India’s health insurance sector has come a long way in offering financial protection during medical emergencies. But the underlying model remains largely reactive—someone falls ill, gets hospitalised, files a claim, and the insurer processes the payout. In FY2023–24, this reactive approach translated into over ₹83,000 crore disbursed in claims, underscoring both the cost burden of inpatient care (IPD) and the urgent need to explore more sustainable approaches.
With rising healthcare costs and evolving patient expectations, health insurers are at a crossroads. The next phase of evolution isn’t just about digitising claims or enhancing fraud detection—it’s about using data and technology to anticipate health needs and intervene earlier. Artificial Intelligence (AI) is fast becoming a foundational tool in reshaping healthcare strategies.
Worldwide, the healthcare AI market is projected to grow significantly, increasing from an estimated $11 billion in 2021 to nearly $187 billion by 2030. But beyond the numbers lies a strategic opportunity for Indian insurers: to reposition themselves not just as financial protectors, but as long-term partners in health. This shift can begin by reimagining the IPD journey before hospitalisation, during care, and after discharge as a continuum where AI helps predict, personalise, and prevent.
Pre-hospitalisation: Predict, prioritise, prevent
The first opportunity lies in spotting health risks before they escalate into hospital admissions. For insurers covering millions of lives, this kind of foresight has always been difficult; manual monitoring is unscalable, and traditional care management often focuses only on chronic conditions.
AI changes this equation. By analysing a combination of claims history, demographics, and health patterns, AI models can forecast which members are most likely to require hospitalisation in the next 6 to 12 months. Instead of blanket screenings, insurers can focus their attention on a targeted set of high-risk individuals.
India’s broader digital health ecosystem is also moving in this direction. Under the Ayushman Bharat Digital Mission, over 500 million patient records are being digitised to build an interoperable national health data platform. This digital foundation will make it easier for AI systems to generate early warnings and risk flags with far greater accuracy.
What makes this approach powerful is its adaptability. AI can continuously learn and improve. The result is a smarter allocation of resources, timely diagnostics, preventive care, or personalised check-ins delivered precisely to those who need it most. Not only does this reduce the likelihood of hospitalisation, but it also marks a meaningful step toward shifting the focus from treatment to prevention.
During hospitalisation: Informing smart care decisions
While the hospitalisation phase is often seen as the domain of doctors, insurers still have an influential role to play, particularly when it comes to guiding decisions through data.
AI can improve this phase in two important ways. First, it enables context-aware pre-authorisations by evaluating a member’s health history and clinical profile to support quicker, better-aligned treatment approvals. This reduces friction for patients while ensuring care remains evidence-based.
Second, AI can drive length of stay management by analysing historical data to identify cases with similar clinical profiles. This allows for evidence-based benchmarks on expected hospitalisation duration. When a patient’s stay exceeds this estimate, a clinical justification is required, helping to identify and reduce unnecessary extensions often driven by non-clinical factors such as billing. By doing so, AI helps optimise resource use, minimise waste and abuse, and lower healthcare costs—while still respecting clinical judgment and enhancing overall care quality.
These interventions don’t intrude into medical decision-making but complement it. By providing a clearer picture of what works and where, insurers can influence care quality while keeping costs in check. It’s a more collaborative role, supporting rather than simply auditing the hospital journey.
Post-Hospitalisation: Preventing repeat admissions
The post care management process continues well beyond a patient’s discharge from the hospital. In fact, some of the most preventable costs in IPD stem from readmissions due to poor follow-up or missed warning signs.
Here again, AI brings in precision. By assessing recovery trends, medication adherence, and the presence of comorbidities, AI systems can flag members who are at higher risk of being readmitted. This allows insurers to proactively offer recovery support, whether in the form of virtual check-ins, remote monitoring, or home healthcare coordination.
This reduces costs associated with repeat admissions and enhances patient satisfaction and long-term outcomes. It also helps close the often-neglected gap between treatment and recovery, strengthening the insurer’s value proposition as a holistic health partner.
The way forward: From reaction to prevention
AI is already being used in some areas of health insurance, fraud detection, faster underwriting, and chatbots. But its true potential lies in preventive, intelligent care management, especially in high-cost IPD scenarios.
For insurers, the true challenge lies not in the technology itself, but in the necessary shift in mindset. Moving from a claims-first culture to a prevention-first approach requires vision, collaboration, and trust in the value of data.
But those who make this shift will redefine their role in India’s healthcare ecosystem. They won’t just settle claims. They’ll help avoid them and, in doing so, improve both business outcomes and the lives of the people they serve.