Over the past few years there has been a lot of buzz around the OBAMACARE Health Reform which was implemented in 2014. The reform mandates that every individual buy health insurance, irrespective of which health bracket they fall in. In a way, it is a mandate for the employers too, to provide health insurance to each of their employees, irrespective of their company size. While this was gaining popularity, several states filed law suits against the federal government claiming that it was unconstitutional to force citizens to buy health insurance.
Companies spend billions of dollars every year on health insurance. Yet, we see very limited initiatives to organize healthcare data and do analytics around it. The major hurdle that comes in the way of healthcare companies, is to decide on the kind of health plan / deal they can offer small and medium size employers, so that their interest in providing comprehensive healthcare to their employees goes up. Companies like Blue Cross & Blue Shield, Kaiser Permanente, Highmark, United Health Group etc. have spent lots of money in setting up their IT infrastructure, but the investment in Exploratory and Predictive Analytics is way behind.
Exploratory Data Analysis has proved to be a great starting point in the analysis of B2B healthcare relations. It has enabled healthcare firms to help companies of all sizes in providing comprehensive health insurance. Analysis like classification and segmentation help in strategizing plans for small companies (with even less than 5 employees) where it gives them an option to be a part of a pool or consortium and avail healthcare like a mid-sized company. Now these companies individually may not be in a position to buy healthcare for its employees at all, but because they join a bigger umbrella (a consortium or a pool), it helps them afford the healthcare plan.
For companies that are mid-sized and over, proper predictive analytics can help healthcare firms estimate the amount of claims that might arise from employees. This will help them estimate right premiums and other costs like co-pay and deductibles for the insured.
With proper analysis of an individual’s health, premium and claims history, data scientists might be able to suggest a proper plan for individuals (HMP vs PPO vs Consumer Directed Health Plan – CDHP).
There is lots of data available in the healthcare system which requires extensive research and analysis. These include –
- DxCG Health Risk Scores Data
- Claims Data
- Inpatient Claims
- Out-Patient Claims
- Denials Data
- Resubmissions of Claims
- Premiums (Co-Pay and Deductible)
- Dental Insurance
- Eye Care Insurance
- TPA Data
The points mentioned above just form the tip of the iceberg. Data scientists have become really interested in the use of big data in healthcare insurance. About 70% of the data in healthcare is unstructured. By using Big Data techniques data scientists expect to learn trends from data so that important information can be extracted from them which could be used for serving Healthcare firms, employers and brokers as well.