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Madhu Aravind

Madhu Aravind is the CEO of Searchlight Health, a Bangalore-based data analytics company that works with hospitals and health insurers and is a subsidiary of Piramal Enterprises Limited.

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Role of Data Analytics in Democratizing Health Insurance in India

Health insurance has been a part of the Indian landscape since the 80s, but its penetration is still fairly limited. Around 2% of the population have individual policies, 3% have corporate policies and 20% are covered through various government schemes.

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As the second most populous nation and one of the fastest-growing economies in the world, India is tasked with meeting the burgeoning expectations of people in providing basic services, such as infrastructure, housing, healthcare, transportation and job opportunities. With the population expanding by almost 450 million people in the last 25 years, the burden of service delivery has increased, especially in the healthcare sector. With increasing healthcare costs and unaffordability of quality healthcare, we need a holistic approach to tackling this issue.

Health insurance has been a part of the Indian landscape since the 80s, but its penetration is still fairly limited. Around 2% of the population have individual policies, 3% have corporate policies and 20% are covered through various government schemes. Amongst the middle class, less than 1% has access to any form of health insurance. Even amongst those that have health insurance, most policies do not have catastrophic coverage, making these policies inadequate. Though the health insurance business has been increasing 20% year on year, the number of people covered under individual cover has remained stagnant in recent years.

Besides logistical issues like distribution, the main reasons behind low penetration have been high cost for meaningful coverage and restrictions and inadequate protections offered by most products. For example, a policy for Rs. 5 lakhs sum insured costs around Rs. 15-20K for a family. Additionally, it comes with a whole slew of restrictions relating to pre-existing conditions, room rents, etc. It is also inadequate in covering many catastrophic illnesses. If insurers can tackle many of these challenges, they have the opportunity to increase coverage from the current 25% to more than 75%.

The first step in tackling these challenges is to abandon the traditional approach of “one size fits all” to product offerings. Insurers can create various products that have lower sum insured for routine diseases and higher sum insured for diseases with high treatment cost and low probability of occurrence, based on customer preferences. Insurers can also factor in pre-existing conditions to make products that are fairer on its customers, removing the tough restrictions that currently exist. This can lead to the creation of policies that vary from a “Scooty” to a “BMW” to cater to the highly varied needs of the population.

To enable this kind of product innovation, insurers need a clear understanding of morbidity patterns based on age/gender/city and models to predict the estimated cost for treatment and disease management. Historically, healthcare related data from electronic health records, claims, pharmacies, and registries were not usable at scale as they were non standard and unstructured. With the progress that has been made in NLP, image processing and neural networks, all of this has now become a very real possibility. Since insurers typically only have a small slice of their customer’s data, they would need to partner with entities which can help with both longitudinal data and also the expertise that is needed to build machine learning solutions that can create these models.

As adoption of health insurance grows, making the business sustainable requires aligning the incentives of both the consumer and the physician to manage “health” as opposed to “disease”. As far as the consumer is concerned, most “wellness” programs have not produced results as traditionally it has been difficult to convert it into relevant steps that can be easily implemented and monitored. With enough longitudinal medical records, latest machine learning algorithms can accurately forecast and monitor the health of an individual based on relevant data available including those that can be measured by smart watches. If these actions can translate to lower premiums and better health for the individual, then this can be a win win for everyone.

There is no doubt that the Health insurance market in India is poised for significant growth in the next decade and data analytics can provide some of the critical pieces that are needed to ensure that the vast majority of the population gets the right type of healthcare access and protection.

Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house


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