ORIGINAL RESEARCH article

Front. Public Health, 24 July 2025

Sec. Public Health Policy

Volume 13 - 2025 | https://doi.org/10.3389/fpubh.2025.1617945

Evaluating medical service performance of hospitals in Sichuan Province, China: exploratory factor analysis and hierarchical clustering analysis based on diagnosis-related groups

  • 1. Department of Medical Administration, The First People’s Hospital of Neijiang, Neijiang, China

  • 2. School of Public Health, Chongqing Medical University, Chongqing, China

Abstract

Objective:

This study aims to evaluate hospital medical service performance in Sichuan Province, China.

Methods:

A total of 306 secondary and tertiary general hospitals were included in the analysis. A comprehensive evaluation model was developed using exploratory factor analysis (EFA) based on diagnosis-related groups (DGRS) indicators to assess medical service performance. Indicators were determined within the Donabedian structure-process-outcome (SPO) framework. Hierarchical clustering analysis (HCA) was applied to categorize hospitals into performance clusters, and the Kruskal-Wallis H test was used to compare disparities in performance characteristics across clusters.

Results:

The comprehensive evaluation revealed that all top 10 hospitals were tertiary general hospitals (TGHs), with 40.00% located in the Chengdu region. Conversely, the bottom 10 hospitals were exclusively secondary general hospitals (SGHs), predominantly concentrated in northeastern Sichuan. TGHs were classified into three clusters: “Excellent” (30.83%), “Middle” (57.14%), and “Inferior” (12.03%), while SGHs were categorized as “Excellent” (26.01%), “Middle” (69.94%), and “Inferior” (4.05%). For TGHs, the “Excellent” cluster displayed significantly higher performance in case-mix index (CMI), number of DRGS (ND), total weight (TW), and time efficiency index (TEI) compared to the “Middle” and “Inferior” clusters, but performed worst in cost efficiency index (CEI) and mortality of middle and low-risk group cases (MMLRG). For SGHs, “Excellent” cluster hospitals significantly outperformed others in ND and TW, while the “Inferior” cluster performed best in CMI but alarmingly worst in MMLRG.

Conclusion:

Significant regional and hierarchical disparities in medical service performance were observed across Sichuan Province, with Chengdu region demonstrating optimal performance. For TGHs, hospitals in the “Inferior” cluster are recommended to enhance their medical ability and efficiency compared to those in the “Excellent” cluster. Conversely, hospitals in the “Excellent” cluster should focus on controlling medical costs compared to those in the “Inferior” cluster. For SGHs, hospitals in the “Inferior” cluster should concentrate on improving medical security and ensuring patient safety compared to those in the “Middle” and “Excellent” clusters.

1 Introduction

The evaluation of medical service performance has emerged as a critical research focus, garnering substantial attention worldwide. Over the past few decades, extensive literature has explored various instruments for measuring hospital service quality (1). In 1966, American scholar Avedis Donabedian proposed the classic three-dimensional quality framework, comprising the dimensions of structure, process, and outcome (SPO) to assess healthcare quality (2, 3). In this framework, “structure” denotes the physical settings, provider qualifications, and administrative systems; “process” refers to the delivery components of care; and “outcome” encompasses recovery, functional restoration, and survival (3). Through continuous international scholarly exploration, the Donabedian model has deepened its theoretical connotations and become a globally recognized framework for healthcare quality assessment (2).

In China, scholars have applied the Donabedian framework to construct evaluation systems. For example, Wang et al. developed an index system for evaluating the core competencies of hospital specialist service operation assistants using SPO-based indicators (4). Cai et al. established a single-disease quality management system guided by the SPO model (5). Wang et al. clarified the model’s connotations in Chinese healthcare, defining “structure” as the static configuration and efficiency of institutional resources, “process” as the dynamic quality and efficiency of service operations, and “outcome” as the integrated measurement of structural and process quality (6). This framework has categorized medical ability and security indicators under “outcome” and efficiency indicators under “process” dimensions, forming a theoretical foundation for similar quality assessment studies.

