Optimization of case-mix for per-diem payment of mental disorders based on the E-CHAID decision tree

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Although per-diem payment can effectively contain healthcare expenditures, the current per-diem payment standards for mental disorders remain relatively crude and fail to reflect differences in actual resource consumption among patients with different characteristics. Therefore, this study analyzes the factors associated with inpatient cost per diem among inpatients with mental disorders to provide evidence for developing a refined case-mix–based per-diem payment system suitable for mental health services in China. Methods A total of 25,510 inpatients with mental disorders admitted to two hospitals in Chongqing between 2020 and 2024 were included. Their demographic, clinical, and cost information were collected. Univariate and multivariable linear regression analyses were performed to identify the main factors associated with inpatient cost per diem, and an E-CHAID decision tree was applied to construct a case-mix classification model for per-diem payment in mental health services. Results The multivariable linear regression results showed that sex, age, insurance type, discharge mode, hospital level, readmission plan, inclusion in clinical pathways, surgical status, comorbidity status, number of hospitalizations, length of stay, and principal diagnosis were significantly associated with inpatient cost per diem. Using length of stay, hospital level, insurance type, and number of comorbidities as splitting variables, a total of 10 case-mix groups were generated. The grouping scheme demonstrated high reliability and robustness, with a reduction in variance (RIV) of 0.43 and coefficients of variation (CVs) below 1 across all groups (0.20–0.59). In addition, the per-diem payment standards for the 10 groups ranged from 120.06 to 658.16 yuan. Conclusion Length of stay, hospital level, insurance type, and number of comorbidities are 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. E-CHAID Decision tree Mental disorders Per diem payment Case-mix Figures Figure 1 Background In recent years, the global burden of mental disorders has become increasingly severe. According to the latest estimates of the World Health Organization, more than 1.1 billion people worldwide—approximately one in seven of the global population—are affected by mental disorders, which accounted for about 155 million disability-adjusted life years (DALYs) in 2021[ 1 – 2 ]. In addition, depression and anxiety alone are estimated to cause global economic losses of approximately US $ 1 trillion annually[ 3 ]. The burden of mental disorders in China is similarly alarming. Data from the China Mental Health Survey (CMHS) indicate that the lifetime prevalence of mental disorders in China reaches 16.57%, with a 12-month prevalence of approximately 9.32%[ 4 ]. In 2021, the number of incident cases of mental disorders in China was estimated at 54.62 million, and mental disorders were responsible for 23.20 million person-years of DALYs[ 5 ]. Furthermore, in 2018, total treatment expenditures for mental and psychological disorders in China amounted to 87.168 billion yuan, accounting for 2.47% of total national disease-related healthcare expenditures and approximately 0.09% of gross domestic product. Of the total treatment costs for mental disorders, 31.8% were borne by households, and 48.3% of caregivers of patients with mental disorders experienced substantial financial burden[ 6 – 7 ]. These findings indicate that mental disorders impose a profound economic burden not only on affected individuals but also on their families and society as a whole. Therefore, it is imperative for the Chinese government to implement effective measures to control healthcare expenditures, and among the policy instruments for containing mental healthcare costs and improving the efficiency of resource utilization, the selection and design of health insurance payment mechanisms play a pivotal role. At present, China’s health insurance payment system has substantially entered a new stage dominated by prospective payment, with multiple disease categories being covered under diagnosis-related groups (DRGs). For inpatient care of mental disorders, China mainly adopts case-based payment (such as DRG-based payment) and per-diem payment; however, DRG-based payment is not well suited to mental disorders, and the rate-setting for per-diem payment remains relatively crude[ 8 ]. For example, in regions such as Beijing, per-diem payment rates are set solely on the basis of hospital level, resulting in weak alignment between payment levels and patients’ clinical characteristics, and thus failing to adequately reflect the healthcare needs and resource consumption of inpatients with mental disorders[ 9 ]. Research on refined case-mix–based per-diem payment systems for mental disorders in China remains limited. Most existing studies are confined to preliminary designs based on a narrow set of grouping variables or to policy feasibility discussions grounded in international experience and theoretical frameworks[ 10 – 11 ]. Several Western countries have explored refined case-mix–based per-diem payment systems for inpatients with mental disorders, representing a transition from extensive payment models toward more patient-centered, finely stratified reimbursement mechanisms. In Switzerland, the mental health insurance pricing system (Tariff Psychiatry, TARPSY) is implemented, whereby cases are first classified into nine basic psychiatric cost groups (APCG) according to the principal diagnosis, and then further subdivided into 23 psychiatric cost groups (PCG) based on patient characteristics such as clinical complexity, secondary diagnoses, and age. The daily reimbursement amount is calculated as the product of a daily weight and a hospital base rate and is ultimately paid on a per-diem basis[ 12 – 13 ]. Germany applies the payment system for psychiatric and psychosomatic institutions (Pauschalierende Entgeltsystem für Psychiatrische und Psychosomatische Einrichtungen, PEPP), which, similar in nature to Switzerland’s TARPSY system, constitutes a refined case-mix–based per-diem payment model, although the grouping structures and criteria differ between the two systems. The PEPP system adopts a three-level grouping structure: cases are first classified into 10 structural categories (Struktur kategorien, SK) according to the type of inpatient care; within each SK, 24 basic PEPP groups (Basis-PEPP) are formed based on the ICD-10-GM principal diagnosis or specific procedures; finally, cases are further stratified into 84 specific PEPP groups according to factors associated with resource consumption, including age, comorbidities and complications, and treatment intensity. Each group is assigned a fixed daily reimbursement rate, with payments decreasing stepwise according to length of stay[ 14 ]. In the United States, inpatient psychiatric services are reimbursed under the Inpatient Psychiatric Facility Prospective Payment System (IPF PPS), which adopts a per-diem payment approach and does not directly apply case grouping; instead, the base per-diem rate is adjusted using coefficients reflecting regional differences, age, comorbidities, length of stay, and diagnosis-related group classifications, thereby generating differentiated daily payment standards[ 15 ]. In addition, the Patient-Driven Payment Model (PDPM), as another per-diem–based payment approach in the United States, also draws on case-mix principles by establishing different per-diem payment rates for different patient groups[ 16 ]. In view of the limitations of existing research in China and drawing on international experience, this study utilizes large-scale inpatient data of patients with mental disorders and applies the E-CHAID decision tree method to construct and validate a refined case-mix–based per-diem payment system for inpatients with mental disorders. The proposed framework is expected to provide empirical evidence for optimizing per-diem payment policies for mental disorders in China, enhance the rationality and precision of payment standards, promote the efficient allocation of healthcare resources, and thereby alleviate patients’ financial burden while curbing the unreasonable growth of medical expenditures. Methods Data Collection and Selection The data for this study were obtained from hospital discharge records of inpatients at two specialized psychiatric hospitals in Chongqing, China, covering the period from January 1, 2020, to December 31, 2024. Patients were included if they were diagnosed with mental disorders according to the International Classification of Diseases, 10th Revision (ICD-10), with ICD-10 codes ranging from F00 to F99. Patients were excluded if their hospitalization cost records contained evident logical errors or if the length of stay was less than one day. A total of 25,510 inpatients with mental disorders were finally included, and information on demographic characteristics, clinical services, and medical expenditures was collected. To eliminate the impact of price fluctuations and improve the comparability of inpatient cost data across different years, inpatient cost per diem from 2020 to 2024 was adjusted using the Consumer Price Index (CPI) for healthcare in Chongqing, China, with 2024 as the base year. CPI data were obtained from the Statistical Yearbook of the National Bureau of Statistics of China. Statistical Analysis First, differences in inpatient cost per diem across patient subgroups with binary and multicategorical characteristics were examined using the Mann–Whitney U test or the Kruskal–Wallis H test, as appropriate. Second, based on the results of the univariate analyses, variables showing statistically significant differences (P < 0.05) were entered into multivariable linear regression models to identify the main factors associated with inpatient cost per diem. Finally, inpatient cost per diem was specified as the dependent variable, and factors with significant effects on inpatient cost per diem were used as splitting variables to classify patients by applying the E-CHAID decision tree method. Case-Mix Classification Methods In the case-mix classification, this study used the Exhaustive Chi-squared Automatic Interaction Detection (E-CHAID) model for decision tree analysis. E-CHAID is an improved version of the Chi-squared Automatic Interaction Detection (CHAID). Its grouping principle is based on the relationships between the target variable and predictor variables, and samples are automatically grouped in multidimensional contingency tables according to the significance levels of chi-squared tests. A major advantage of E-CHAID is its ability to handle nonlinear data and allow for a certain degree of missing values, which helps overcome the limitations of traditional parametric tests[ 17 ]. In addition, compared with CHAID, E-CHAID applies more thorough procedures for variable merging and grouping, which supports more accurate identification of splitting variables[ 18 ]. The parameters for decision tree growth were set as follows: a maximum depth of three levels, a minimum of 100 cases in each parent node, a minimum of 50 cases in each child node, and a significance level of α = 0.05 for node splitting. Evaluation Metrics The nonparametric Kruskal–Wallis H test and the Reduction in Variance (RIV) were used to assess between-group heterogeneity, while