Using Health Insurance Claims to Predict Hospital Days
Abstract
Health-care executives throughout the world are working to reduce the cost of treatment while increasing the quality of care provided. Hospitalization accounts for the majority of health-care spending. As a result, earlier identification of patients at higher risk of hospitalisation would assist health-care managers and insurers in developing better plans and strategies. In this study, a strategy for predicting the number of hospitalisation days in a population was created utilising large-scale health insurance claims data. Based on hospital admissions and procedure claims data, we used a regression decision tree algorithm and insurance claim data from 242 075 people over three years to forecast the number of days in hospital in the third year.
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