High-Throughput Approach to Modeling Healthcare Costs Using Electronic Healthcare Records

TL;DR

This paper introduces a machine learning method that leverages 40 years of electronic health records to accurately predict healthcare costs and medical events across a large patient population, aiding in better healthcare planning and insurance negotiations.

Contribution

The study presents a generalizable machine learning approach for predicting medical events and costs, validated on extensive healthcare data, improving upon existing models for large-scale healthcare systems.

Findings

Models performed well compared to similar studies.

Approach is applicable to various medical events.

Provides comprehensive predictive capabilities for healthcare costs.

Abstract

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.