The implementation of Taiwan’s diagnosis-related group payment system (Tw-DRGs) by the National Health Insurance Administration for hospital inpatient expenses necessitates the use of big data analytics in health-care resource management.

This study was conducted to develop a machine learning–based predictive model for inpatient resource utilization, aiming to provide decision support for managing Tw-DRGs.

Relevant data (January 2019 to July 2023; 65,446 patients) were collected from a regional hospital in Kaohsiung City. The data were randomly allocated to a training set (80%), a validation set (10%), and a testing set (10%). A machine learning algorithm and 33 features were used to construct the Tw-DRGs Medical Resource Utilization Predictive Model.

The use of the deep learning algorithm XGBoost ensured outstanding performance by the predictive model. The model achieved an area under the curve value of 91.09%, an accuracy score of 83.83, a precision score of 82.61%, a recall score of 80.84%, and an F1 score of 81.59%. Key predictive features included age, sex, major diagnostic category, primary diagnosis, main procedure, and hospitalization duration.

The development of a machine learning–based predictive model for inpatient medical resource utilization markedly advances the management of diseases within the Tw-DRGs framework.