India’s National Health Authority runs one of the world’s largest health insurance and digital health programmes. However, when the data is examined State by State, outcomes diverge sharply. This explainer breaks down PM-JAY and ABDM performance using verified data, state-wise scorecards, and clear analysis to show why healthcare results depend more on State capacity than on national design.
New Delhi (ABC Live): India’s health reforms under the National Health Authority (NHA) often appear impressive when viewed through national aggregates—crores of beneficiaries enrolled, crores of Health IDs created, and tens of thousands of crores paid out as claims. However, once analysts re-check the data and examine it State by State, a far more uneven reality becomes visible. In practice, India does not experience one uniform NHA outcome; instead, it experiences several parallel ones.
Accordingly, this explainer reassesses PM-JAY and ABDM performance using range-based, audit-safe data, removes false precision, and clearly separates verified facts from analytical inference. At the same time, it places the findings within ABC Live’s broader health-system coverage, including the internal explainer How India Can Benefit from Integrated Pharmacology (ABC Live, 7 November 2025).
How the NHA Framework Operates on the Ground
At the institutional level, NHA performs two clear functions. First, it provides financial risk protection through Ayushman Bharat–PMJAY. Second, it builds digital public infrastructure through the Ayushman Bharat Digital Mission (ABDM).
However, the Constitution assigns healthcare delivery to the States. As a result, NHA does not equalise outcomes across India. Instead, it amplifies the administrative, fiscal, and hospital capacity that already exists within each State.
Verified National Baseline
Before comparing States, this analysis establishes a verified national context. Cross-checked disclosures from NHA dashboards, Union Budget documents, and MoHFW releases confirm the following ranges:
| Indicator | Re-verified Range |
|---|---|
| PM-JAY eligible population | ~49–50 crore |
| e-cards generated | ~33–35 crore |
| Cumulative hospitalisations | ~6.5–7.2 crore |
| Cumulative claims paid | ~₹90,000–95,000 crore |
| Average claim size | ₹13,500–15,500 |
| ABDM Health IDs created | ~45–50 crore |
| Public health spending | ~1.3–1.4% of GDP |
Therefore, all State-wise comparisons in this report rely on these verified ranges rather than isolated dashboard snapshots.
PM-JAY Utilisation: Why Rates Matter More Than Raw Numbers
To avoid misleading conclusions, this analysis uses annual hospitalisations per 1,000 eligible population, rather than absolute admission counts.
| State | Utilisation Band | What It Signals |
|---|---|---|
| Tamil Nadu | Low (3–6) | Strong public hospitals reduce insurance dependence |
| Kerala | Low (2–5) | PM-JAY plays a supplementary role |
| Karnataka | Moderate (6–9) | Balanced public–private mix |
| Gujarat | Moderate (6–9) | Private-led utilisation |
| Maharashtra | Moderate (5–8) | Urban skew conceals rural gaps |
| Rajasthan | High (9–13) | Insurance substitutes weak capacity |
| Uttar Pradesh | High (10–15) | Lack of alternatives drives usage |
| Bihar | Moderate (6–9) | Access constraints cap utilisation |
| Delhi (UT) | Very Low (<2) | Public hospitals dominate care |
Notably, higher utilisation does not automatically indicate better performance. On the contrary, in several large States it signals the absence of functional public healthcare, not the success of PM-JAY itself.
Claims Settlement Efficiency: The Silent Stress Test
While enrolment figures attract attention, claims settlement speed determines whether hospitals remain in the system.
| State | Median Settlement Range (Days) |
|---|---|
| Tamil Nadu | 18–25 |
| Kerala | 20–30 |
| Karnataka | 25–35 |
| Maharashtra | 35–50 |
| Rajasthan | 40–60 |
| Uttar Pradesh | 45–75 |
| Bihar | 60–90 |
| North-East (avg.) | 45–70 |
Importantly, State Health Agency capacity drives these delays far more than NHA’s central IT platform. Consequently, small and district-level hospitals exit PM-JAY first, and access shrinks quietly rather than collapsing publicly.
Hospital Empanelment Density: Coverage Versus Real Access
Insurance only matters when hospitals exist nearby. Therefore, this report measures empanelment density per 10 lakh population.
| State | Density Band |
|---|---|
| Tamil Nadu | High (6–8) |
| Kerala | High (6–8) |
| Karnataka | High (5–7) |
| Gujarat | Moderate (4–6) |
| Maharashtra | Moderate (4–6) |
| Uttar Pradesh | Low (2–4) |
| Bihar | Very Low (<2) |
| North-East | Very Low (<2) |
As a result, States with low density experience a coverage illusion—insurance cards exist, but hospitals do not.
ABDM: Identity Creation Without Full Clinical Integration
ABDM often features large Health ID numbers in official narratives. However, no State publishes verified daily clinical usage data. Accordingly, this analysis uses record-linkage depth as a conservative proxy.
| State | Health IDs with Any Records |
|---|---|
| Delhi | ~30–40% |
| Karnataka | ~20–30% |
| Tamil Nadu | ~15–20% |
| Maharashtra | ~12–18% |
| Uttar Pradesh | ~6–10% |
| Bihar | ~4–7% |
Crucially, ID creation reflects verified administrative progress, whereas clinical usefulness remains an analytical inference. Thus, ABDM currently functions as a registry-first system, not yet as a clinical backbone.
Composite State Performance: Re-verified Results
After cleaning inputs and removing false precision, the composite rankings remain stable.
| State | Composite Score (25) |
|---|---|
| Tamil Nadu | 18–19 |
| Kerala | 17–18 |
| Karnataka | 16–17 |
| Gujarat | 15–16 |
| Maharashtra | 14–15 |
| Rajasthan | 14–15 |
| Uttar Pradesh | 12–13 |
| Bihar | 7–9 |
| North-East (avg.) | 7–9 |
| Delhi (UT) | 15–16 |
Therefore, re-verification strengthens confidence in the direction of findings rather than weakening them.
What This Means for India
First, NHA has reduced catastrophic medical expenditure. However, it has not altered the underlying disease burden.
Second, PM-JAY performs best where public healthcare already exists. In contrast, high utilisation in weaker States signals distress.
Third, ABDM will deepen inequality unless assisted-access models expand rapidly.
Finally, policymakers must target future health funding State-wise rather than distribute it uniformly.
Sources and Context
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National Health Authority (official portal): https://nha.gov.in
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ABC Live internal reference:
Explained | How India Can Benefit from Integrated Pharmacology
https://abclive.in/2025/11/07/explained-how-india-can-benefit-from-integrated-pharmacology/
The Bottom Line
Ultimately, the National Health Authority acts as a powerful safety net and digital enabler. Nevertheless, it cannot replace State capacity, hospitals, or sustained public health spending.
In short, under a single national framework, India continues to operate multiple healthcare realities, shaped far more by State governance than by central design.
