Role Of Data & Analytics In Transforming Indian Healthcare
Prashanth has over 22 years of data and technology leadership experience, including senior leadership roles at United Health Care and Optum, most recently as SVP of Data and Analytics for United Health Care Operations. He has deep experience leading data management, data science, analytics and AI/ML functions in the health insurance space.
The use of data and analytics in Indian healthcare has been limited. The limited number of structured and trusted datasets, typically available in western countries, has throttled the development of healthcare analytics in India. This in turn, has had limiting effects on the Indian healthcare industry and in turn, population health of the worlds soon to be most populous country.
However, now with the advent of Identity management (Aadhar), verification (digi locker and other applications), universal healthcare programs (Ayushman Bharath and similar), Electronic health records (EHR and EMR), digital finTech (UPI, bank accounts, and credit tracking) the stage is set for a very robust ecosystem of healthcare analytic products and services to develop, and in turn have a transformational effect on the overall health profile of the Indian population.
How does data and analytics play a core role in what traditionally has been the domain of medical practitioners whose core skills are the ability to diagnose conditions, intervene surgically and prescribe medication, you wonder?
1.Healthcare Costs & Insurance Coverage: The cost of healthcare is increasing everyday, and with that, comes the need for insurance. India has some of the highest rates of out of pocket expenditures (out of pocket healthcare expenditure as a percentage of current healthcare expenditure is 55 percent plus), as compared to countries with universal healthcare (15-20 percent)*. Most private health insurance in India is indemnity based, with limited coverage (fixed amounts of coverage with low premiums). These plans do not provide coverage for most preventive healthcare like out patient’s consultations, specialist referrals, tests and diagnostics, and post hospitalization recuperation. This drives a chicken and egg situation, where people are reluctant to seek help early, through doctor visits and diagnostics, due to the high out of pocket costs involved. Hospitalization, which is partly covered by insurance, is a too-late, expensive affair. All this, drives up the overall per capita cost of healthcare, with poor clinical outcomes, and very low patient satisfaction.
Going ahead, improved availability of population health data EHR data, longitudinal patient data with understanding of pre-existing conditions and predilections will improve the ability of insurers to assess risk and customize insurance coverage appropriately. Data availability and the better assessment of risk will also incent insurers to assume full risk for medical cost. This in turn, will also drive the focus of insurers on preventive health incenting their coverage of doctor and specialist referrals, tests and diagnostics in order to detect, treat early and minimize hospitalizations. They will also increase vigilance on care providers (doctors and hospitals), encouraging their members to consult care providers who provide the best clinical outcomes at the best cost possible.
2.Fraud Analytics & Payment Integrity: Fraudulent claims cost upwards of Rs.800 crore annually per industry estimates Due to the lack of data, and robust tracking, this could just be the tip of the iceberg. Inspite of a annual growth rate of 10.2 percent, healthcare insurers in India have been unable to turn an underwriting profit. The lack of good quality population health data as explained in point 1, and significant healthcare fraud, waste and abuse hamper the growth of better coverage and policies. Today, investigating fraud, waste and abuse is a manual activity for many insurance companies. Due to the high costs involved, several smaller, but very frequent frauds go undetected. Fraud could occur at the insurance application stage (identity, history, pre-existing conditions, and so on), or during eligibility check (employment status, medical condition and severity etc), or during claim (false representation of services provided). Fraud drives up the cost of healthcare overall. The ability to apply machine learning algorithms at scale to detect aberrant claim patterns will bring about a scale change in the detection and investigation of fraud and reduce administration costs, and improve access to coverage.
3.Clinical Analytics: Including disease prediction, provider steerage, provider performance, therapy and medication compliance. The ability to track an individuals’ longitudinal (over time) health or medical history is invaluable to doctors and in a de-identified form, to population health planners. One can get a better insight into a persons’ predilection for certain conditions, understand comorbidities, and tailor treatment and medication appropriately. It can also help plan and prevent onset of disease.
With the push to establish uniform standards for Electronic Medical Records (EMR) from the government, the ability to share records seamlessly across doctors and facilities (interoperability) and interpret these records quickly and easily (common coding standards) will come into play.
This will enable many clinical innovations that will drive down medical cost and improve patient outcomes
•Condition Prediction: Understanding disease progression at a population level, will allow better prediction enabling better interventions. This is especially important as every patient is unique the most ill and expensive, the ones with several comorbidities.
•Steerage: Ability to steer patients to the best performing doctors and facilities for that particular condition, thereby driving optimal treatment, cost and outcomes.
•Therapy & Medication:Understanding the effectiveness of therapy and treatment, as well as tracking compliance.
These are just three ways in which better data availability and improved interopera bility across the healthcare ecosystem can drive transformational impact at population scale in India. Reduction in per capita medical cost, improved clinical quality and outcomes, and overall patient experience will be the yardsticks by which this transformation can be measured. As India progresses towards a remarkable leadership position in digital transactions, the foundational blocks of its digital infrastructure and the foresight to extend those into healthcare, will drive significant opportunities for both startups as well as professionals in the field of healthcare analytics.
