The Health Insurance Kaleidoscope
(Note: This post was first published by the author on 31 October 2009 at http://www.healthinsurancecxo.blogspot.com )
Health insurance business involves managing large repositories of data and information. It is critical to understanding the health profile of the country and plan accordingly. To the insurers, it is critical to designing products in such a way that a large percentage of the insured see it as a security, yet the insurers do not lose on account of product characteristics.
To do analysis of the kind suggested in this article, it is assumed that insurers and their TPAs have the appropriate ERP systems to capture the required data and the BI tools to analyze them. The challenge lies, not in doing this kind of analysis once a year, but in being able to do anytime, from anywhere, in near real time! With ease that can be called as ‘child’s play’ – with ‘Drag & Drop’, ‘Filter’, ‘Select / Unselect’ features of typical BI tools.
This post will be built over a period through a series of graphical representation of how data, if captured by the health insurance administrators, can be used meaningfully for strategic and operational management purposes. However, the focus will be more on understanding the Claims, Hospitals and Diseases rather than the enrollment profiles.I will take up some questions randomly and show how graphical representation can give the answers.
Note & Disclaimer: The information shown below is based on “hypothetical scenario” and does not claim to be based on real data. The illustrations are to be used as templates for study and understanding the perspectives of health insurance
| 1. What is the distribution of Major Expense Heads of hospital bills? (Click on graph for a clear view) |  |
2. How would the above distribution look if we look at only heart related ailments? |
| 3. What is the typical distribution of hospital earnings from various heart ailments? In the graph: NO = No surgery; YES = With surgeryChronic Ischemic Heart Disease (without surgery) tops the list and is nearly double that of IHD (with surgery). For a detailed description of ICD Codes used on the X-axis in the graph, visit WHO site (link givn at top left of this blog) (I21-Acute Myocardial Infarction)For complete list, click link to WHO site on top left corner of this blog |
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| 4. Are claims generally made by existing or new policy holders?Interpretation: Invariably, claims by new policy holders exceed that of existing policy holders – both in Number and Value – across all diseases. Graphs typically look like the one alongside – year after year! |  |
| 5. How is the distribution of Claim Amount across Gender and Age Groups?Interpretation: As expected, claim value is higher for males than females across most groups; the higher claims by females in 21-30 age band is due to Maternity Claims |  |
| 6. How is the distribution of Claims (by Count or Value)based on Length of Stay for major heart diseases such as Chronic IHD (Graph on Left) and Acute Myocardial Infarction (Graph on Right)? | |
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| 7. For a given body part (say, Circulatory System), how do the claim payouts differ across two types of hospitals, in two different years?Interpretation of Graph: For circulatory system ailments, a higher Length of Stay appears to be more common in Premium Hospitals than Economy Hospitals |
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