the coefficient of variation (CV) was used to evaluate within-group homogeneity. RIV was calculated as (total sum of squared deviations − sum of squared deviations within all subgroups) divided by the total sum of squared deviations, and CV was calculated as the standard deviation divided by the mean. For the grouping model, a statistically significant Kruskal–Wallis H test, together with a larger RIV and a smaller CV, indicates greater between-group variation and smaller within-group variation, reflecting better grouping performance. In this study, RIV > 40% and CV < 1 were adopted as the criteria for evaluating grouping results[ 19 ]. All analyses were conducted using SPSS version 27.0, and the significance level was set at P < 0.05. Calculation of Per-Diem Payment Standards, High-Cost Cases, and Disease Weights To reduce the influence of extreme values, the median inpatient cost per diem within each group was used as the per-diem payment standard for that group. The upper cost limit was defined as the 75th percentile of the inpatient cost per diem plus 1.5 times the interquartile range (P75 + 1.5IQR) to identify extremely high-cost cases, and cases exceeding this limit were classified as outliers. The disease weight was calculated as the ratio of the mean inpatient cost per diem of a given case-mix group to the mean inpatient cost per diem of all cases; a higher weight indicates greater consumption of healthcare resources. Results Baseline characteristics of patients A total of 25,510 inpatients with mental disorders were included in this study. The length of stay was mainly distributed within ≤ 30 days (56.12%) and 31–60 days (18.81%). Most patients were treated in tertiary medical institutions (92.5%). In terms of health insurance coverage, the Urban and Rural Resident Basic Medical Insurance accounted for the largest proportion (64.92%). In addition, 55.69% of patients had at least one comorbidity. Other characteristics are presented in Table 1 . The results of univariate analysis showed that sex, age, insurance type, admission mode, discharge mode, source of admission, hospital level, readmission plan, inclusion in clinical pathways, surgical status, number of comorbidities, medical payment method, number of hospitalizations, length of stay, and principal diagnosis were significantly associated with differences in hospitalization costs. Table 1 Baseline characteristics of inpatients with mental disorders and univariate analysis of inpatient cost per diem Variables N(%) Inpatient cost per diem M(P 25 -P 75 ) Z/H 值 P value Sex -43.102 <0.001 male 10974(43.02) 443.69(306.69, 600.57) female 14536(56.98) 561.58(444.81, 706.68) Age 2919.067 <0.001 <18 5143(20.16) 636.10(520.08, 777.21) 18–34 8165(32.01) 531.37(672.70, 412.45) 35–49 4732(18.55) 455.72(304.44, 612.11) 50–64 4696(18.41) 457.25(315.20, 597.68) ≥ 65 2774(10.87) 426.99(318.67, 558.24) Insurance type -40.769 <0.001 Urban Employee Basic Medical Insurance 8950(35.08) 442.15(349.27, 535.76) Urban and Rural Resident Basic Medical Insurance 16560(64.92) 572.57(421.68, 714..45) Admission mode 9.678 0.0079 outpatient 25326(99.28) 516.39(378.97, 668.03) emergency department 177(0.69) 564.51(444.75, 696.97) transfer from other medical institutions 7(0.03) 601.64(187.46, 661.82) Condition on admission -2.446 0.0145 general 25476(99.87) 516.68(379.26, 668.10) severe 34(0.13) 597.35(478.20, 734.34) Discharge mode 644.788 <0.001 discharge against medical advice 1078(4.23) 690.27(539.85, 860.77) discharge with medical advice 24348(95.44) 510.13(372.89, 658.01) transfer to another hospital/community with medical advice 24(0.09) 624.24(386.21, 845.92) death 4(0.02) 246.99(164.61, 323.06) other 56(0.22) 721.63(556.67, 937.24) Source of admission -12.658 <0.001 local 24194(94.84) 512.30(373.49, 664.93) non-local 1316(5.16) 575.90(471.71, 711.85) Hospital level -68.425 <0.001 secondary hospital 1912(7.50) 137.28(109.39, 220.31) tertiary hospital 23598(92.50) 535.46(414.44, 682.56) Readmission plan 430.629 <0.001 none 24,182(94.79) 520.91(386.37, 671.49) readmission plan within 7 days 591(2.32) 336.09(309.64, 374.79) readmission plan within 31 days 737(2.89) 536.21(424.26, 687.19) Inclusion in clinical pathways -22.889 <0.001 no 20,725(81.24) 503.18(357.39, 655.26) yes 4,785(18.76) 570.41(450.91, 717.77) Surgical status -55.433 <0.001 none 20205(79.20) 474.04(340.69, 620.45) electroconvulsive therapy 5305(20.80) 649.07(552.26, 761.13) Number of comorbidities 721.552 <0.001 none 11,303(44.31) 560.03(408.66, 710.43) number of comorbidities = 1 5520(21.64) 529.47(410.99, 663.46) number of comorbidities = 2 3,084(12.09) 497.34(384.95, 632.83) number of comorbidities ≥ 3 5,603(21.96) 437.76(332.13, 566.31) Medical payment method 2609.949 <0.001 Urban Employee Basic Medical Insurance 7537(29.55) 445.85(349.06, 566.20) Urban and Rural Resident Basic Medical Insurance 11614(45.53) 508.33(333.89, 663.06) fully self-paid 5933(23.26) 624.56(522.00, 767.56) fully publicly funded 11(0.04) 449.82(342.85, 704.39) other 415(1.63) 433.34(338.20, 567.55) Number of hospitalizations 5272.885 <0.001 1 20140(78.95) 522.70(437.92, 701.75) 2 2713(10.64) 431.58(309.74, 570.86) ≥ 3 2657(10.42) 242.87(131.51, 319.52) Length of stay 12672.528 <0.001 ≤ 30Days 14,317(56.12) 625.65(509.31, 773.12) 31-60Days 4799(18.81) 484.03(410.85, 572.88) 61–90 Days 1,350(5.29) 420.09(353.44, 489.08) 91–120 Days 560(2.20) 364.51(296.31, 431.02) 121–150 Days 517(2.03) 281.84(197.87, 368.60) ≥ 151 Days 3967(15.55) 283.09(185.75, 319.06) Principal diagnosis 4343.333 <0.001 F00-F09 1561(6.12) 432.30(322.68, 577.71) F10-F19 867(3.40) 401.32(289.60, 537.54) F20-F29 10324(40.47) 429.05(301.40, 574.12) F30-F39 7996(31.34) 597.91(483.65, 732.94) F40-F49 1727(6.77) 567.61(467.26, 703.22) F50-F59 62(0.24) 589.88(464.43, 739.73) F60-F69 108(0.42) 507.86(371.98, 685.10) F70-F79 348(1.36) 368.61(187.50, 536.39) F80-F89 18(0.07) 621.97(529.23, 867.91) F90-F98 2449(9.60) 619.97(514.61, 751.51) F99-F99 50(0.2) 275.70(165.84, 786.67) Multivariable linear regression analysis Using the log-transformed inpatient cost per diem as the dependent variable, a multivariable linear regression analysis was performed with the 15 variables that were statistically significant in the univariate analysis as independent variables. The results 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, and principal diagnosis were significantly associated with the dependent variable (P < 0.05). Collinearity diagnostics indicated a mean variance inflation factor (VIF) of 2.153, with all VIF values below 3, suggesting no evidence of multicollinearity among the variables. The detailed results are presented in Table 2 . Table 2 Results of multivariable linear regression analysis of inpatient cost per diem among patients with mental disorders Variables unstandardized coefficient β t P value Tolerance VIF B ± SE 95%CI Constant 5.391 ± 0.024 (5.344, 5.438) 222.736 <0.001 Sex 0.035 ± 0.004 (0.026, 0.043) 0.032 7.992 <0.001 0.880 1.136 Age -0.006 ± 0.002 (-0.010, -0.002) -0.014 -2.791 0.005 0.549 1.822 Insurance type 0.139 ± 0.005 (0.129, 0.150) 0.123 25.662 <0.001 0.612 1.633 Condition on admission 0.056 ± 0.056 (-0.053, 0.165) 0.004 1.008 0.313 0.998 1.002 Discharge mode -0.071 ± 0.008 (-0.087, -0.055) -0.033 -8.743 <0.001 0.989 1.011 Source of admission 0.005 ± 0.009 (-0.014, 0.023) 0.002 0.496 0.620 0.967 1.034 Hospital level 0.995 ± 0.010 (0.975, 1.016) 0.484 94.917 <0.001 0.538 1.858 Readmission plan 0.020 ± 0.006 (0.009, 0.031) 0.013 3.474 <0.001 0.975 1.026 Inclusion in clinical pathways 0.036 ± 0.005 (0.026, 0.047) 0.026 6.779 <0.001 0.940 1.063 Surgical status 0.167 ± 0.005 (0.157, 0.177) 0.125 31.985 <0.001 0.914 1.094 Number of comorbidities 0.012 ± 0.002 (0.008, 0.016) 0.026 5.644 <0.001 0.671 1.490 Medical payment method 0.003 ± 0.003 (-0.003, 0.009) 0.005 1.096 0.273 0.653 1.531 Number of hospitalizations 0.114 ± 0.005 (0.105, 0.123) 0.137 24.014 <0.001 0.430 2.325 Length of stay -0.155 ± 0.002 (-0.158, -0.152) -0.522 -96.228 <0.001 0.475 2.104 Principal diagnosis 0.002 ± 0.001 (0.000, 0.005) 0.010 2.268 0.023 0.717 1.396 Development of the decision tree–based case-mix grouping scheme Using inpatient cost per diem as the dependent variable, the statistically significant factors identified in the multivariable analysis—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, and principal diagnosis—were entered into the decision tree model as independent variables. In the final model, length of stay, hospital level, insurance type, and number of comorbidities were selected as the splitting variables, resulting in a total of 10 case-mix groups (Fig. 1 ). After grouping, the Kruskal–Wallis H test was applied to examine differences in inpatient cost per diem among groups. The results showed significant differences in cost distributions across the 10 groups (H = 14,014.074, P < 0.001), indicating good between-group heterogeneity and statistically significant group separation. In addition, the calculated RIV value was 0.43, further supporting substantial between-group heterogeneity. The CV values of the case-mix groups ranged from 0.20 to 0.59, indicating small within-group variation and satisfactory grouping performance. The detailed results are presented in Table 3 . Table 3 Case-mix classification of inpatients with mental disorders in China Class Group description Frequency(%) Mean SD CV 1 ≤ 30 Days、Urban Employee Basic Medical Insurance 4016(15.7) 594.35 291.29 0.49 2 ≤ 30 Days, Urban and Rural Resident Basic Medical Insurance 10301(40.4) 719.12 328.08 0.46 3 31–60 Days、61–90 Days、91–120 Days, tertiary hospital 6269(24.6) 483.67 119.19 0.25 4 31–60 Days、61–90 Days、91–120 Days, secondary hospital 440(1.7) 141.81 83.19 0.59 5 121–150 Days, ≥ 151 Days, tertiary hospital, no comorbidities 467(1.8) 241.38 82.25 0.34 6 121–150 Days, ≥ 151 Days, tertiary hospital, number of comorbidities ≥ 3 1771(6.9) 322.51 65.91 0.20 7 121–150 Days, ≥ 151 Days, tertiary hospital, number of comorbidities = 1 499(2.0) 279.05 82.15 0.29 8 121–150 Days, ≥ 151 Days, tertiary hospital, number of comorbidities = 2 478(1.9) 298.38 74.04 0.25 9 secondary hospital, ≥ 151 Days 1010(4.0) 155.49 57.29 0.43 10 121–150 Days, secondary hospital 259(1.0) 197.14 84.49 0.37 Inpatient cost per diem, high-cost cases, and weights of each case-mix group After grouping inpatients with mental disorders using the decision tree, the per-diem payment standards, high-cost cases, and weights for each group were further calculated. The results showed that the per-diem payment standards ranged from 120.06 to 658.16 yuan. A total of 1,730 patients had inpatient cost per diem exceeding the cost control threshold, accounting for 6.78% of all cases. Group 2 had the highest weight, indicating that patients in this group were more complex and had higher levels of healthcare resource utilization. The detailed results are presented in Table 4 . Table 4 Inpatient cost per diem, high-cost cases, and weights of each case-mix group Class Per-Diem Payment Standards Upper Limit (P75 + 1.5IQR) Outlier(%) RW 1 510.7316 904.509 325(8.09) 1.078 2 658.1628 1047.935 881(8.55) 1.304 3 469.274 709.8118 237(3.78) 0.877 4 120.0552 158.0294 62(14.09) 0.257 5 190.1968 417.327 17(3.64) 0.438 6 314.3028 394.3604 172(9.71) 0.585 7 292.9374 469.734 11(2.20) 0.506 8 305.6115 407.0426 23(4.81) 0.541 9 229.8282 439.024 0(0) 0.358 10 152.4093 290.6115 2(0.77) 0.282 Discussion The results of this study show that the E-CHAID decision tree model classified