Personalized Precision in Healthcare Payment Systems: Insights From a Real-World Cancer Patients Cohort Study.

Background: The medical expenses of cancer patients have been rising worldwide, and the current payment system based on diagnosis-related groups is limited in reflecting the actual burden at the patient level. We aimed to develop a prediction model for reasonably high cost of hospitalization, taking gastric cancer (GC) patients undergoing surgery as an example. Methods: In this real-world patient cohort study, we collected clinical data from 3981 GC surgery inpatients in a tertiary hospital in Beijing, China. Reasonably high cost was defined as exceeding the 75th percentile of the hospitalization costs. The final model for reasonably high cost was derived based on univariate and multivariate unconditional logistic regression, with predictors selected according to the Akaike information criterion. Model performance was evaluated in terms of discrimination and calibration. Results: The final model consisted of 13 predictors, with the area under the receiver operating characteristics curve of 0.718 (95% confidence interval: 0.700-0.736). Leave-one-out cross-validation generated an area under the receiver operating characteristics curve of 0.708 (95% confidence interval: 0.690-0.727). No significant differences in model discrimination were detected among the subgroups stratified by gender, age, N stage, surgical method, and admission year. The calibration plot showed good agreement between predicted probability and observed frequency of reasonably high cost. Conclusions: Our prediction tool for reasonably high cost of GC surgery patients has demonstrated good performance. The combined use of diagnosis-related groups payment and reasonably high cost assessment is expected to promote individualization and precision in medical cost control and ensure rational, efficient, and equitable provision of medical services.