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)。

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-06-26 DOI:10.1186/s13561-024-00522-6
Júlia Folguera, Elisabet Buj, David Monterde, Gerard Carot-Sans, Isaac Cano, Jordi Piera-Jiménez, Miquel Arrufat
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引用次数: 0

摘要

背景:医院服务通常使用病例组合工具进行报销,该工具根据诊断和手术对患者进行分组。我们最近开发了一种病例组合工具(即 Queralt 系统),旨在为临床医生管理病人提供支持。在这项研究中,我们比较了一种广泛使用的工具(即 APR-DRG)和 Queralt 系统的性能:对 2019 年期间加泰罗尼亚卫生研究所八家医院中任何一家医院的所有住院病人(即约占加泰罗尼亚所有住院病人的 30%)进行回顾性分析。费用从全面成本核算中提取。电子病历用于计算 APR-DRG 组别和 Queralt 指数,以及诊断(主要诊断、入院时的合并症和住院期间发生的并发症)和手术(主要和次要手术)的不同子指数。主要目标是这些工具的预测能力;我们还调查了效率和组内同质性:分析包括 166,837 次住院治疗,平均费用为 4,935 欧元(中位数为 2,616 欧元;四分位数范围为 1,011-5,543 欧元)。Queralt系统的各组成部分具有更高的效率(即每个病例组合工具中百分比不断增加的组别所涵盖的费用和住院百分比)和更低的异质性。在预先设定的阈值(即第 80、90 和 95 百分位数)下,预测成本的 Logistic 模型的效率更高,异质性更低、第 80、90 和 95 百分位数)显示,Queralt 系统的性能更好,尤其是在结合诊断和手术(DP)时:第 80、90 和 95 百分位数费用的接收器操作特征曲线下面积分别为 0.904、0.882、0.882、0.882。APR-DRG为0.904、0.882和0.863,Queralt DP为0.958、0.945和0.928;精确-调用曲线下的相应面积值分别为:APR-DRG为0.522、0.604和0.699,Queralt DP为0.748、0.7966和0.834。同样,预测实际成本的线性模型也更适合 Queralt 系统:结论:Queralt 系统最初是为预测住院结果而开发的,在预测住院费用方面具有良好的性能和效率。
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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.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
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2.10%
发文量
464
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