One data point is fully verifiable because the government reported it directly. Announcing the milestone of 100 crore ABHA-linked health records on 22 May 2026, the Press Information Bureau noted high linked-record volumes across several states, including Uttar Pradesh (15.03 crore), Andhra Pradesh (11.95 crore), Bihar (7.37 crore), Rajasthan (6.32 crore), and Gujarat (4.77 crore) [1].
These figures capture a single dimension: the volume of records linked, which, as the sections below discuss, is shaped largely by how far a state has integrated its own programme platforms with ABDM. Volume is one useful lens on progress rather than a complete measure of a state’s digital-health development, and states not named on this particular metric may be advancing along other dimensions, as the Tamil Nadu and Kerala examples illustrate.
Uttar Pradesh: scale driven by state platform integration
On record-linkage volume, Uttar Pradesh reports one of the highest totals in the country: as of 22 May 2026, it had linked over 15.03 crore ABHA-linked health records [1].
That performance traces directly to the state government’s integration of its own platforms with the ABDM national framework. The eKavach platform, Uttar Pradesh’s state-level health programme infrastructure, became a significant driver of record linkage, routing state programme data through ABDM’s systems at scale; the national milestone release explicitly names eKavach among the state platforms behind the achievement [1]. The Scan and Share initiative was expanded to 792 public health facilities across the state and, according to the state’s own best-practice documentation, reduced OPD registration time from 30–40 minutes to 5–10 minutes while generating large volumes of ABHA-linked patient interactions [6].
Academic assessors noted that Uttar Pradesh aligned its HMIS (Health Management Information System) platforms, trained medical officers, and initiated ABHA creation drives at primary health centres, proactive state-level steps that translated the national architecture into ground-level adoption [10].
What the UP example demonstrates is that state-led public-platform integration can generate large volumes of linked records; the driver here is the decision to route existing government programme data through ABDM. Parallel state-platform approaches can also be seen in Gujarat’s TeCHO platform and Rajasthan’s iHMS system, both named in the same national release as contributors to their states’ record-linkage totals [1].
Tamil Nadu: an early start and a different integration path
Tamil Nadu’s position is structurally different from Uttar Pradesh’s, and harder to read from aggregate numbers alone.
Tamil Nadu was among the first states in the country to implement a digital health programme, launching a comprehensive state-wide HMIS in 2008 with World Bank assistance, designed to streamline clinical, logistical, and administrative processes [11]. That early investment gave Tamil Nadu substantial public-sector digital-health capacity before ABDM existed.
This creates a layering task. Organisations that built substantial prior digital infrastructure integrate established systems with newer ABDM components, rather than building fresh. As a matter of technical implementation, an existing HMIS does not automatically become an ABDM-connected system: connecting it typically involves technical integration, FHIR-compliant data outputs, and HFR/HPR registration of facilities and professionals that may already exist in other registries. This is an implication of how ABDM integration works, not a documented account of Tamil Nadu’s specific system.
This is a sequencing difference rather than a performance one. States with early HMIS investments approach ABDM integration differently from states building digital capacity more recently. The Tamil Nadu Health Systems Project, a multi-year World Bank-partnered programme, continues to support broader health-system strengthening and digital-health capacity that may facilitate future ABDM integration [12].
The broader pattern holds across the country: the federal structure of governance and the differing strength of state health systems have shaped ABDM implementation. States with longer histories of digital investment sometimes carry more complex legacy-integration work, while states with newer systems can build for ABDM compatibility from the outset [10].
Kerala: an earlier starting point, a different kind of base
Kerala’s contribution to national ABHA record totals does not, on its own, reflect the full picture of its digital-health development. The state piloted its e-Health project in Thiruvananthapuram district in 2016 and launched it state-wide in 2017, before the national ABDM framework existed, introducing permanent unique health identification across its public health facilities and building the foundations of an electronic health record system with World Bank and MeitY (Ministry of Electronics and Information Technology) support [13].
A 2025 study published in Discover Public Health (Springer Nature) assessed eHealth adoption across Kerala’s public health facilities from the programme’s 2016 launch through April 2024. It found adoption occurring across districts and facility types, with utilisation data including OPD visits, inpatient admissions, laboratory investigations, and unique health ID coverage, and noted that areas such as digital literacy and infrastructure variation across facilities warrant continued attention for the full benefits of digitalisation to be realised [13].
Kerala’s path into ABDM therefore runs through integration with its existing eHealth infrastructure, rather than starting from scratch. Peer-reviewed literature cites several states, including Uttar Pradesh, Rajasthan, West Bengal, and Kerala, for taking proactive steps to integrate ABDM into their existing health systems [10].