inpatients with mental disorders into 10 case-mix groups using four splitting variables, with length of stay serving as the primary splitting variable. As length of stay increased, inpatient cost per diem decreased, which is consistent with previous studies[ 20 – 21 ]. This relationship may reflect the typical pattern of inpatient care for mental disorders, characterized by more intensive resource use in the early stage and a gradual stabilization in later stages[ 22 ]. During the initial phase of hospitalization, patients are often in the acute stage, and diagnostic tests, treatments, and intensive nursing care are concentrated, resulting in higher daily costs. As patients enter the stabilization and rehabilitation phases, care focuses on maintenance and functional recovery, with fewer new treatment services and lower testing frequency, leading to a progressive decline in daily resource use [ 23 ]. Therefore, using length of stay as the primary splitting variable allowed the E-CHAID model to stratify patients into relatively homogeneous per-diem cost categories corresponding to the early, middle, and late stages of hospitalization. This provides empirical evidence for the development of phased and differentiated per-diem payment standards. Hospital level is selected as one of the second-level splitting variables, and inpatient cost per diem is higher among patients treated in higher-level medical institutions, which is consistent with previous studies[ 24 – 25 ]. This is because higher-level medical institutions usually undertake the treatment of patients with mental disorders in the acute phase and more complex cases, including those with acute agitation, severe behavioral problems, or concomitant physical risks, whereas patients with relatively stable conditions are more often hospitalized for treatment or rehabilitation management in lower-level medical institutions[ 26 – 27 ]. Under this care structure, inpatients with mental disorders treated in higher-level medical institutions tend to have higher overall risk and greater management complexity. Accordingly, the inpatient management of these patients requires more intensive medical and nursing input and a broader range of therapeutic and monitoring services. In addition, higher-level medical institutions generally have more specialized staff and more advanced technical capacity, which are associated with higher treatment costs. Together, these factors increase daily resource consumption among inpatients with mental disorders in higher-level medical institutions.[ 28 ]. In the existing design of per-diem payment for mental disorders, hospital level has been incorporated into the payment standard system [ 29 ]. This indicates that differences in service capacity and treatment costs across institutional levels are regarded as important determinants of patient resource use under per-diem payment. For example, cities such as Xuzhou and Ningbo set per-diem payment standards for mental disorders according to hospital level. Therefore, when constructing a case-mix–based per-diem payment system for mental disorders, incorporating hospital level as a case-mix grouping variable in the model design can improve the level of refinement of the per-diem payment system.. Based on the initial stratification by length of stay and hospital level, the E-CHAID decision tree further selects insurance type and number of comorbidities to refine the classification of inpatients with mental disorders. Patients covered by the Urban and Rural Resident Basic Medical Insurance have higher inpatient cost per diem than those covered by the Urban Employee Basic Medical Insurance, a pattern that is also reported in previous studies[ 30 ]. This is mainly because the population covered by the Urban and Rural Resident Basic Medical Insurance includes a higher proportion of older adults and individuals with lower socioeconomic status, who are more likely to delay seeking care and be hospitalized after disease progression, resulting in greater treatment difficulty and higher per-diem costs[ 31 – 32 ]. In addition, reimbursement levels for Urban and Rural Resident Basic Medical Insurance beneficiaries in China are generally lower, which further contributes to higher daily costs in this group[ 33 ]. As shown in Fig. 1 , the inpatient cost per diem for Node 9 (Urban and Rural Resident Basic Medical Insurance) is 719.12 yuan, which is higher than the mean inpatient cost per diem of 594.345 yuan for Node 7 (Urban Employee Basic Medical Insurance). When these two nodes are further subdivided in the case-mix classification, differences in healthcare-seeking behavior and insurance coverage associated with insurance type are reflected in the corresponding per-diem payment standards and weights. The number of comorbidities is also selected as a splitting variable, and patients with more comorbidities have higher inpatient cost per diem. This is because when patients with mental disorders present with additional diseases, hospitals increase diagnostic testing, medication use, and nursing input, and inpatient cost per diem rises as the number of comorbidities increases[ 34 ]. Studies from multiple countries have confirmed that comorbid physical conditions in patients with mental disorders lead to additional healthcare resource use and cost burden[ 35 – 36 ]. Therefore, the impact of comorbidities on inpatient cost per diem should be fully considered when implementing refined per-diem payment for mental disorders, This may help avoid inappropriate case-mix classification that could encourage hospitals to preferentially admit patients with milder conditions while discouraging the admission of patients with more severe conditions. In addition, classifying comorbidities not only by number but also by different types may help to more accurately characterize differences in per-diem resource consumption among inpatients with mental disorders. The case-mix classification system developed in this study demonstrates good statistical performance. With only 10 groups and a total of 25,510 inpatients with mental disorders included, the reduction in variance (RIV) reaches 0.43, which is higher than that reported for many existing case-mix classification systems. A scoping review by Tran et al. on case-mix classification systems in community mental health shows that although various models have been developed in this field, most systems still have limited explanatory power for mental health resource use. Among the few better-performing models, the Australian Mental Health Classification and Costing System (AMHCC) defines 46 groups in community settings and achieves an RIV of 0.266 [ 37 ]. In addition, the RIV observed in this study is also higher than that of the case-mix classification system jointly developed by interRAI and the Arkansas Department of Human Services for resource allocation in child and adolescent mental health (ChYRI), which includes eight groups and reports an RIV of 0.30[ 38 ]. However, it remains lower than the system constructed by Eagar et al. based on mental health service data in New Zealand, which reports an RIV of 0.67[ 39 ]. Compared with the international findings described above, the results of this study are generally reasonable but still require further improvement. Finally, as shown in Table 4 , establishing scientifically sound per-diem payment standards and cost control thresholds for each case-mix group not only helps to optimize health insurance payment incentive mechanisms and regulate healthcare service delivery, but also strengthens the management of high-cost cases and promotes more appropriate control of inpatient expenditures. However, this study has several limitations. First, the data used in this analysis are derived from two hospitals in Chongqing; therefore, caution is required when generalizing the findings to the national level, and further studies conducted on a larger scale and across different regions are needed for validation. Second, due to the limited availability of medical record data, clinical severity indicators or functional assessment measures could not be included, and patient complexity is mainly characterized indirectly by variables such as length of stay and number of comorbidities, which may not fully reflect the true clinical condition of mental disorders. Third, this study adopts a retrospective design and is therefore unable to evaluate the dynamic impact of implementing the refined case-mix–based per-diem payment model on cost containment; the relevant conclusions should be further examined in larger-scale and prospective studies. Conclusions This study proposes an optimized case-mix–based per-diem payment scheme for inpatient mental health services by systematically identifying key grouping variables associated with inpatient cost per diem and applying the E-CHAID decision tree for case-mix classification. The findings indicate that length of stay, hospital level, insurance type, and number of comorbidities are the primary splitting variables for constructing case-mix groups under per-diem payment. Based on a large sample, the developed case-mix system classifies inpatients with mental disorders into 10 groups with relatively homogeneous per-diem costs, effectively distinguishing different levels of resource consumption. This study provides important evidence for developing a per-diem payment case-mix system that aligns with the characteristics of mental health services in China and offers strategic guidance for strengthening inpatient cost management and reducing the financial burden on patients. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Chongqing Medical University and was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the Institutional Review Board waived the requirement for informed consent. Consent for publication Not applicable. Competing Interests The authors declare no competing interests. Funding This work was supported by the Chongqing Municipal Health Commission (Grant No. 2024120007). Author Contribution Yongqi Han conducted the main data analysis and drafted the manuscript. Xin Xiong and Menghan Zhang contributed to preliminary data analysis. Bo Yan conceived the study, supervised the research process, and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript. Acknowledgments We would like to express our sincere gratitude to all institutions that provided the data for this study. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable reques References World Health Organization. Mental disorders. 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Wuhan (CN): Huazhong University of Science and Technology; 2021. Wolff J, McCrone P, Koeser L, Normann C, Patel A. Cost drivers of inpatient mental health care: a systematic review. Epidemiol Psychiatr Sci. 2015;24(1):78–89. Ki Y, McAleavey AA, Moger TA, Moltu C. Cost structure in specialist mental healthcare: what are the main drivers of the most expensive episodes? Int J Ment Health Syst. 2023;17(1):37. Ma Y, Tu X, Luo X, Hu L, Wang C. Machine-learning-based cost prediction models for inpatients with mental disorders in China. BMC Psychiatry. 2025;25(1):33. Li J, Du H, Dou F, Yang C, Zhao Y, Ma Z, et al. A study on the changing trend and influencing factors of hospitalization costs of schizophrenia in economically underdeveloped areas of China. Schizophrenia (Heidelb). 2023;9(1):4. Zhu Y, Li X, Zhao M. Promotion of Mental Health Rehabilitation in China: Community-Based Mental-Health Services. Consort Psychiatr. 