Annual healthcare spending in the US is estimated to be around $3 Trillion, nearly 18% of the country’s GDP. According to a report by the Institute of Medicine, the country lost $750 billion due to inefficiencies in the system. The report states that the primary areas of wasteful expenditure are unnecessary services, improper care delivery, excess administrative costs, inflated prices, prevention failures and fraud. While there are multiple measures being taken to control these inefficiencies, healthcare analytics is certainly one of the cogs in the wheel.
Healthcare industry so far has not utilized analytics the way other data heavy industries such as retail and banking have. This could partly be due to legacy systems, unavailability of data, data privacy issues and lack of incentives. Given that the data generated and collected through the introduction of electronic health records, health insurance exchanges, and social media portals is on a rise, the healthcare analytics sector is ripe for change. Moreover, the move from pay-for-service model to pay-for-performance, focus on wellness to prevention and universal coverage clearly makes a strong case to implement analytics in the healthcare space.
Some of the use cases where healthcare analytics can deliver value are as follows:
- Fraud Analysis: Large amounts of claims data can be analysed to using fraud detection models. Predictive models can detect suspicious claims by providers which can then be further investigated.
- Incentive Design Analytics: Customer records can be analysed to predict health issues before hand. The payers then can promote preventive measures by linking premiums to the use of such preventive measures
- Evidence-Based Medicine: Doctors can use EMR, EHR, financial, operational, clinical, and genomic data. This will eventually decrease the cost by reducing readmission rates, wrong diagnoses and efficient care.
- Research & Development: R&D can benefit from analysing clinical data which can provide insights to accurately design trials which in turn would provide data points to efficiently design clinical trials, leaner R&D pipeline, improve time to market drugs hence reducing costs in the drug discovery process.
Although big data offers a very promising proposition to tackle the rising healthcare costs, there are certain challenges that need to be addressed. One of the aspects that need special attention is patient data security and privacy. With a large number of regulations in place, data analytics applications would also be required to comply with the rules of the game. Additionally, all the stakeholders in the healthcare space will have to foster an open culture of trust to allow data sharing & data-integration to reap the full potential of analytics solutions.