Júlia Folguera, Elisabet Buj, David Monterde, Gerard Carot-Sans, Isaac Cano, Jordi Piera-Jiménez, Miquel Arrufat
{"title":"使用两种支付系统对住院费用进行回顾性分析:诊断相关组(DRG)和 Queralt 系统(一种新开发的住院病人病例组合工具)。","authors":"Júlia Folguera, Elisabet Buj, David Monterde, Gerard Carot-Sans, Isaac Cano, Jordi Piera-Jiménez, Miquel Arrufat","doi":"10.1186/s13561-024-00522-6","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11202329/pdf/","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"Júlia Folguera, Elisabet Buj, David Monterde, Gerard Carot-Sans, Isaac Cano, Jordi Piera-Jiménez, Miquel Arrufat\",\"doi\":\"10.1186/s13561-024-00522-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>The Queralt system, originally developed to predict hospital outcomes, has good performance and efficiency for predicting hospitalization costs.</p>\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11202329/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://doi.org/10.1186/s13561-024-00522-6\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"96","ListUrlMain":"https://doi.org/10.1186/s13561-024-00522-6","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
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.
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.