The curious case of private health insurance bills | Number Theory
The difference in spending between patients with private health insurance and no insurance coverage at all is almost non-existent for non-hospitalisation.
The most important statistic from the latest National Statistics Office (NSO) report on health spending in India was health care inflation outpacing overall inflation between July 2017-June 2018 and January-December 2025. What really explains this higher-than-normal increase in health spending in India? Answering this question is crucial from both an institutional and household perspective. There are a large number of Indians who are still not covered by health insurance and even private and government health insurers have to pay for the cost of health care, which, ultimately feeds into higher insurance premium or fiscal costs. NSO data cannot be deemed conclusive, but it does offer some important hints.

Hospitalisation costs have increased faster than non-hospitalisation health spendingAverage spending per treated spell of ailment where hospitalisation was not required increased from ₹612 in 2017-18 to ₹878. This is an increase of 43.4%, similar to the 42.8% growth in overall Consumer Price Index (CPI) during this period. The cost in hospitalisation spending, increased by a much larger magnitude: 88% (for non-childbirth cases), well above the growth in overall inflation.
Hospitalisation costs vary depending on the kind of insurance coverage you haveThat hospitals charge higher when a patient has insurance coverage has been a running refrain in India for some time. The latest NSO report shows that this is a reality and not just perception. Average medical pending per hospitalisation spell in a private hospital if covered by private or employer-related health insurance (henceforth private insurance) is 1.9 times the cost without coverage. The ratio of private insurance and government-sponsored scheme is similar. The higher spending for private insurance covered patients is almost consistent across most major illnesses listed in the NSO report. What is interesting is that the difference in spending between patients with private health insurance and no insurance coverage at all is almost non-existent for non-hospitalisation.
The biggest source of higher spending in patients with private insurance coverage seems to be bundled costWhat really explains the higher cost of hospitalisation in patients with private insurance coverage compared to those with no insurance coverage? NSO data allows us to answer this question as it gives a break-up of various heads of hospitalisation costs. While almost every major treatment head is more expensive for private health insurance covered patients, the biggest mark-up is in the “package component”, which is the grouped cost for things such as medical procedures, operation theatre consumables, medicines, and doctor’s fees -- what can be described as the bundled cost of the treatment.- To be sure, patients do not pay the higher costs, at least immediatelyThis is the most important part of the entire debate. Despite private insurance coverage treatment cost being higher than no insurance coverage or government insurance coverage, patients pay only a fraction of it and end with lower OOP spending than not being covered with private insurance. However, what is perhaps not obvious is that the higher ticket size of treatment via private health insurance is bound to lead to an increase in premiums overtime barring which insurance companies cannot remain viable.
- InferenceIs there a collusion between insurance companies and private hospitals where both are increasing their revenues at the cost of the gullible consumer who is buying insurance in good faith? NSO data cannot answer this question. But this is an important question which ought to be asked by consumers, regulators and the government.
ABOUT THE AUTHORRoshan KishoreRoshan Kishore is the Data and Political Economy Editor at Hindustan Times. He heads the newsroom's data journalism team, which produces Number Theory, a daily data-driven feature for the print edition and the HT app. Number Theory uses data analysis and story-telling based on it to add value to the newsroom’s daily coverage by putting stories in a larger context on a range of issues, including politics, macroeconomy, markets, global affairs and climate. Under his leadership HT’s data journalism work has established itself as a niche product in Indian journalism and pushed the boundaries of marrying academic rigour with news sense and speed. Along with writing and editing data stories, he has also been writing a weekly political economy column called Terms of Trade for HT Premium. A trained economist with an MPhil degree from Jawaharlal Nehru University, Kishore has also been a visiting fellow at the Centre for Advanced Studies of India (CASI) at the University of Pennsylvania. Along with his journalistic work, his writings have also appeared in journals such as the Economic and Political Weekly and working papers for CASI and UNESCAP.Read More
ABOUT THE AUTHORAbhishek JhaAbhishek Jha is Assistant Editor-Data at Hindustan Times. He uses statistical programming to generate newsworthy insights from large datasets. He is part of the team that produces Number Theory, a daily data story feature of the paper’s print edition. Since March 2024, he has been writing Weather Bee, a weekly column for the Hindustan Times website. He is a chemical engineer by training, who specialises in stories related to weather, climate, and the environment. Jha has been at HT since 2018, where he offers data-driven perspective and analysis on politics, environment, weather, climate, economy and society. His work includes data coverage of elections in India and abroad, including the 2019 and 2024 Lok Sabha elections; the disasters and extreme weather resulting from changing climate, such as floods, droughts, heat waves, cold waves, and dwindling snow cap in the Himalayas; the factors that drive poor air quality in northern India; the changing patterns of land use; the Covid-19 pandemic and its impact on labour market conditions; the changing pattern of consumer spending seen in the new consumer spending surveys; and social norms seen in the surveys such as the National Family Health Survey.Read More

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