Abstract
The Indian Health Sector is a highly fragmented with a state of art private hospital industry, an unregulated group of standalone practitioners, a partially defunct public sector and growing insurance sector. The out of pocket payments form the largest portion of health financing. Just like several other countries India has launched the nationally sponsored health insurance program – Pradhan Mantri Jan Aarogya Yojana to address the high out of pocket expenditure (OOPE). In such a scenario there is a need to understand the drivers of OOPE and how would they effect the OOPE in the future. This would enable the Government of India to make the right policy decisions in terms of which diseases (like Cardiovascular, kidney or cancer), which components (drugs, diagnostics or consultation fee) and which group of individuals (age, sex, location, education and socioeconomic level) will drive the OOPE. In this paper we use the nationally and state level representative National Sample Survey data to look current and past trend of Out of Pocket Expenditure in India and what are the strongest drivers of OOPE in the country. The study aims to use techniques of Machine Learning to make these predictions to build a robust model for the future.
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