Introduction

India is aging at a pace that will test the capacity of its health and economic systems. In 2022, 149 million Indians were aged 60 and above, about 10.5% of the population; and by 2050, there will be 347 million, or 20.8%.

The elderly population is currently growing at a decadal rate of 41%, faster than any other age group. Older adults will likely outnumber children aged 0–15 by 2046. After decades of planning for a young and growing population, India must now prepare for rapid aging. The country must also ensure that people live these years in good health, with financial security, independence, and dignity.

Population aging in India currently appears as a future welfare burden, but it is an urgent economic policy challenge. The case for investing in healthy aging rests upon proactive systems. These systems protect households, sustain labor markets, improve productivity, and support continued contribution in later life. They also challenge the assumption that aging inevitably leads to dependency.

These economic gains will largely come through two complementary pathways: the reduction of losses from poor health and disability in later life, and the creation of new value through support that helps older adults remain healthy, active, and independent for longer.

Reducing the economic losses of unhealthy aging

The first gain from investing in healthy aging comes from lowering the costs of poor health in later life. Despite advances in financial protection and health assurance, out-of-pocket expenditure continues to account for 43.4% of total health expenditure in India. This burden falls heavily on the elderly. An older adult spends roughly 17.4% of total household consumption on healthcare, a share that rises to nearly a quarter (24.8%) among the poorest of older adults.

For many families, a serious illness in old age can deplete savings, force the sale of productive assets, or push entire households into debt. Families divert savings that could support education or enterprise investment to medical expenses instead. The economic consequences extend well beyond the health sector. They affect household consumption, generational asset creation, and long-term financial security.

Much of this burden is preventable. Investments in preventive care, early diagnosis, and effective management of chronic conditions reduce the likelihood of expensive hospitalizations and complications, generating savings for both households and the health system.

Healthy aging is about more than just extending lifespan. It is about increasing health-span, the years people live free from significant illness, disability, or functional decline. Ultimately, it is also about enhancing joy-span, which includes purpose, social connection, autonomy, and emotional well-being.

Healthier, more independent years mean lower treatment and long-term care costs for both families and the health system.

Creating economic value through healthy aging

The economic case extends beyond healthcare cost reductions. As the population ages, demand for informal, unpaid caregiving rises. Women bear most of this burden and often leave paid work to provide care. Globally, one year away from paid work leaves women with 42% less in retirement savings, rising to 56% after five years of interrupted employment.

Healthy aging also expands the productive contribution of older adults. Traditional assumptions often portray older people as dependents. Yet, nearly 40% of Indians aged 60 years and above remain economically active, particularly through agriculture, self-employment, household enterprises, and the informal economy. Older adults also take on childcare and household management, work that is essential to families but has historically gone unpaid. These forms of work generate substantial economic value, despite their limited recognition in national accounts.

Preserving functional capacity and independence helps older adults and their families to remain active participants in the economy.

Why the opportunity is greatest now

India is younger than many countries that already face the fiscal pressures of population aging. In 2021, the country had roughly 16 older adults for every 100 working-age people. This demographic window gives India fiscal and institutional space to invest in healthy aging before these pressures intensify. Early investments in the aging-friendly interventions can compound over time.

Vietnam offers a close parallel. Its intergenerational self-help clubs, launched in 2006, began as older people’s associations before they expanded to include the wider community. These voluntary village-level groups have 50–70 members and combine health monitoring, livelihoods, and social participation. These clubs focus on older people, women, and those facing economic hardship. This award-winning model costs little and transfers easily because it uses existing community structures.

Fortunately, India does not need to build an entirely new system. The Ayushman Arogya Mandir network, community health workers, and digital health platforms like eSanjeevani already support preventive, continuous, and community-based care.

Older adults also benefit from dedicated government programs such as Atal Vayo Abhyuday Yojana, Elderline, Senior Able Citizens for Re-Employment in Dignity (SACRED), and Seniorcare Ageing Growth Engine (SAGE). In addition, the expanded Ayushman Bharat coverage now supports citizens aged 70 and above.

The foundations already exist. The challenge is to integrate existing health, aging, and digital systems into a coherent strategy that prioritizes prevention, functional ability, and healthy aging across the life course.

The choice: Spend now or pay more later

India will incur the costs of population aging regardless of its policy choices. The real question is whether it pays those costs proactively through prevention, early intervention, and community-based care, or reactively through hospitalization, disability, and crisis-driven spending.

Healthy aging is often framed as a social responsibility, and it most definitely is. But it is also an economic strategy. For a country entering one of its largest demographic transitions, adding years to life is no longer enough. The greater challenge and opportunity lie in adding joy, dignity, social participation, and care to those years. Whether longer lives weigh on the economy or add to it will depend on the choices India makes now.