China has implemented SPO-based hospital performance evaluation for years. A significant milestone was the 2019 State Council document entitled “Strengthening Performance Evaluation of Tertiary Public Hospitals” (7). This initiative focused on SPO dimensions relating to hospital management, such as sustainable development, operational efficiency, medical quality, and patient satisfaction (7). Indicators like inpatient workload per physician, physician-to-nurse ratio, and outpatient satisfaction were used to construct a comprehensive evaluation matrix (7). A notable feature of this approach is the use of isolated indicators with assigned weights to form the assessment system.

However, previous studies have identified limitations in using isolated indicators such as average cost, length of stay (LOS), mortality, work efficiency, and workload for performance evaluation (8–10). These indicators are inadequate and inappropriate due to healthcare’s complexity (9), diverse needs, and information asymmetry (11, 12). Such isolated indicators fail to capture the full spectrum of service quality (1, 13), raising concerns about comparability and comprehensiveness (10). One widely recognized approach is integrating risk adjustment into evaluation processes (8, 14). Diagnosis-related groups (DRGS), a patient classification system developed at Yale University in the 1970s, standardizes healthcare payment and performance assessment by grouping patients with similar clinical causes and treatments (10, 15, 16). A significant application is for performance evaluation. For instance, Vitikainen et al. used two different output grouping systems (Classic and FullDRG) to estimate hospital efficiency (17). Luo et al. utilized DRGS indicators, including case-mix index (CMI), number of DRGs (ND), total weight (TW), cost efficiency index (CEI), time efficiency index (TEI), and mortality of middle and low-risk group cases (MMLRG) to objectively evaluate inpatient performance among tertiary hospitals in Sichuan’s Panxi region (18).

Since its inception, numerous DRGS-based evaluation models have been developed. Jian et al. used CEI, TEI, CMI, and inpatient mortality of low-risk group cases (IMLRG) to evaluate inpatient service performance in Beijing (19). Liu et al. adopted CMI, ND, TW, CEI, TEI, and IMLRG to evaluate medical service performance for breast cancer patients in Henan Province (20). Lu et al. evaluated an organ transplant department using similar metrics (21). These models primarily compare inter-hospital performance via DRGS indicators themselves. Contrasts to previous studies, Liu et al. developed two models by combining principal component analysis (PCA), entropy, TOPSIS, and rank sum ratio (RSR) methods based on CMI, ND, TW, CEI, TEI, MMLRG, and hospital case fatality rate (SCFR) to evaluate TGHs performance (10).

Despite great advancements in hospital performance evaluation, literature review has revealed that no studies have integrated exploratory factor analysis (EFA) and hierarchical clustering analysis (HCA) methods based on DRGS indicators within the Donabedian theoretical framework to evaluate hospital medical service performance. Given this gap, our study introduces a novel model combining the two methods based on DRGS indicators within the Donabedian theoretical framework to assess 306 hospitals in Sichuan, China. The findings of this study may inform healthcare management and future research.

2 Methods

2.1 Hospital determination

This study focused on secondary general hospitals (SGHs) and tertiary general hospitals (TGHs) in Sichuan, China. Hospitals specializing in traditional Chinese medicine, traditional Chinese medicine and Western medicine hospitals, and specialized hospitals were excluded. To holistically assess the disparities in medical service performance across hospital levels, all SGHs and TGHs registered in the Sichuan Health Data Analysis and Decision Support Cloud Platform (SHDADSCP) (22) were selected as the study hospitals. This resulted in a total sample size of 306 hospitals, including 173 SGHs and 133 TGHs. These hospitals are distributed across all 21 municipalities in Sichuan. Detailed geographic and hospital-level distributions of the study hospitals are displayed in Figure 1.

