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Data Analytics To Create Effective Preventive Healthcare System

It will be the major driver for an effective preventive healthcare system in India.

Recently, Maharashtra minister Rajesh Tope announced the setting up of 6,500 'Health Wellness Centres' in the state by the end of FY20. He further added that the major goal of these centres would be to make available preventive and persuasive health services to the common people in the state. An extremely progressive and bold proclamation that not only showcases policymakers commitment to move from sick care to preventive care but also reaffirms the challenges posed by Non-communicable or lifestyle-related diseases. The last decade has seen a seismic shift in the disease burden in India with more than 67 per cent of all hospitalizations due to NCDs’. The silver lining, if there is any, is in the fact that with timely identification and intervention, NCDs” can be prevented and many cases reversed. Early identification could lead to a much better diagnosis and more effective treatments.

This noble intent though is much easier said than achieved. To move the needle for 1.5 billion people, it requires a fundamental shift in healthcare policymaking, a deeper symbiotic relationship between public and private players and most significant adoption of new-age technology and tools. To evangelize the message of preventive health and reach out to the last mile, the role of technology becomes absolutely crucial. When we talk about all the technology enhancements at our disposal, preventive care relies on one aspect of it more than others and that is data analytics. Data Analytics lies absolutely at the core of building a robust and inclusive preventive care model. It provides the fulcrum around which all preventive care initiatives are tethered.

A successful preventive care system rests on three crucial pillars, identification, tracking and management. Identification relates to reducing health information asymmetry and early identification of health issues. Tracking takes care of ensuring the citizens are inculcating health habits and following healthy lifestyles whereas management takes care of people in need of health services and ensuring that such services are made available to them in a timely and seamless manner. If we observe, in each of these three aspects, Data Analytics plays a pivotal role. It can always be argued that for data analytics to be efficient enough at identifying patterns and eliminating false positives, we need to have the 4V’s (Velocity, Veracity, Volume & Variety) working harmoniously. Luckily, for us, the last decade also witnessed a positive move by many healthcare ecosystem players to embark on digital transformation. What that has enabled is moving, if not already; into a realm where the 4V’s may not be a big worry anymore. Systems are getting more and more interconnected; interoperability is a buzz word; some of the larger health systems are truly trying to adopt and the same time, data storage costs have come down significantly. Considering these shifts, it might be the right time to put a comprehensive data analytics mechanism in place and this could only augur well for the future.

Data Analytics could go a long way in identifying disease patterns, major health issues, population profiles, probability of disease outbreaks and so on. Each of these outcomes could then be used by various public and private agencies to not only fine-tune their policies and service offerings but also to build new and improved health and wellness initiatives. From providing vernacular medical content to reduce information asymmetry to providing discovery platforms for consumers to search for health experts, a plethora of services could be designed keeping in mind the preventive aspect and providing a consumer with the right information at the right time. On the public health side, data analytics could help identify health issues at infancy which in turn could aid the primary health centres to reach out to such individuals and put them on a preventive care plan. By delaying or reversing the onset of NCDs’, especially in a rural context, the burden on secondary and tertiary care centres could be significantly reduced and at the same time, for the and consumer it means a less worrisome life and less expense on medical care.

That data analytics is extremely crucial is beyond debate. It is now up to the policymakers to include understanding its criticality from day one and not leaving it for a later date.



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