Kerala’s e-Health infrastructure may provide a substantial base of structured clinical data utilisation covering hospital visits, laboratory investigations, and unique health-ID coverage across public facilities. The extent to which that data is already ABDM-connected and interoperable should not be inferred from e-Health adoption alone; that is a separate technical question. What can be said is that Kerala faces an integration task that differs from states prioritising rapid ABHA creation and record linkage: connecting an established clinical database to ABDM’s consent layer through HIE-CM integration (as described in Article 4) is a different undertaking from generating ABHA numbers at scale.
The Structural Factors Behind the Divergence
The three-state comparison points to factors that explain inter-state variation more generally.
Prior digital infrastructure. States that invested early in HMIS or eHealth platforms (Tamil Nadu from 2008, Kerala from 2016–17) have a different relationship with ABDM integration than states building digital capacity for the first time. Prior investment creates an integration task; its absence allows a state to build for ABDM compatibility from the start. Neither path is inherently faster.
State platform integration decisions. UP’s record-linkage volume traces to the decision to integrate eKavach, a state programme platform, with ABDM’s systems. This approach, treating existing government programme data as ABDM-compatible, produces large record-linkage numbers relatively quickly. Where states have taken different integration approaches, linked-record volumes are correspondingly different.
Private-sector participation. Private-sector adoption is at an earlier stage than public-sector adoption, and this shapes the ecosystem in every state. In Arthur D. Little’s February 2024 analysis, private hospitals accounted for approximately 70% of healthcare market share, while around 30% of HFR registrations in that dataset were private-sector facilities; of roughly 35 crore reports linked through the HFR as of February 2024, about 2% were contributed by private providers [14]. This reflects where private-sector onboarding stood nationally at that time, including in states with strong public-sector adoption. For many patients using private hospitals, clinics, and diagnostic providers, ABDM-enabled consent-based exchange may still be in the process of being enabled, particularly where a provider’s systems are not yet connected to ABDM. Recognising this, the NHA has made private-sector onboarding one of the priority areas for its Digital Health Incentive Scheme (DHIS) [4].
Provider registration depth. The distinction Article 3 drew between registry registration and active use matters here. As of 6 February 2025, 3,63,520 health facilities had registered on the HFR, and 1,59,020 fewer than half were using ABDM-enabled software [3]. Registration creates a directory entry; software integration creates the data flows. State variation in this ratio likely reflects differences in technical support availability, facility size, and the incentive environment.
Digital readiness and workforce support. Peer-reviewed research on ABDM adoption at primary and community health centres notes that many facilities are still digitising their core work processes, and that medical officers managing these facilities can encounter practical challenges as they adopt ABDM workflows [9]. These are capacity-building needs rather than fixed conditions; because they depend on local resources, training, and infrastructure, the level of readiness naturally varies from one setting to another, which is why workforce training and phased onboarding form part of the rollout.
What the NHA Has Done to Address Variation
Recognising that inter-state variation is a structural feature of a health system where states retain implementation authority, the NHA has deployed several instruments to support adoption.
The Digital Health Incentive Scheme (DHIS), launched in January 2023, provides financial incentives to healthcare facilities and health technology companies for ABHA-linked digital record transactions. A Lok Sabha reply reported that Rs. 73,28,18,430 (approximately Rs. 73.28 crore) had been disbursed under the scheme as of 17 March 2025 [4]. The incentives are designed to help offset the upfront cost of adopting ABDM-compliant systems, supporting providers through the transition to digital record-keeping.
The Model ABDM Facility Initiative selects public and private facilities for end-to-end ABDM digitisation, with the intent that these serve as reference implementations for their regions. By November 2024, 133 facilities had been selected and were undergoing action-plan workshops [5].
The Microsite initiative, designed specifically to support private-sector onboarding, had operationalised 121 microsites by late 2024, registering over 48,000 facilities and linking 32 lakh ABHA health records through these concentrated adoption drives [5].
ABHA creation has also been extended through assisted and facility-based channels, including camps, microsites, and Scan and Share counters, recognising that internet connectivity and familiarity with digital tools vary, and that assisted pathways help reach people for whom self-service online registration may not be the easiest option [5].
Reading the Numbers Carefully
One point of interpretation runs through this comparison, and it connects to Article 5’s account of the consent architecture.
Differences between states in ABHA-linked record volume reflect, above all, how far each state has integrated its own programme platforms with ABDM. A high volume shows that a state has routed large amounts of programme data through the national system; it does not, by itself, indicate how much of that data takes the form of facility-level FHIR (Fast Healthcare Interoperability Resources) records, or how often patients are using the consent-based HIE-CM layer. Volume and functional interoperability are related but distinct milestones.