Figure 1

2.2 Indicator selection

In 2014, the former Health and Family Planning Commission of Sichuan Province introduced the “Front Page of Medical Records (FPMR) (2014 edition)” (23), a standardized format for collecting inpatient medical data across the region. This data included demographic characteristics, diagnosis and treatment information, and medical expenses (24). In 2024, the SHDADSCP analyzed the FPMR data from 306 hospitals and developed a comprehensive evaluation matrix. This matrix consisted of three primary indicators and seven secondary indicators (22). In reference to relevant studies (23, 25, 26), this study selected six indicators for evaluation: CMI, ND, TW, CEI, TEI, and MMLRG. Among these, CMI, ND, and TW were considered positive indicators, while CEI, TEI, and MMLRG were negative indicators (27–29). Detailed explanations of these evaluation indicators are illustrated in Table 1.

Table 1

IndicatorsDimensions of indicatorsExplanations of indicatorsAttribute of indicators
CMI (x1)Medical ability indicatorsHigher CMI values in hospitals indicate their advanced medical techniques in treating critically ill and complex patients.Positive indicators
ND (x2)Higher ND values reflect a hospital’s capacity to provide a broader range of medical service.
TW (x3)Higher TW values suggest greater output of inpatient service.
CEI (x4)Medical efficiency indicatorsLower CEI values imply lower costs for treating similar diseases.Negative indicators
TEI (x5)Lower TEI values indicate shorter hospital stays for treating similar diseases.
MMLRG (x6)Medical security indicatorsHigher MMLRG values may suggest issues in the hospital’s clinical or management processes, as death in such cases is often closely related to errors in the clinical process.

Explanations of medical service performance evaluation indicators.

CMI, case-mix index; ND, number of DRGs; TW, total weight; CEI, cost efficiency index; TEI, time efficiency index; MMLRG, mortality of middle and low-risk group cases.

2.3 Medical service performance evaluation procedures

2.3.1 Data extraction and preparation

The data for this study were extracted from the SHDADSCP in the “Comprehensive Evaluation” section. All data were collected and organized in Excel format. The data preparation followed the following procedures:

  • 1 Data extraction: The original data matrix was labeled as Xij, where i = 1, 2, …, m and j = 1, 2, …, n. Here, m represents the number of evaluation indicators, and n represents the number of study hospitals.

  • 2 Data trends homogenization: The absolute negative indicators of CEI and TEI were homogenized using Equation 1. The relative negative indicator of MMLRG was homogenized using Equation 2 (30).

  • 3 Data standardization: To eliminate the influence of varying dimensions, all data were standardized using Equation 3. This step ensured that each indicator contributed equally to the analysis, regardless of its original scale or unit of measurement.

2.3.2 EFA procedures

EFA, initially developed by Charles Spearman in 1904, is a multivariate statistical method. It classifies multiple variables into a few common factors based on the correlations among the variables. The fundamental concept involves decomposing original variables into two components: one is a linear combination of common factors (CFs) that condense most of the information in the original variables, and the other is a special factor that exhibits no correlation with the CFs. The main purpose is to explore the underlying structure beneath extensive observed data and identify latent factors influencing these data (31).

Following data standardization, we conducted EFA using SPSS 27.0 software. The main procedures were as follows:

  • 1 Test data appropriateness. Before performing EFA, it is necessary to evaluate data appropriateness. Two commonly used metrics are the Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test of sphericity. Data are deemed appropriate for EFA when the KMO value exceeds 0.6 or 0.7 and Bartlett’s test yields a significance level below 0.05.

  • 2 Calculate communality values. Communality, denoted as , was calculated using Equation 4:

where i = 1,2,…,n.

  • 3 Extract CFs. The principal component analysis (PCA) method was used to determine the number of CFs. Factors with accumulative variance contribution (AVC) ≥ 85% (32) were extracted. The AVC was calculated using Equation 5:

where represents the eigenvalues of each indicator.

  • 4 Compute factor loading matrix. The factor loading matrix A was derived from the eigenvalues and their corresponding eigenvectors. The matrix was defined as Equation 6:

where represents the eigenvalue, represents the eigenvector, i = 1, 2, …, m; j = 1, 2, …, n; m represents the number of evaluation indicators, and n represents the number of study hospitals.