2020;1(2):21–7. Zhu Y, He S, Liu Y, Chen C, Ge X, Zhang W, et al. Shanghai Community-Based Schizophrenia Cohort (SCS): a protocol for establishing a longitudinal cohort and research database of patients with schizophrenia receiving community-based mental health treatment. BMJ Open. 2024;14(4):e079312. Ajiguli A, Jiang L, Di N, Chen X. Analysis of hospitalization expenses of mental diseases in Xinjiang Uygur Autonomous Region from 2016 to 2020. Chin J Soc Med. 2024;41(3):361–4. Tu Q. Study on the influencing factors and case-mix of per-diem cost of hospitalized rehabilitation patients with mental disorders. [master’s thesis]. Beijing (CN): Peking Union Medical College; 2024. Yuan J, Yin Y, Yu Y, Zhao H, Gao S, Ning L, et al. Medical costs and related factors associated with mental disorders in Jilin Province, China, 2020–2022. BMC Health Serv Res. 2025;25(1):1145. Yan Y, Tu Y. The impact of China's urban and rural economic revitalization on the utilization of mental health inpatient services. Front Public Health. 2023;10:1043666. Fu L, Pei T, Xu J, Han J, Yang J. Inspecting the health poverty trap mechanism: self-reinforcing effect and endogenous force. BMC Public Health. 2024;24(1):917. Zhang Y, He Y, Wang Q, Meng Y, Xia X, Ji X, et al. Disparities in inpatient treatment and expenditures among lung cancer patients under tiered social health insurance: a population-based study in China. Int J Equity Health. 2025;24(1):163. Christensen MK, McGrath JJ, Momen N, Weye N, Agerbo E, Pedersen CB, et al. The health care cost of comorbidity in individuals with mental disorders: A Danish register-based study. Aust N Z J Psychiatry. 2023;57(6):914–22. Simon J, Wienand D, Park AL, Wippel C, Mayer S, Heilig D, et al. Excess resource use and costs of physical comorbidities in individuals with mental health disorders: A systematic literature review and meta-analysis. Eur Neuropsychopharmacol. 2023;66:14–27. Huang Q, Xin Y, Zhu J, Dong Y, Chen Y, Chen C, et al. Impact of somatic comorbidities on healthcare costs in patients with severe mental disorders: a cross-sectional study in Beijing. BMC Psychiatry. 2025;25(1):1128. Tran N, Poss JW, Perlman C, Hirdes JP. Case-Mix Classification for Mental Health Care in Community Settings: A Scoping Review. Health Serv Insights. 2019;12:1178632919862248. Stewart SL, Celebre A, Semovski V, Hirdes JP, Vadeboncoeur C, Poss JW. The interRAI Child and Youth Suite of Mental Health Assessment Instruments: An Integrated Approach to Mental Health Service Delivery. Front Psychiatry. 2022;13:710569. Eagar K, Gaines P, Burgess P, Green J, Bower A, Buckingham B, et al. Developing a New Zealand casemix classification for mental health services. World Psychiatry. 2004;3(3):172–7. Additional Declarations Competing interest reported. The authors declare no competing interests. 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The authors declare no competing interests.","formattedTitle":"Optimization of case-mix for per-diem payment of mental disorders based on the E-CHAID decision tree","fulltext":[{"header":"Background","content":"\u003cp\u003eIn recent years, the global burden of mental disorders has become increasingly severe. According to the latest estimates of the World Health Organization, more than 1.1\u0026nbsp;billion people worldwide\u0026mdash;approximately one in seven of the global population\u0026mdash;are affected by mental disorders, which accounted for about 155\u0026nbsp;million disability-adjusted life years (DALYs) in 2021[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In addition, depression and anxiety alone are estimated to cause global economic losses of approximately US\u003cspan\u003e$\u003c/span\u003e1 trillion annually[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The burden of mental disorders in China is similarly alarming. Data from the China Mental Health Survey (CMHS) indicate that the lifetime prevalence of mental disorders in China reaches 16.57%, with a 12-month prevalence of approximately 9.32%[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2021, the number of incident cases of mental disorders in China was estimated at 54.62\u0026nbsp;million, and mental disorders were responsible for 23.20\u0026nbsp;million person-years of DALYs[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Furthermore, in 2018, total treatment expenditures for mental and psychological disorders in China amounted to 87.168\u0026nbsp;billion yuan, accounting for 2.47% of total national disease-related healthcare expenditures and approximately 0.09% of gross domestic product. Of the total treatment costs for mental disorders, 31.8% were borne by households, and 48.3% of caregivers of patients with mental disorders experienced substantial financial burden[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. These findings indicate that mental disorders impose a profound economic burden not only on affected individuals but also on their families and society as a whole. Therefore, it is imperative for the Chinese government to implement effective measures to control healthcare expenditures, and among the policy instruments for containing mental healthcare costs and improving the efficiency of resource utilization, the selection and design of health insurance payment mechanisms play a pivotal role.\u003c/p\u003e \u003cp\u003eAt present, China\u0026rsquo;s health insurance payment system has substantially entered a new stage dominated by prospective payment, with multiple disease categories being covered under diagnosis-related groups (DRGs). For inpatient care of mental disorders, China mainly adopts case-based payment (such as DRG-based payment) and per-diem payment; however, DRG-based payment is not well suited to mental disorders, and the rate-setting for per-diem payment remains relatively crude[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. For example, in regions such as Beijing, per-diem payment rates are set solely on the basis of hospital level, resulting in weak alignment between payment levels and patients\u0026rsquo; clinical characteristics, and thus failing to adequately reflect the healthcare needs and resource consumption of inpatients with mental disorders[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research on refined case-mix\u0026ndash;based per-diem payment systems for mental disorders in China remains limited. Most existing studies are confined to preliminary designs based on a narrow set of grouping variables or to policy feasibility discussions grounded in international experience and theoretical frameworks[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral Western countries have explored refined case-mix\u0026ndash;based per-diem payment systems for inpatients with mental disorders, representing a transition from extensive payment models toward more patient-centered, finely stratified reimbursement mechanisms. In Switzerland, the mental health insurance pricing system (Tariff Psychiatry, TARPSY) is implemented, whereby cases are first classified into nine basic psychiatric cost groups (APCG) according to the principal diagnosis, and then further subdivided into 23 psychiatric cost groups (PCG) based on patient characteristics such as clinical complexity, secondary diagnoses, and age. The daily reimbursement amount is calculated as the product of a daily weight and a hospital base rate and is ultimately paid on a per-diem basis[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Germany applies the payment system for psychiatric and psychosomatic institutions (Pauschalierende Entgeltsystem f\u0026uuml;r Psychiatrische und Psychosomatische Einrichtungen, PEPP), which, similar in nature to Switzerland\u0026rsquo;s TARPSY system, constitutes a refined case-mix\u0026ndash;based per-diem payment model, although the grouping structures and criteria differ between the two systems. The PEPP system adopts a three-level grouping structure: cases are first classified into 10 structural categories (Struktur kategorien, SK) according to the type of inpatient care; within each SK, 24 basic PEPP groups (Basis-PEPP) are formed based on the ICD-10-GM principal diagnosis or specific procedures; finally, cases are further stratified into 84 specific PEPP groups according to factors associated with resource consumption, including age, comorbidities and complications, and treatment intensity. Each group is assigned a fixed daily reimbursement rate, with payments decreasing stepwise according to length of stay[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In the United States, inpatient psychiatric services are reimbursed under the Inpatient Psychiatric Facility Prospective Payment System (IPF PPS), which adopts a per-diem payment approach and does not directly apply case grouping; instead, the base per-diem rate is adjusted using coefficients reflecting regional differences, age, comorbidities, length of stay, and diagnosis-related group classifications, thereby generating differentiated daily payment standards[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, the Patient-Driven Payment Model (PDPM), as another per-diem\u0026ndash;based payment approach in the United States, also draws on case-mix principles by establishing different per-diem payment rates for different patient groups[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn view of the limitations of existing research in China and drawing on international experience, this study utilizes large-scale inpatient data of patients with mental disorders and applies the E-CHAID decision tree method to construct and validate a refined case-mix\u0026ndash;based per-diem payment system for inpatients with mental disorders. The proposed framework is expected to provide empirical evidence for optimizing per-diem payment policies for mental disorders in China, enhance the rationality and precision of payment standards, promote the efficient allocation of healthcare resources, and thereby alleviate patients\u0026rsquo; financial burden while curbing the unreasonable growth of medical expenditures.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Selection\u003c/h2\u003e \u003cp\u003eThe data for this study were obtained from hospital discharge records of inpatients at two specialized psychiatric hospitals in Chongqing, China, covering the period from January 1, 2020, to December 31, 2024. Patients were included if they were diagnosed with mental disorders according to the International Classification of Diseases, 10th Revision (ICD-10), with ICD-10 codes ranging from F00 to F99. Patients were excluded if their hospitalization cost records contained evident logical errors or if the length of stay was less than one day. A total of 25,510 inpatients with mental disorders were finally included, and information on demographic characteristics, clinical services, and medical expenditures was collected. To eliminate the impact of price fluctuations and improve the comparability of inpatient cost data across different years, inpatient cost per diem from 2020 to 2024 was adjusted using the Consumer Price Index (CPI) for healthcare in Chongqing, China, with 2024 as the base year. CPI data were obtained from the Statistical Yearbook of the National Bureau of Statistics of China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eFirst, differences in inpatient cost per diem across patient subgroups with binary and multicategorical characteristics were examined using the Mann\u0026ndash;Whitney U test or the Kruskal\u0026ndash;Wallis H test, as appropriate. Second, based on the results of the univariate analyses, variables showing statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were entered into multivariable linear regression models to identify the main factors associated with inpatient cost per diem. Finally, inpatient cost per diem was specified as the dependent variable, and factors with significant effects on inpatient cost per diem were used as splitting variables to classify patients by applying the E-CHAID decision tree method.