That distinction matters because the consent architecture Article 5 described, where patients approve time-bound, purpose-specific data-sharing requests, depends on more than a record count. A record supports meaningful consent-based exchange when it is available as a structured, interoperable clinical record through an ABDM-connected Health Information Provider (HIP), with the exchange itself enabled by ABDM’s consent and interoperability infrastructure. The public figures cited in this article do not, on their own, confirm that every linked record meets those conditions.
This is also why the comparison here is qualitative rather than a single side-by-side scorecard across every indicator: ABHAs created, records linked, HFR and HPR registrations, and facilities using ABDM-enabled software. Per-state figures become public gradually, through Parliament answers and press notes, on different dates and for different indicators, rather than as one continuously updated per-state dataset. A dated figure happens to be available for Uttar Pradesh because national releases have highlighted high-volume states, not because any state’s underlying data is more complete or better structured than another’s. Working from figures that can each be verified on their own terms gives a more reliable picture than forcing mismatched numbers into a common table.
What This Means for Providers and Technology Partners
For hospital systems, insurers, and health technology platforms operating across multiple states, interstate variation has practical consequences.
Integration readiness differs by geography. A hospital group with facilities in Tamil Nadu and Uttar Pradesh will encounter different state-level ABDM support structures, different legacy-integration considerations, and different levels of patient familiarity with ABDM. Technology partners building ABDM-compliant solutions need state-specific implementation roadmaps, not only national compliance frameworks.
Private-sector participation also carries commercial relevance. Private providers serve a large share of the country’s patients, and their participation in ABDM continues to grow. For smaller hospitals, clinics, and diagnostic centres, the cost and effort of integration are practical factors in that process. Making participation easier and more affordable is a shared effort across providers, technology partners, and the NHA.
The DHIS incentive structure, with Rs. 73.28 crore disbursed as of March 2025 [4] indicates that financial incentives are one of the NHA’s important near-term levers for private-sector adoption, alongside model facilities and microsites. For providers and technology partners weighing the economics of ABDM integration, the incentive programme is a material consideration.
The Arc of the Series
The five articles preceding this one moved from what ABDM is to how its infrastructure works and what its consent architecture means; this article turns to where adoption stands across the country’s varied health systems. The pattern that emerges is consistent: ABDM’s design is architecturally coherent, its consent philosophy is deliberate, and its adoption varies from state to state in ways that reflect India’s broader health-system diversity.
The variation examined here is not a temporary condition to be resolved by a single policy instrument. It reflects differences in state health systems, historical investment, administrative capacity, and the pace of private-sector participation that have developed over decades. One thread runs across every state: private-sector adoption is still developing, and because private hospitals, clinics, and diagnostic providers serve a large share of patients, their growing participation is central to bringing the consent-based exchange layer into full effect nationwide. ABDM provides the shared infrastructure. Continued progress will come through sustained, state-specific implementation work, steady growth in private-sector onboarding, and a health technology ecosystem that meets providers at their actual level of readiness.
CaladriusHealth.AI covers India’s health technology landscape with a focus on ABDM, NHCX, medical billing, and AI in healthcare. This article is part of a series on India’s digital health infrastructure.
Editorial Disclosure: This article is published by CaladriusHealth.AI, a revenue intelligence company building tools for India’s digital health infrastructure. Our commercial interest in ABDM adoption is direct, and readers should weigh our perspective accordingly. The interpretations of law and policy in this article are informational and do not constitute legal advice; compliance teams should consult qualified counsel before making operational or regulatory decisions based on this content.
Sources
All sources are publicly verifiable. Government and statutory sources are listed first; academic and industry sources follow, each explicitly labelled. Figures are attributed to the specific source and reference date on which they were reported.
Government and Statutory Sources
[1] Press Information Bureau, Government of India. 100 Crore Health Records Linked with ABHA under ABDM. 22 May 2026. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2264241 (Primary government source; reports UP 15.03 crore, Andhra Pradesh 11.95 crore, Bihar 7.37 crore, Rajasthan 6.32 crore, Gujarat 4.77 crore, and names the eKavach, TeCHO, and iHMS state platforms)
[2] National Health Authority. Guidelines for Setting Up of State Offices for ABDM. NHA / MoHFW. https://abdm.gov.in/strapicms/uploads/State_Guidelines_ABDM_Final_f766c3b11c.pdf (Primary government source; Rs. 500 crore allocation across states/UTs, FY 2021–22 to FY 2025–26)
[3] Press Information Bureau, Government of India. Update on the Implementation of Ayushman Bharat Digital Mission (ABDM) (Rajya Sabha written reply, Minister of State for Health), 11 February 2025; figures as on 6 February 2025. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2101737 (Primary government source; ABHA 73,98,09,607, records linked 49,06,02,540, HFR 3,63,520, ABDM-enabled software facilities 1,59,020, HPR 5,64,851. Replaces a previously cited MoHFW URL that did not resolve reliably.)