  • 5 Rotate CFs. To enhance the interpretability of each CF, rotation was performed using the varimax method. The rotated factor loading matrix visually reflects the contribution of each variable to the principal components. A larger absolute value of a variable’s loading coefficient on a specific CF implies a stronger correlation between the variable and that factor.

  • 6 Determine factor score functions. The factor score function was defined as Equation 7:

where represents the coefficients of CF scores, represents the standardized data matrix, and k represents the number of CFs.

  • 7 Calculate the evaluation scores for each study hospital. The comprehensive evaluation scores (CES) of each study hospital were calculated using Equation 8 and ranked accordingly:

where RVC represents the rotated variance contribution.

2.4 HCA procedures

HCA is a descriptive statistical method that groups original data into clusters by measuring distances between data points. The goal is to minimize intra-cluster heterogeneity and maximize inter-cluster heterogeneity. To account for inherent differences between hospital levels, SGHs and TGHs were clustered separately following these procedures:

  • Variable standardization. Prior to HCA, variables including CF1, CF2, CF3, and CES were standardized using the Z-score method.

  • Distance metric and clustering algorithm. The Squared Euclidean Distance was used to measure data point dissimilarity, and the Between-groups Linkage method served as the clustering algorithm.

  • Optimal cluster determination. The Silhouette Coefficient (SC) (33, 34) was initially used to identify the optimal number of clusters (K), theoretically set at the highest SC value. Professional interpretation was additionally incorporated to refine this determination. The final optimal number of clusters for HCA was three, as illustrated in Figure 2.

  • Cluster definition. Three clusters were defined by research members according to the average values of six evaluation indicators per cluster.

Figure 2

2.5 Statistical analysis

All statistical analyses were performed using SPSS 27.0. The normality of continuous variables was assessed using the Shapiro–Wilk test. Non-normally distributed variables were described using the median and interquartile range [M (IQR)]. To examine the characteristic disparities across clusters, the Kruskal-Wallis H test was applied to non-normally distributed variables. A p-value less than 0.05 was considered statistically significant.

3 Results

3.1 EFA results

The KMO test and Bartlett’s test of sphericity confirmed the data suitability for EFA, with a KMO value was 0.726 and significant Bartlett’s test results (p < 0.001). Three CFs were extracted according to the extraction principle. CF1 explained 48.502% of the total variance (rotated eigenvalue = 2.910), CF2 explained 19.052% (rotated eigenvalue = 1.143), and CF3 explained 18.085% (rotated eigenvalue = 1.085), collectively accounting for 85.639% of the cumulative variance. All communality values of the six indicators exceeded 0.790, with the largest in MMLRG (0.956), followed by TEI (0.918), CMI (0.874), ND (0.806), TW (0.794), and CEI (0.791), indicating strong representation of original indicators by the extracted CFs. Detailed variance contributions and eigenvalues are presented in Table 2.

Table 2

ComponentInitial eigenvaluesRotation sums of squared loading
EigenvalueVariance contribution rate (%)Cumulative variance contribution rate (%)EigenvalueVariance contribution rate (%)Cumulative variance contribution rate (%)
CF13.14752.44552.4452.91048.50248.502
CF21.24720.77973.2241.14319.05267.554
CF30.74512.41585.6391.08518.08585.639
CF40.4667.76693.405
CF50.2544.24197.646
CF60.1412.354100.000

Eigenvalues of the correlation matrix and variance contribution rate.

CF, common factor.

The rotated factor loading matrix showed that CF1 had high loadings on CMI (0.903), ND (0.823), and TW (0.804), reflecting hospital medical abilities, thus labeled as the medical ability factor. CF2 exhibited a dominant loading on TEI (0.931), labeled as the medical efficiency factor, while CF3 demonstrated a high loading on MMLRG (0.964), designated as the medical security factor. Factor loading details are presented in Table 3.