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCase-Mix Classification Methods\u003c/h3\u003e\n\u003cp\u003eIn the case-mix classification, this study used the Exhaustive Chi-squared Automatic Interaction Detection (E-CHAID) model for decision tree analysis. E-CHAID is an improved version of the Chi-squared Automatic Interaction Detection (CHAID). Its grouping principle is based on the relationships between the target variable and predictor variables, and samples are automatically grouped in multidimensional contingency tables according to the significance levels of chi-squared tests. A major advantage of E-CHAID is its ability to handle nonlinear data and allow for a certain degree of missing values, which helps overcome the limitations of traditional parametric tests[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, compared with CHAID, E-CHAID applies more thorough procedures for variable merging and grouping, which supports more accurate identification of splitting variables[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The parameters for decision tree growth were set as follows: a maximum depth of three levels, a minimum of 100 cases in each parent node, a minimum of 50 cases in each child node, and a significance level of α\u0026thinsp;=\u0026thinsp;0.05 for node splitting.\u003c/p\u003e\n\u003ch3\u003eEvaluation Metrics\u003c/h3\u003e\n\u003cp\u003eThe nonparametric Kruskal\u0026ndash;Wallis H test and the Reduction in Variance (RIV) were used to assess between-group heterogeneity, while the coefficient of variation (CV) was used to evaluate within-group homogeneity. RIV was calculated as (total sum of squared deviations\u0026thinsp;\u0026minus;\u0026thinsp;sum of squared deviations within all subgroups) divided by the total sum of squared deviations, and CV was calculated as the standard deviation divided by the mean. For the grouping model, a statistically significant Kruskal\u0026ndash;Wallis H test, together with a larger RIV and a smaller CV, indicates greater between-group variation and smaller within-group variation, reflecting better grouping performance. In this study, RIV\u0026thinsp;\u0026gt;\u0026thinsp;40% and CV\u0026thinsp;\u0026lt;\u0026thinsp;1 were adopted as the criteria for evaluating grouping results[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. All analyses were conducted using SPSS version 27.0, and the significance level was set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eCalculation of Per-Diem Payment Standards, High-Cost Cases, and Disease Weights\u003c/h3\u003e\n\u003cp\u003eTo reduce the influence of extreme values, the median inpatient cost per diem within each group was used as the per-diem payment standard for that group. The upper cost limit was defined as the 75th percentile of the inpatient cost per diem plus 1.5 times the interquartile range (P75\u0026thinsp;+\u0026thinsp;1.5IQR) to identify extremely high-cost cases, and cases exceeding this limit were classified as outliers. The disease weight was calculated as the ratio of the mean inpatient cost per diem of a given case-mix group to the mean inpatient cost per diem of all cases; a higher weight indicates greater consumption of healthcare resources.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eBaseline characteristics of patients\u003c/h2\u003e \u003cp\u003eA total of 25,510 inpatients with mental disorders were included in this study. The length of stay was mainly distributed within \u0026le;\u0026thinsp;30 days (56.12%) and 31\u0026ndash;60 days (18.81%). Most patients were treated in tertiary medical institutions (92.5%). In terms of health insurance coverage, the Urban and Rural Resident Basic Medical Insurance accounted for the largest proportion (64.92%). In addition, 55.69% of patients had at least one comorbidity. Other characteristics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The results of univariate analysis showed that sex, age, insurance type, admission mode, discharge mode, source of admission, hospital level, readmission plan, inclusion in clinical pathways, surgical status, number of comorbidities, medical payment method, number of hospitalizations, length of stay, and principal diagnosis were significantly associated with differences in hospitalization costs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of inpatients with mental disorders and univariate analysis of inpatient cost per diem\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInpatient cost per diem\u003c/p\u003e \u003cp\u003eM(P\u003csub\u003e25\u003c/sub\u003e-P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eZ/H\u003c/em\u003e值\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-43.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10974(43.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e443.69(306.69, 600.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14536(56.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e561.58(444.81, 706.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2919.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5143(20.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e636.10(520.08, 777.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8165(32.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e531.37(672.70, 412.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4732(18.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e455.72(304.44, 612.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4696(18.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e457.25(315.20, 597.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2774(10.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e426.99(318.67, 558.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-40.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Employee Basic Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8950(35.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e442.15(349.27, 535.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban and Rural Resident Basic Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16560(64.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e572.57(421.68, 714..45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdmission mode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0079\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eoutpatient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25326(99.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e516.39(378.97, 668.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eemergency department\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177(0.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e564.51(444.75, 696.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etransfer from other medical institutions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7(0.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e601.64(187.46, 661.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCondition on admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egeneral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25476(99.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e516.68(379.26, 668.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34(0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e597.35(478.20, 734.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDischarge mode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e644.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edischarge against medical advice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1078(4.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e690.27(539.85, 860.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edischarge with medical advice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24348(95.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e510.13(372.89, 658.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etransfer to another hospital/community with medical advice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e624.24(386.21, 845.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edeath\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(0.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e246.99(164.61, 323.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56(0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e721.63(556.67, 937.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-12.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elocal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24194(94.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e512.30(373.49, 664.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enon-local\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1316(5.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e575.90(471.71, 711.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-68.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esecondary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1912(7.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137.28(109.39, 220.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etertiary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23598(92.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e535.46(414.44, 682.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReadmission plan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e430.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24,182(94.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e520.91(386.37, 671.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereadmission plan within 7 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e591(2.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e336.09(309.64, 374.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereadmission plan within 31 days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e737(2.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e536.21(424.26, 687.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInclusion in clinical pathways\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-22.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20,725(81.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e503.18(357.39, 655.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eyes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,785(18.76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e570.41(450.91, 717.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-55.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20205(79.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e474.04(340.69, 620.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelectroconvulsive therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5305(20.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e649.07(552.26, 761.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e721.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,303(44.