Retrospective analysis of hospitalization costs using two payment systems: the diagnosis related groups (DRG) and the Queralt system, a newly developed case-mix tool for hospitalized patients

• Published in: Health economics review Vol. 14; no. 1; pp. 45 - 9
• Main Authors: Folguera, Júlia, Buj, Elisabet, Monterde, David, Carot-Sans, Gerard, Cano, Isaac, Piera-Jiménez, Jordi, Arrufat, Miquel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 26.06.2024; Springer Nature B.V; BMC
• Subjects: Case-mix tools; Costs; Diagnosis related groups; DRGs; Efficiency; Health Care Management; Health Economics; Health Services Research; Hospital costs; Hospitalization; Medicine; Medicine & Public Health; Pharmacoeconomics and Health Outcomes; Public Finance; Public Health; Queralt system; Hospital costs; Case-mix tools; Queralt system; Diagnosis-related groups
• ISSN: 2191-1991; 2191-1991
• DOI: 10.1186/s13561-024-00522-6

Background Hospital services are typically reimbursed using case-mix tools that group patients according to diagnoses and procedures. We recently developed a case-mix tool (i.e., the Queralt system) aimed at supporting clinicians in patient management. In this study, we compared the performance of a broadly used tool (i.e., the APR-DRG) with the Queralt system. Methods Retrospective analysis of all admissions occurred in any of the eight hospitals of the Catalan Institute of Health (i.e., approximately, 30% of all hospitalizations in Catalonia) during 2019. Costs were retrieved from a full cost accounting. Electronic health records were used to calculate the APR-DRG group and the Queralt index, and its different sub-indices for diagnoses (main diagnosis, comorbidities on admission, andcomplications occurred during hospital stay) and procedures (main and secondary procedures). The primary objective was the predictive capacity of the tools; we also investigated efficiency and within-group homogeneity. Results The analysis included 166,837 hospitalization episodes, with a mean cost of € 4,935 (median 2,616; interquartile range 1,011–5,543). The components of the Queralt system had higher efficiency (i.e., the percentage of costs and hospitalizations covered by increasing percentages of groups from each case-mix tool) and lower heterogeneity. The logistic model for predicting costs at pre-stablished thresholds (i.e., 80th, 90th, and 95th percentiles) showed better performance for the Queralt system, particularly when combining diagnoses and procedures (DP): the area under the receiver operating characteristics curve for the 80th, 90th, 95th cost percentiles were 0.904, 0.882, and 0.863 for the APR-DRG, and 0.958, 0.945, and 0.928 for the Queralt DP; the corresponding values of area under the precision-recall curve were 0.522, 0.604, and 0.699 for the APR-DRG, and 0.748, 0.7966, and 0.834 for the Queralt DP. Likewise, the linear model for predicting the actual cost fitted better in the case of the Queralt system. Conclusions The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.