I have access to medical claim data from a large health insurance company. As some of you may know there is a large delta between the price of drug X depending on where it is administered.
My company wishes to help members to reduce the cost they pay in deductibles, co pays, and coinsurance as well as reducing the cost for the health play by identifying the lowest cost of care typically home infusion or physician administration.
I am researching various approaches and customer segmentation modeling seems like the best approach to this sort of problem.
The claim data goes back 3 years and is at a member level. For each member claim, there could be many records in the DB. One line could be for 2hrs of chemo admin, another for the drugs they were provided, others for nurse's time, etc. Each row specifies where the site of care was i.e. home infusion, physician, outpatient, specialty pharmacy. Each claim also provides details on the primary diagnosis I.e. rheumatoid arthritis (R.A) etc.
Ideally, I would like to identify those members who use expensive sites of care when there are cheaper alternatives available. I was thinking for example that for primary diagnosis for R.A I could filter down to R.A members using the diagnosis code and then cluster those members based on certain input features such as demographics, distance to home infusion center/hospital/physician, level of cover, frequency of refill, dosage, cost of a drug, the current site of service, etc.
Is my approach right or is there a better way to solve this problem other than clustering?