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e560.03(408.66, 710.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enumber of comorbidities\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5520(21.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e529.47(410.99, 663.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enumber of comorbidities\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,084(12.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e497.34(384.95, 632.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enumber of comorbidities\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,603(21.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e437.76(332.13, 566.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical payment method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2609.949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Employee Basic Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7537(29.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e445.85(349.06, 566.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban and Rural Resident Basic Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11614(45.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e508.33(333.89, 663.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efully self-paid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5933(23.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e624.56(522.00, 767.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efully publicly funded\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e449.82(342.85, 704.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e415(1.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e433.34(338.20, 567.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of hospitalizations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5272.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20140(78.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e522.70(437.92, 701.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2713(10.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e431.58(309.74, 570.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2657(10.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e242.87(131.51, 319.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12672.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,317(56.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e625.65(509.31, 773.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31-60Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4799(18.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e484.03(410.85, 572.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61\u0026ndash;90 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,350(5.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e420.09(353.44, 489.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e91\u0026ndash;120 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e560(2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364.51(296.31, 431.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e121\u0026ndash;150 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e517(2.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e281.84(197.87, 368.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;151 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3967(15.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e283.09(185.75, 319.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrincipal diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4343.333\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF00-F09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1561(6.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e432.30(322.68, 577.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF10-F19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e867(3.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e401.32(289.60, 537.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF20-F29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10324(40.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e429.05(301.40, 574.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF30-F39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7996(31.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e597.91(483.65, 732.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF40-F49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1727(6.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e567.61(467.26, 703.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF50-F59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e62(0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e589.88(464.43, 739.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF60-F69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108(0.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e507.86(371.98, 685.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF70-F79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e348(1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e368.61(187.50, 536.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF80-F89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e621.97(529.23, 867.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF90-F98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2449(9.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e619.97(514.61, 751.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF99-F99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50(0.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e275.70(165.84, 786.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariable linear regression analysis\u003c/h3\u003e\n\u003cp\u003eUsing the log-transformed inpatient cost per diem as the dependent variable, a multivariable linear regression analysis was performed with the 15 variables that were statistically significant in the univariate analysis as independent variables. The results 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, and principal diagnosis were significantly associated with the dependent variable (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Collinearity diagnostics indicated a mean variance inflation factor (VIF) of 2.153, with all VIF values below 3, suggesting no evidence of multicollinearity among the variables. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of multivariable linear regression analysis of inpatient cost per diem among patients with mental disorders\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eunstandardized coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTolerance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVIF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e5.391\u0026thinsp;\u0026plusmn;\u0026thinsp;0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(5.344, 5.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e222.736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.035\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.026, 0.043)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.006\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.010, -0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.822\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.139\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.129, 0.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCondition on admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.056\u0026thinsp;\u0026plusmn;\u0026thinsp;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.053, 0.165)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDischarge mode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.071\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.087, -0.055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-8.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource of admission\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.005\u0026thinsp;\u0026plusmn;\u0026thinsp;0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.014, 0.023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHospital level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.995\u0026thinsp;\u0026plusmn;\u0026thinsp;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.975, 1.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e94.917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReadmission plan\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.020\u0026thinsp;\u0026plusmn;\u0026thinsp;0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.009, 0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInclusion in clinical pathways\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.036\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.026, 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSurgical status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.167\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.157, 0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of comorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.012\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.008, 0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.490\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedical payment method\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.003, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of hospitalizations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.114\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.105, 0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.325\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLength of stay\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e-0.155\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(-0.158, -0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.522\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-96.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrincipal diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.000, 0.005)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of the decision tree\u0026ndash;based case-mix grouping scheme\u003c/h2\u003e \u003cp\u003eUsing inpatient cost per diem as the dependent variable, the statistically significant factors identified in the multivariable analysis\u0026mdash;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, and principal diagnosis\u0026mdash;were entered into the decision tree model as independent variables. In the final model, length of stay, hospital level, insurance type, and number of comorbidities were selected as the splitting variables, resulting in a total of 10 case-mix groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). After grouping, the Kruskal\u0026ndash;Wallis H test was applied to examine differences in inpatient cost per diem among groups. The results showed significant differences in cost distributions across the 10 groups (H\u0026thinsp;=\u0026thinsp;14,014.074, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating good between-group heterogeneity and statistically significant group separation. In addition, the calculated RIV value was 0.43, further supporting substantial between-group heterogeneity. The CV values of the case-mix groups ranged from 0.20 to 0.59, indicating small within-group variation and satisfactory grouping performance. