Predicting Medical Fees for Hospitalized Inpatients and the Determination of Inflection Point

Abstract

Background: Taiwan’s Bureau of National Health Insurance (BNHI) implemented an inpatient DRG payment system scheduled for January 2008. Many hospital managers urgently invent initiatives to decrease the impacts of DRGs. Predicting medical fees for hospitalized inpatients every day and the corresponding inflection points (IPs) are required for investigations. The aims of this study include (1) verifying the efficacy of the exponential growth model on accumulative publications of mobile health research between 1997 and 2016 in the literature; (2) building the model of predicting medical fees for hospitalized inpatients and determining the inflection points; and (3) demonstrating visualizations of the prediction model online in use for hospital physicians.Methods: An exponential growth model was applied to determine the IP and predict the medical fees to help physicians contain the medical fees of a specific patient during hospitalization. The IP is equal to the item difficulty proven using the differential equation in calculus. An online visual display of the medically contained and predicted inpatient hospitalization was demonstrated in this study.Results: We observed (1) a model accuracy (R2 = 0.99) higher than that (R2 = 0.98) in the literature based on identical data; (2) 231 samples of medical fees for inpatients in the study module with a length of days between 6 and 20 and an IPS falling in the range between 1 and 10 (Q1=0.98, Q3=1.00); and (3) online visualization demonstration of medical fees predicted for hospital inpatients and IP determination on ogive curves.Conclusion: The exponential growth model can be applied to a clinical setting to help physicians consecutively predict medical fees for hospitalized inpatients and upgrade the level of hospital management in the future.