The use of data and analytics in Indian healthcare has been limited. The limited number of structured and trusted datasets, typically available in western countries, has throttled the development of healthcare analytics in India. This in turn, has had limiting effects on the Indian healthcare industry and in turn, population health of the worlds soon to be most populous country.
However, now with the advent of Identity management (Aadhar), verification (digi locker and other applications), universal healthcare programs (Ayushman Bharath and similar), Electronic health records (EHR and EMR), digital finTech (UPI, bank accounts, and credit tracking) the stage is set for a very robust ecosystem of healthcare analytic products and services to develop, and in turn have a transformational effect on the overall health profile of the Indian population.
How does data and analytics play a core role in what traditionally has been the domain of medical practitioners whose core skills are the ability to diagnose conditions, intervene surgically and prescribe medication, you wonder?
1.Healthcare Costs & Insurance Coverage: The cost of healthcare is increasing everyday, and with that, comes the need for insurance. India has some of the highest rates of out of pocket expenditures (out of pocket healthcare expenditure as a percentage of current healthcare expenditure is 55 percent plus), as compared to countries with universal healthcare (15-20 percent)*. Most private health insurance in India is indemnity based, with limited coverage (fixed amounts of coverage with low premiums). These plans do not provide coverage for most preventive healthcare like out patient’s consultations, specialist referrals, tests and diagnostics, and post hospitalization recuperation. This drives a chicken and egg situation, where people are reluctant to seek help early, through doctor visits and diagnostics, due to the high out of pocket costs involved. Hospitalization, which is partly covered by insurance, is a too-late, expensive affair. All this, drives up the overall per capita cost of healthcare, with poor clinical outcomes, and very low patient satisfaction.
Going ahead, improved availability of population health data, EHR data, longitudinal patient data with understanding of pre-existing conditions & predilections will improve the ability of insurers to assess risk & customize insurance coverage appropriately
Going ahead, improved availability of population health data EHR data, longitudinal patient data with understanding of pre-existing conditions and predilections will improve the ability of insurers to assess risk and customize insurance coverage appropriately. Data availability and the better assessment of risk will also incent insurers to assume full risk for medical cost. This in turn, will also drive the focus of insurers on preventive health incenting their coverage of doctor and specialist referrals, tests and diagnostics in order to detect, treat early and minimize hospitalizations. They will also increase vigilance on care providers (doctors and hospitals), encouraging their members to consult care providers who provide the best clinical outcomes at the best cost possible.
2.Fraud Analytics & Payment Integrity: Fraudulent claims cost upwards of Rs.800 crore annually per industry estimates Due to the lack of data, and robust tracking, this could just be the tip of the iceberg. Inspite of a annual growth rate of 10.2 percent, healthcare insurers in India have been unable to turn an underwriting profit. The lack of good quality population health data as explained in point 1, and significant healthcare fraud, waste and abuse hamper the growth of better coverage and policies. Today, investigating fraud, waste and abuse is a manual activity for many insurance companies. Due to the high costs involved, several smaller, but very frequent frauds go undetected. Fraud could occur at the insurance application stage (identity, history, pre-existing conditions, and so on), or during eligibility check (employment status, medical condition and severity etc), or during claim (false representation of services provided). Fraud drives up the cost of healthcare overall. The ability to apply machine learning algorithms at scale to detect aberrant claim patterns will bring about a scale change in the detection and investigation of fraud and reduce administration costs, and improve access to coverage.
3.Clinical Analytics: Including disease prediction, provider steerage, provider performance, therapy and medication compliance. The ability to track an individuals’ longitudinal (over time) health or medical history is invaluable to doctors and in a de-identified form, to population health planners. One can get a better insight into a persons’ predilection for certain conditions, understand comorbidities, and tailor treatment and medication appropriately. It can also help plan and prevent onset of disease.
With the push to establish uniform standards for Electronic Medical Records (EMR) from the government, the ability to share records seamlessly across doctors and facilities (interoperability) and interpret these records quickly and easily (common coding standards) will come into play.
This will enable many clinical innovations that will drive down medical cost and improve patient outcomes
•Condition Prediction: Understanding disease progression at a population level, will allow better prediction enabling better interventions. This is especially important as every patient is unique the most ill and expensive, the ones with several comorbidities.
•Steerage: Ability to steer patients to the best performing doctors and facilities for that particular condition, thereby driving optimal treatment, cost and outcomes.
•Therapy & Medication:Understanding the effectiveness of therapy and treatment, as well as tracking compliance.
These are just three ways in which better data availability and improved interopera bility across the healthcare ecosystem can drive transformational impact at population scale in India. Reduction in per capita medical cost, improved clinical quality and outcomes, and overall patient experience will be the yardsticks by which this transformation can be measured. As India progresses towards a remarkable leadership position in digital transactions, the foundational blocks of its digital infrastructure and the foresight to extend those into healthcare, will drive significant opportunities for both startups as well as professionals in the field of healthcare analytics.