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCase-mix classification of inpatients with mental disorders in China\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroup description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eFrequency(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 Days、Urban Employee Basic Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4016(15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e594.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e291.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;30 Days, Urban and Rural Resident Basic Medical Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10301(40.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e719.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e328.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;60 Days、61\u0026ndash;90 Days、91\u0026ndash;120 Days, tertiary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6269(24.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e483.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31\u0026ndash;60 Days、61\u0026ndash;90 Days、91\u0026ndash;120 Days, secondary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e440(1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e141.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u0026ndash;150 Days, \u0026ge;\u0026thinsp;151 Days, tertiary hospital, no comorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e467(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e241.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u0026ndash;150 Days, \u0026ge;\u0026thinsp;151 Days, tertiary hospital, number of comorbidities\u0026thinsp;\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1771(6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e322.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e65.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u0026ndash;150 Days, \u0026ge;\u0026thinsp;151 Days, tertiary hospital, number of comorbidities\u0026thinsp;=\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e499(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e279.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u0026ndash;150 Days, \u0026ge;\u0026thinsp;151 Days, tertiary hospital, number of comorbidities\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e478(1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e298.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e74.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esecondary hospital, \u0026ge;\u0026thinsp;151 Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1010(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e155.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121\u0026ndash;150 Days, secondary hospital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e259(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e197.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e84.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eInpatient cost per diem, high-cost cases, and weights of each case-mix group\u003c/h2\u003e \u003cp\u003eAfter grouping inpatients with mental disorders using the decision tree, the per-diem payment standards, high-cost cases, and weights for each group were further calculated. The results showed that the per-diem payment standards ranged from 120.06 to 658.16 yuan. A total of 1,730 patients had inpatient cost per diem exceeding the cost control threshold, accounting for 6.78% of all cases. Group 2 had the highest weight, indicating that patients in this group were more complex and had higher levels of healthcare resource utilization. The detailed results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInpatient cost per diem, high-cost cases, and weights of each case-mix group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePer-Diem Payment Standards\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUpper Limit (P75\u0026thinsp;+\u0026thinsp;1.5IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOutlier(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510.7316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e904.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325(8.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e658.1628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1047.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e881(8.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.304\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e469.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e709.8118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e237(3.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e120.0552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e158.0294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62(14.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e190.1968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e417.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e314.3028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e394.3604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172(9.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e292.9374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e469.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.506\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e305.6115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e407.0426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(4.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e229.8282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e439.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.4093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e290.6115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(0.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.282\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study show that the E-CHAID decision tree model classified inpatients with mental disorders into 10 case-mix groups using four splitting variables, with length of stay serving as the primary splitting variable. As length of stay increased, inpatient cost per diem decreased, which is consistent with previous studies[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This relationship may reflect the typical pattern of inpatient care for mental disorders, characterized by more intensive resource use in the early stage and a gradual stabilization in later stages[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. During the initial phase of hospitalization, patients are often in the acute stage, and diagnostic tests, treatments, and intensive nursing care are concentrated, resulting in higher daily costs. As patients enter the stabilization and rehabilitation phases, care focuses on maintenance and functional recovery, with fewer new treatment services and lower testing frequency, leading to a progressive decline in daily resource use [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, using length of stay as the primary splitting variable allowed the E-CHAID model to stratify patients into relatively homogeneous per-diem cost categories corresponding to the early, middle, and late stages of hospitalization. This provides empirical evidence for the development of phased and differentiated per-diem payment standards.\u003c/p\u003e \u003cp\u003eHospital level is selected as one of the second-level splitting variables, and inpatient cost per diem is higher among patients treated in higher-level medical institutions, which is consistent with previous studies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This is because higher-level medical institutions usually undertake the treatment of patients with mental disorders in the acute phase and more complex cases, including those with acute agitation, severe behavioral problems, or concomitant physical risks, whereas patients with relatively stable conditions are more often hospitalized for treatment or rehabilitation management in lower-level medical institutions[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Under this care structure, inpatients with mental disorders treated in higher-level medical institutions tend to have higher overall risk and greater management complexity. Accordingly, the inpatient management of these patients requires more intensive medical and nursing input and a broader range of therapeutic and monitoring services. In addition, higher-level medical institutions generally have more specialized staff and more advanced technical capacity, which are associated with higher treatment costs. Together, these factors increase daily resource consumption among inpatients with mental disorders in higher-level medical institutions.[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In the existing design of per-diem payment for mental disorders, hospital level has been incorporated into the payment standard system [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This indicates that differences in service capacity and treatment costs across institutional levels are regarded as important determinants of patient resource use under per-diem payment. For example, cities such as Xuzhou and Ningbo set per-diem payment standards for mental disorders according to hospital level. Therefore, when constructing a case-mix\u0026ndash;based per-diem payment system for mental disorders, incorporating hospital level as a case-mix grouping variable in the model design can improve the level of refinement of the per-diem payment system..\u003c/p\u003e \u003cp\u003eBased on the initial stratification by length of stay and hospital level, the E-CHAID decision tree further selects insurance type and number of comorbidities to refine the classification of inpatients with mental disorders. Patients covered by the Urban and Rural Resident Basic Medical Insurance have higher inpatient cost per diem than those covered by the Urban Employee Basic Medical Insurance, a pattern that is also reported in previous studies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is mainly because the population covered by the Urban and Rural Resident Basic Medical Insurance includes a higher proportion of older adults and individuals with lower socioeconomic status, who are more likely to delay seeking care and be hospitalized after disease progression, resulting in greater treatment difficulty and higher per-diem costs[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In addition, reimbursement levels for Urban and Rural Resident Basic Medical Insurance beneficiaries in China are generally lower, which further contributes to higher daily costs in this group[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the inpatient cost per diem for Node 9 (Urban and Rural Resident Basic Medical Insurance) is 719.12 yuan, which is higher than the mean inpatient cost per diem of 594.345 yuan for Node 7 (Urban Employee Basic Medical Insurance). When these two nodes are further subdivided in the case-mix classification, differences in healthcare-seeking behavior and insurance coverage associated with insurance type are reflected in the corresponding per-diem payment standards and weights. The number of comorbidities is also selected as a splitting variable, and patients with more comorbidities have higher inpatient cost per diem. This is because when patients with mental disorders present with additional diseases, hospitals increase diagnostic testing, medication use, and nursing input, and inpatient cost per diem rises as the number of comorbidities increases[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Studies from multiple countries have confirmed that comorbid physical conditions in patients with mental disorders lead to additional healthcare resource use and cost burden[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, the impact of comorbidities on inpatient cost per diem should be fully considered when implementing refined per-diem payment for mental disorders, This may help avoid inappropriate case-mix classification that could encourage hospitals to preferentially admit patients with milder conditions while discouraging the admission of patients with more severe conditions. In addition, classifying comorbidities not only by number but also by different types may help to more accurately characterize differences in per-diem resource consumption among inpatients with mental disorders.