I have access to medical claims data from a US health insurance company.
I believe there's an opportunity to find some cost savings by switching the site of service from high cost outpatient facilities to possibly home infusion or physician for some procedures.
An example would be that Botox costs(in dollars) the company 10000 per claim at the outpatient facilities compared to 3000 at the physicians office.
I am interested in learning about how a data scientist would approach this problem of reducing costs from medical claims.
Could I run a cluster analysis on the population of claims to see who is most similar to the existing Botox patients who go to their physician and target these to move them to the lower cost sites of service?