  Accurate estimation of healthcare costs is crucial for healthcare systems to
plan and effectively negotiate with insurance companies regarding the coverage
of patient-care costs. Greater accuracy in estimating healthcare costs would
provide mutual benefit for both health systems and the insurers that support
these systems by better aligning payment models with patient-care costs. This
study presents the results of a generalizable machine learning approach to
predicting medical events built from 40 years of data from >860,000 patients
pertaining to >6,700 prescription medications, courtesy of Marshfield Clinic in
Wisconsin. It was found that models built using this approach performed well
when compared to similar studies predicting physician prescriptions of
individual medications. In addition to providing a comprehensive predictive
model for all drugs in a large healthcare system, the approach taken in this
research benefits from potential applicability to a wide variety of other
medical events.
