Evidence map›Paper›PMID 42265678›Full record

ArticleBMC health services research2026

Optimizing case mix for per-diem payment of mental disorders based on E-CHAID decision tree analysis.

Yongqi Han, Xin Xiong, Menghan Zhang, Bo Yan

Abstract read
In one paragraph

Article in BMC health services research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yongqi HanCollege of Public Health, Chongqing Medical University, Chongqing, China.
Xin XiongPeople's Hospital of Xiushan County, Chongqing, China.
Menghan ZhangCollege of Public Health, Chongqing Medical University, Chongqing, China.
Bo YanCollege of Public Health, Chongqing Medical University, Chongqing, China. yanbo@cqmu.edu.cn.

Funding

Chongqing Municipal Health Commission 2024120007
6 · The paper itself

Abstract

backgroundMental disorders impose a substantial economic burden on patients, their families, and society. Although per-diem payment is commonly used for inpatient mental health services, the current per-diem payment standards for mental disorders remain relatively crude and fail to reflect the differences in actual resource consumption among patients with different characteristics. Therefore, this study analyzed the factors associated with per-diem inpatient costs among patients with mental disorders to provide evidence for the development of a refined case mix-based per-diem payment system suitable for mental health services provided in China.

methodsA total of 25,510 inpatients with mental disorders admitted to two hospitals in Chongqing between 2020 and 2024 were included in this study, and their demographic, clinical, and cost information collected. A univariate analysis and generalized linear model were used to identify the main factors associated with per-diem inpatient costs, and the Exhaustive CHi-squared Automatic Interaction Detection (E-CHAID) decision tree model was applied to construct a case-mix classification model for per-diem payments in mental health services.

resultsThe generalized linear model showed that sex, age, insurance type, discharge mode, hospital level, readmission plan, inclusion in clinical pathways, surgical status, number of comorbidities, number of hospitalizations, length of stay (LOS), and principal diagnosis were significantly associated with per-diem inpatient costs. Upon the use of LOS, hospital level, insurance type, and number of comorbidities as splitting variables, 10 case-mix groups were generated. The grouping scheme demonstrated high reliability and robustness, with a reduction in variance of 0.43 and coefficients of variation of < 1 across all groups (0.20-0.59). The per-diem payment standards for the 10 groups were 120.06-658.16 yuan.

conclusionsLOS, hospital level, insurance type, and number of comorbidities were key factors for grouping inpatients with mental disorders, and the construction of a refined case mix-based per-diem payment system using the E-CHAID decision tree is a reasonable and appropriate approach.

Indexed as

Decision TreesDiagnosis-Related GroupsMental DisordersAdultChinaFemaleHumansMaleMiddle AgedCase mixDecision treeE-CHAIDMental disordersPer-diem payment

Identifiers

PMID42265678
PMCPMC13393877

What Socratic holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.