High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records
This research provides a comprehensive predictive model for all drugs in a large healthcare system, which could help healthcare systems and insurers better align payment models with patient-care costs.
This study developed a machine learning approach to predict medical events using 40 years of electronic healthcare records from over 860,000 patients and 6,700 prescription medications. The models demonstrated good performance compared to similar studies focused on predicting individual medication prescriptions.
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.