Daily Archives: October 16, 2017

Can Big Data Help Reduce India’s Burden of Healthcare Costs?

October 16, 2017

healthcare-options

India remains high on the list of medical tourists based on the high quality of healthcare available at comparatively lower costs when compared to the costs in more developed nations. In fact, medical tourism is growing at a compounded rate of over 20% y-o-y leading to a flourishing industry that is expected to touch US$ 6 billion by 2018. However the proverbial grass on the other side of the fence is not greener. It is no secret that healthcare costs for an average Indian family has seen significant increase year-on-year basis 10 year data collated by the National Sample Survey Organization that revealed a significant increase in expenses for public health services for both urban and rural patients between 2010-14.

Between 2004 and 2014, for example, the average medical expenditure per hospitalisation for urban patients increased by about 176% across both urban and rural patients. During the same period, India’s GDP per capita, based on purchasing power parity grew by 121%. While outpatient (OP) care increased by over 100%, inpatient care expenditure went up by an almost 300% increase during this period. With limited insurance coverage, a majority of rural and urban households depended on ‘household income / savings’ to meet healthcare costs while almost 21% of families had to rely on ‘borrowings’. Data also reveals that healthcare access in India is affected with 70:70 paradox – 70 per cent of healthcare expenses are incurred by people from their pockets, of which 70 per cent is spent on medicines alone.

The situation fares no better when it comes to the private healthcare burden on the population. Data reveals that the use of more expensive private healthcare is increasing in comparison to the use of public healthcare systems, which is reducing across both urban and rural areas. Here again the issue is that of consumers paying top dollar to healthcare service providers. While much of this is borne by insurance companies, a significant portion continues to be out-of-pocket expenses borne by the consumer. Claimants from India’s top six cities and metros (comprising of around 5% of the population) account for a disproportionately high (over 20%) health-insurance claims, receiving over 25% of all health insurance pay-outs, according to the Insurance Information Bureau of India.

Bringing in big data to make big changes

The challenges facing both insurance companies, public health institutions and policy makers is to improve financial performance, increase cost effectiveness while improving overall health indicators, and boosting quality of care. The increasing need is to address them all. In such a situation, the question that arises is, can the application of big data and analytics address these needs? Here’s how I believe it can.

Insurance companies and other stakeholders need to invest in predictive, statistical data modelling to more accurately determine ‘future possibilities’ basis ‘past happenings’. “Models” can be created to determine future variables that can ensure that stakeholders, whether insurance firms or healthcare service providers can get more accurate predictions for the future. The other is to generate greater streams of ‘real-time’ data that would be more useful in generating even better predictive modelling.

Integrating the service provider domain with wearable technology is one way to do this. Companies such as Goqii, a wearable start-up in India is already working with insurance service providers to enable this. Wearables can monitor a person’s habits and provide ongoing assessment of their lifestyle and activity levels. Internationally, many insurers are offering services based on the use of these devices. The great thing is that if consumers are willing to share data, consumers can benefit with cheaper insurance premiums by using these devices to better track and improve unhealthy behaviours. Insurance companies are also leveraging such learnings to increasingly move away from the concept of ‘one policy, one price’. As a result, the price of a health cover has become a complex variable, depending on where you live, what hospital you plan to use and your age.

Insurance companies on their part can share this data with public and private healthcare service providers who in turn can also use this data to develop better products and services, as well as improve their marketing RoI.

On the other side, healthcare service providers can collate data from patients and figure out how much of the problems are due to genetic profiling and how much from behaviour using behavioural analytics – thereby drawing an empirical distinction between when ill health is caused by bad choices, and when it’s down to genetic factors. Such learnings gleaned through the application of Big Data will benefit the entire ecosystem including consumers through more tailored products and services and similarly pharma companies and insurance companies as well.

Another use of Big Data within the healthcare ecosystem is in marketing. By gaining a more complete understanding of a customer by analysing all available data, service providers can become more efficient in offering products and services tailored to specific needs. Increasingly this deeper understanding is being achieved through sentiment analysis of customer feedback and even social media activity.

Finding and reducing inefficiencies

In India one out of every 10 insurance claims is reported to be a fraud impacting insurance companies and consumers both. Reducing fraud not only saves the service provider huge sums of money but also has the potential to reduce premiums. For instance Teradata’s customer, The Centers for Medicare & Medicaid Services (CMS) (a part of the Department of Health and Human Services), covering 100 million Americans through Medicare, the Children’s Health Insurance Program and the healthcare Insurance Program leveraged data analytics to obtain a single, nationwide view of approximately 4.5 million claims and $1.3 billion that was paid out each day. This helped in creating a consistent, reliable, secure, enterprise-wide view of claims data that was available to all users which in turn helped the organization deliver quality healthcare more effectively, and quickly identify fraud, waste and abuse.

By using analytics, Business leaders and clinicians are able to have make decisions about patient care and operational efficiency that impact lives and save money. Combining nurse training with mobile device technology designed to help clinicians recognize early warning signs of high-risk conditions like sepsis and congestive heart failure, Christus Health System , a Teradata customer in Texas reduced Medicare costs by $1.31 million among hospitalized patients and $2.2 million among long-term post-acute care patients. This may seem like an anomaly but hospitals can actually save money by discharging patients faster, thereby improving patient satisfaction.

Indian hospitals, especially those operating at capacity such as India’s premier All India Institute of Medical Sciences or Safdarjung hospital in Delhi could similarly analyze multiple parameters and reports on key data points to plan prescriptions, follow-up visits, wheelchair transportation and room cleaning to reduce lag time between patient treatment steps. Reducing the length of hospital stays by even just a few hours allows the hospital to better serve patients who are waiting for care. Many patients could be better served through the provision of appropriate preventive care thereby reducing healthcare costs and overall hospital admissions since early treatment can decrease the severity of an illness. 

Eliminate unnecessary testing

Some private hospitals, in a bid to increase revenue, push patients to undergo excessive number of tests – some of which are not even necessary and can also be dangerous. In addition, insurance companies and patients bear the financial brunt of this. Insurance service providers can leverage big data to understand optimum testing patterns across diseases and medical conditions and get hospitals to modify their standards of care and reduce the number of costly tests pushed onto unsuspecting patients.

Conclusion

Big data is not a panacea of all that ails the medical fraternity. However, used wisely, it is a solution that can drive significant benefits for the industry in its entirety, benefiting insurance companies, the public healthcare system, private hospitals by pointing anomalies, improving services and standards, reducing inefficiencies and finally by contributing to make services more affordable for the common man.


Rajesh Shewani

Rajesh Shewani, Head, Technology and Solution Architecture, Teradata India

Rajesh Shewani heads Technology and Solutions at Teradata India. He comes with close to two decades of experience in the areas of Data Management, Advanced Analytics, Solution Architecture, Enterprise data warehousing and Business Intelligence to name a few.

At Teradata he is responsible for leading a team of experienced Business Analytics consultants, data scientists and solution architects focusing on Teradata Data Analytics solutions. Rajesh has worked in various areas of information management across different industries and functional domains, advising customers on devising corporate performance management and business analytics strategies that will help them achieve differential advantage.

Prior to Teradata, Rajesh was with IBM for over 14 years and performed various roles in technology leadership, architecture and consulting. In his most recent role at IBM he was Country Manager for technical sales for IBM Business Analytics portfolio that included Cognos, SPSS, OpenPages and Algorithmics, leading a team of analytics functional and technical sales architects. Rajesh is certified in technologies such as Cognos BI & FPM, SOA, WebSphere Application Server and DB2.

He holds a Masters in Business Marketing & Information Management from Narsee Monjee Institute of Management Studies.