\u003c/p\u003e \u003cp\u003eThe case-mix classification system developed in this study demonstrates good statistical performance. With only 10 groups and a total of 25,510 inpatients with mental disorders included, the reduction in variance (RIV) reaches 0.43, which is higher than that reported for many existing case-mix classification systems. A scoping review by Tran et al. on case-mix classification systems in community mental health shows that although various models have been developed in this field, most systems still have limited explanatory power for mental health resource use. Among the few better-performing models, the Australian Mental Health Classification and Costing System (AMHCC) defines 46 groups in community settings and achieves an RIV of 0.266 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, the RIV observed in this study is also higher than that of the case-mix classification system jointly developed by interRAI and the Arkansas Department of Human Services for resource allocation in child and adolescent mental health (ChYRI), which includes eight groups and reports an RIV of 0.30[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, it remains lower than the system constructed by Eagar et al. based on mental health service data in New Zealand, which reports an RIV of 0.67[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Compared with the international findings described above, the results of this study are generally reasonable but still require further improvement. Finally, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, establishing scientifically sound per-diem payment standards and cost control thresholds for each case-mix group not only helps to optimize health insurance payment incentive mechanisms and regulate healthcare service delivery, but also strengthens the management of high-cost cases and promotes more appropriate control of inpatient expenditures.\u003c/p\u003e \u003cp\u003eHowever, this study has several limitations. First, the data used in this analysis are derived from two hospitals in Chongqing; therefore, caution is required when generalizing the findings to the national level, and further studies conducted on a larger scale and across different regions are needed for validation. Second, due to the limited availability of medical record data, clinical severity indicators or functional assessment measures could not be included, and patient complexity is mainly characterized indirectly by variables such as length of stay and number of comorbidities, which may not fully reflect the true clinical condition of mental disorders. Third, this study adopts a retrospective design and is therefore unable to evaluate the dynamic impact of implementing the refined case-mix\u0026ndash;based per-diem payment model on cost containment; the relevant conclusions should be further examined in larger-scale and prospective studies.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study proposes an optimized case-mix\u0026ndash;based per-diem payment scheme for inpatient mental health services by systematically identifying key grouping variables associated with inpatient cost per diem and applying the E-CHAID decision tree for case-mix classification. The findings indicate that length of stay, hospital level, insurance type, and number of comorbidities are the primary splitting variables for constructing case-mix groups under per-diem payment. Based on a large sample, the developed case-mix system classifies inpatients with mental disorders into 10 groups with relatively homogeneous per-diem costs, effectively distinguishing different levels of resource consumption. This study provides important evidence for developing a per-diem payment case-mix system that aligns with the characteristics of mental health services in China and offers strategic guidance for strengthening inpatient cost management and reducing the financial burden on patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was approved by the Ethics Committee of Chongqing Medical University and was conducted in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study, the Institutional Review Board waived the requirement for informed consent.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Chongqing Municipal Health Commission (Grant No. 2024120007).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYongqi Han conducted the main data analysis and drafted the manuscript. Xin Xiong and Menghan Zhang contributed to preliminary data analysis. Bo Yan conceived the study, supervised the research process, and critically revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere gratitude to all institutions that provided the data for this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable reques\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Mental disorders. Available online at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/mental-disorders.2025\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/mental-disorders.2025\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed January 4, 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan Y, Fan A, Yang Z, Fan D. Global burden of mental disorders in 204 countries and territories, 1990\u0026ndash;2021: results from the global burden of disease study 2021. BMC Psychiatry. 2025;25(1):486.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Over a billion people living with mental health conditions-services require urgent scale-up. 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Front Psychiatry. 2022;13:710569.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEagar K, Gaines P, Burgess P, Green J, Bower A, Buckingham B, et al. Developing a New Zealand casemix classification for mental health services. World Psychiatry. 2004;3(3):172\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"E-CHAID, Decision tree, Mental disorders, Per diem payment, Case-mix","lastPublishedDoi":"10.21203/rs.3.rs-8599747/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8599747/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eMental disorders have imposed a substantial economic burden on patients, families, and society in China. Although per-diem payment can effectively contain healthcare expenditures, the current per-diem payment standards for mental disorders remain relatively crude and fail to reflect differences in actual resource consumption among patients with different characteristics. Therefore, this study analyzes the factors associated with inpatient cost per diem among inpatients with mental disorders to provide evidence for developing a refined case-mix\u0026ndash;based per-diem payment system suitable for mental health services in China.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 25,510 inpatients with mental disorders admitted to two hospitals in Chongqing between 2020 and 2024 were included. Their demographic, clinical, and cost information were collected. Univariate and multivariable linear regression analyses were performed to identify the main factors associated with inpatient cost per diem, and an E-CHAID decision tree was applied to construct a case-mix classification model for per-diem payment in mental health services.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe multivariable linear regression results showed that sex, age, insurance type, discharge mode, hospital level, readmission plan, inclusion in clinical pathways, surgical status, comorbidity status, number of hospitalizations, length of stay, and principal diagnosis were significantly associated with inpatient cost per diem. Using length of stay, hospital level, insurance type, and number of comorbidities as splitting variables, a total of 10 case-mix groups were generated. The grouping scheme demonstrated high reliability and robustness, with a reduction in variance (RIV) of 0.43 and coefficients of variation (CVs) below 1 across all groups (0.20\u0026ndash;0.59). In addition, the per-diem payment standards for the 10 groups ranged from 120.06 to 658.16 yuan.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLength of stay, hospital level, insurance type, and number of comorbidities are key factors for grouping inpatients with mental disorders, and the construction of a refined case-mix\u0026ndash;based per-diem payment system using the E-CHAID decision tree is a reasonable and appropriate approach.\u003c/p\u003e","manuscriptTitle":"Optimization of case-mix for per-diem payment of mental disorders based on the E-CHAID decision tree","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 16:36:36","doi":"10.21203/rs.3.rs-8599747/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T05:59:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T14:34:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92241481991909203603190895955166627993","date":"2026-03-10T09:37:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T03:40:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197019278943282607020258102594542161267","date":"2026-03-08T16:28:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-06T17:09:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"172361251328165030322060388949235317640","date":"2026-03-05T08:25:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37803848997660721792420549939973235998","date":"2026-03-03T14:50:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5078176266073873534491916770178248578","date":"2026-03-03T02:17:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"58528014638174425533006429412161776106","date":"2026-03-03T01:47:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-20T07:40:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-16T20:24:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168107150117554506295272552702637599750","date":"2026-02-16T08:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"277796554511922396717690451405599621524","date":"2026-02-16T05:53:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"56193506306810013325197732960111818976","date":"2026-02-07T05:03:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-06T10:31:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-19T08:46:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T11:13:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T11:06:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-01-14T08:47:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"328151ea-ad2c-4895-88c9-1267f47b63f6","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T11:38:38+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 16:36:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8599747","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8599747","identity":"rs-8599747","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}