[4] Lok Sabha, Government of India. Unstarred Question No. 3519 — Ayushman Bharat Digital Mission, answered 21 March 2025 (Ministry of Health and Family Welfare); figures as on 17 March 2025. Annexure PDF (Digital Sansad): https://sansad.in/getFile/loksabhaquestions/annex/184/AU3519_VkMcAc.pdf?source=pqals (Primary government source; DHIS disbursement of Rs. 73,28,18,430)
[5] Press Information Bureau, Government of India. Update on Ayushman Bharat Digital Mission. November 2024. https://www.pib.gov.in/PressReleasePage.aspx?PRID=2081482 (Primary government source; 121 microsites and 48,000+ facilities, 133 Model ABDM Facilities, 32 lakh linked records)
[6] NITI for States / ABDM-Uttar Pradesh. A Document on Best Practices of ABDM-UP on Scan & Share & HMIS Implementation (NITI for States landing page and the underlying ABDM-UP best-practice document). 25 June 2025. https://www.nitiforstates.gov.in/best-practice-detail?id=109266 (Government/state best-practice document; the underlying ABDM-UP PDF is the source for the Scan and Share expansion to 792 facilities and the OPD registration-time reduction)
[7] Press Information Bureau / Ministry of Health and Family Welfare, Government of India. Healthcare in India. 25 November 2016. https://www.pib.gov.in/newsite/PrintRelease.aspx?relid=154292 (Primary government source; states “the delivery of health care largely rests with the States, Health being a state subject” and “public health is a state subject.” This reflects the Constitution of India, Seventh Schedule under Article 246, List II — State List, Entry 6: “Public health and sanitation; hospitals and dispensaries.”)
Academic and Industry Sources
[8] Mishra US, Yadav S, Joe W. The Ayushman Bharat Digital Mission of India: An Assessment. Health Systems Reform, Vol. 10, 2024. Taylor & Francis. Published online 22 October 2024. https://www.tandfonline.com/doi/full/10.1080/23288604.2024.2392290 (Peer-reviewed academic source; analysis uses ABDM public dashboard data through 2023 — cited here as historical evidence of inter-state variation, including high HPR disparity, not as a current ranking)
[9] PubMed Central. The Ayushman Bharat Digital Mission (ABDM): Making of India’s Digital Health Story. 2023. https://pmc.ncbi.nlm.nih.gov/articles/PMC10064942/ (Peer-reviewed academic source; state implementation responsibility and workforce/digitisation challenges)
[10] PubMed Central. Digital Foundations for Health Equity: Rethinking Primary Care Through the Ayushman Bharat Digital Mission. Journal of Family Medicine and Primary Care. July 2025. https://pmc.ncbi.nlm.nih.gov/articles/PMC12349786/ (Peer-reviewed academic source; proactive state integration steps, including UP, Rajasthan, West Bengal, and Kerala)
[11] Springer Nature / Journal of the Egyptian Public Health Association. The Coming of Age of Digital Technologies in Global Health Within the Indian Context: A Review. 2024. https://link.springer.com/article/10.1186/s42506-024-00169-5 (Peer-reviewed academic source; explicitly cites Tamil Nadu’s 2008 state-wide HMIS launch with World Bank support)
[12] Tamil Nadu Health Systems Project (TNHSP). Tamil Nadu Health System Reform Program. Government of Tamil Nadu / World Bank. https://www.tnhsp.org/ (Primary state government source)
[13] Springer Nature / Discover Public Health. An Analysis of eHealth Adoption and Utilisation in Kerala. 2025. https://link.springer.com/article/10.1186/s12982-025-00829-7 (Peer-reviewed academic source; e-Health Kerala piloted in Thiruvananthapuram in 2016 and launched state-wide in 2017, with MeitY and World Bank support and permanent unique health identification; assessment covers the 2016 launch through April 2024)
[14] Arthur D. Little. Catalyzing Digital Health in India. March 2024. https://www.adlittle.com/en/insights/report/catalyzing-digital-health-india (Industry / management-consulting source — labelled explicitly; private-sector market share ~70%, ~30% of HFR registrations private, ~2% of ~35 crore linked reports shared by private providers as of February 2024)