In a rural health catchment in northern India, an Auxiliary Nurse Midwife (ANM) worker updates a pregnant woman’s details on the Reproductive and Child Health (RCH) portal after a household visit. Later that day, the same information must be entered again into other systems for facility reporting and program tracking. Hours that could be spent on follow-up care are instead spent across platforms that were never designed to work together. 

This operational reality rarely comes up in conversations about artificial intelligence (AI) in healthcare with an adequate and workable solution. Yet it is precisely at the point of data generation and entry that the effectiveness of future AI systems takes shape. The quality of the data that enters these systems, and the way it moves between them, influences what AI can do later. 

India has invested heavily in digital health infrastructure. Across maternal and child health, tuberculosis (TB), human immunodeficiency virus (HIV), immunization, and disease surveillance, these platforms capture vast amounts of data every day. At the same time, there is growing momentum to deploy AI, from clinical decision support to AI-assisted diagnostics.  

Yet, AI systems are only as reliable as the data environments they are built on. For an Accredited Social Health Activist (ASHA), the information entered during a household visit does not necessarily remain in one place. A pregnant woman’s information may be recorded separately across maternal tracking, facility reporting, and immunization workflows. Small inconsistencies, such as spelling variations, incomplete histories, and missing identifiers, make it hard to build a longitudinal picture of care. AI does not make these gaps disappear, and it often makes them harder to detect. The real challenge lies in the fragmented structure of the systems that AI tools enter, rather than the absence of AI tools themselves.  

The structure shapes the outcome 

India’s health data architecture evolved program by program: HMIS for facility reporting, the RCH portal for maternal and child tracking, Ni-kshay for TB management, and IHIP for disease surveillance. Each system was built independently to meet specific operational needs. The result is parallel platforms with distinct workflows, identifiers, and requirements. More recently, the JANANI platform has sought to address some of these gaps by consolidating maternal and child records into a single longitudinal system, though its rollout is still at an early stage and will take time to stabilize. 

A patient’s TB history, maternal records, immunization status, and chronic conditions may sit in disconnected systems, which limits an integrated view of care across conditions. This is more than a technical issue. This reflects the way public health programs evolved in institutional silos, and AI systems built on top inherit those same gaps. 

Why this matters for AI 

AI learns patterns from the data it receives, but fragmented systems often provide only partial snapshots rather than a connected continuum of care. A predictive model may miss a high-risk patient because the system around it never captured an overview of that patient’s journey, even when the algorithm itself is sound. AI outputs can appear precise and credible while relying on incomplete data. As a result, AI across fragmented ecosystems may reinforce blind spots rather than resolve them. 

Why interoperability alone may not be enough 

Most efforts to date have focused on interoperability, which enables systems to exchange data. India’s Ayushman Bharat Digital Mission has invested in shared standards, digital IDs, and registries. These are necessary foundations; however,  they do not change how data is generated on the ground.  

Frontline workers still navigate multiple platforms, and they enter the same information repeatedly because systems stay organized around separate mandates rather than a unified patient journey.  

Interoperability helps systems talk to each other after the fact, but it does not stop health workers from entering the same data repeatedly at the source. Fragmentation begins at the point of data capture itself. 

The missing layer 

Between frontline data collection and system-level intelligence lies a less-discussed layer that determines how information moves across the continuum of care. 

Today’s platforms reflect program boundaries, rather than care continuity, so integration often happens only after data has already entered the ecosystem in fragmented form. Backend fixes alone cannot always fully address these issues later. 

A better approach may be to redesign workflows at the point of entry so that frontline workers capture information once through shared interfaces. This data can then flow into program-specific systems as needed. This would not eliminate program-specific requirements but could reduce duplication and build a stronger foundation for AI that depends on connected, longitudinal data.  

For the ASHA worker, reducing the burden of repeated data entry can create more space for work beyond the screen, such as patient care, follow-up, and community engagement. MSC is testing this approach in Bihar, where ASHA incentive claims benefit from streamlined processes through the removal of redundant data entry, and in Odisha, where system redesign supports improvements in maternal and child health indicators.  

Through its work with ICMR–National Institute for Research in Digital Health (ICMR-NIRDH), MSC supports the development of national guidance instruments that directly address the governance dimensions of this challenge. These instruments include frameworks for data curation, data sharing, and interoperability standards for AI systems in public health. A multi-stakeholder Technical Working Group convenes this work and translates the structural data challenges described here into actionable policy direction at the national level. 

India has built strong digital infrastructure and now seeks to add intelligence on top. Yet, intelligence is only as coherent as the systems beneath it.  

The real question may be: can healthcare AI ever be truly integrated if the health system itself still operates in silos?