Table 3

IndicatorsFactor loading matrixRotated factor loading matrix
CF1CF2CF3CF1CF2CF3
CMI (x1)0.894−0.2470.1130.9030.214−0.113
ND (x2)0.8720.071−0.2000.8230.1590.323
TW (x3)0.866−0.0560.2000.8040.382−0.031
CEI (x4)−0.7680.3450.287−0.8690.183−0.054
TEI (x5)0.4340.6460.5590.1270.9310.188
MMLRG (x6)0.2390.801−0.5080.0360.1630.964

Factor loading matrix and rotated factor loading matrix.

CMI, case-mix index; ND, number of DRGs; TW, total weight; CEI, cost efficiency index; TEI, time efficiency index; MMLRG, mortality of middle and low-risk group cases.

Common factor scores for each study hospital were calculated based on the factor score coefficient matrix (Table 4):

Table 4

IndicatorsCF1CF2CF3
CMI (x1)0.3100.085−0.200
ND (x2)0.278−0.0830.261
TW (x3)0.2370.259−0.163
CEI (x4)−0.3690.377−0.084
TEI (x5)−0.1360.908−0.079
MMLRG (x6)−0.043−0.1100.932

Factor score coefficient matrix.

CMI, case-mix index; ND, number of DRGs; TW, total weight; CEI, cost efficiency index; TEI, time efficiency index; MMLRG, mortality of middle and low-risk group cases.

The CES for each study hospital was computed using the following function:

Ranking based on CES showed all top 10 hospitals were TGHs, with four located in Chengdu and one each in Luzhou, Suining, Deyang, Mianyang, and Nanchong. Conversely, the bottom 10 hospitals were all SGHs, predominantly concentrated in northeastern Sichuan (Guangyuan, Dazhou, Guang’an, Nanchong, Bazhong), with seven of the 10 located there. Top and the bottom 10 hospital details are in Table 5, and full EFA rankings for 306 study hospitals are in Table 6.

Table 5

Hospital codeHospital levelRegionCMINDTWCEITEIMMLRGCF1CF2CF3CESRanking
Top 10 hospitals
Hospital 1TertiaryChengdu1.81688423,760.081.210.770.055.20254.3696−2.23613.44281
Hospital 2TertiaryChengdu1.42719278,580.961.240.830.123.78402.5935−1.15922.47292
Hospital 18TertiaryLuzhou1.33713189,932.711.160.860.113.05541.7628−0.67401.97853
Hospital 3TertiaryChengdu1.24681126,680.291.020.820.062.26731.8086−0.35341.61024
Hospital 37TertiarySuining1.2688129,339.90.920.830.042.11741.8244−0.29691.54085
Hospital 22TertiaryDeyang1.19698109,574.310.930.830.022.00971.6686−0.15761.47476
Hospital 4TertiaryChengdu1.4459077,231.311.190.90.12.44190.7886−0.49561.45267
Hospital 26TertiaryMianyang1.16689127,757.070.880.850.072.01061.6718−0.27771.45058
Hospital 41TertiaryNanchong1.09701146,486.331.040.980.072.35610.5564−0.12101.43169
Hospital 48TertiaryYibin1.12692119,917.10.820.860.051.81561.6012−0.17931.345310
Bottom 10 hospitals
Hospital 193SecondaryYa’an0.45999960.481.050−1.92040.40690.1029−0.9749297
Hospital 257SecondaryGuangyuan0.64118702.370.691.491.25−0.4948−1.8251−1.4455−0.9902298
Hospital 286SecondaryDazhou0.722464,097.060.722.131.880.1958−3.0898−2.0586−1.0095299
Hospital 149SecondaryPanzhihua0.632325,289.820.61.791.24−0.4178−2.3381−1.2436−1.0179300
Hospital 276SecondaryGuang’an0.722271,873.110.611.223.22−0.3793−0.3583−4.1284−1.1653301
Hospital 266SecondaryNanchong0.812653,854.960.511.193.89−0.46620.2791−5.1682−1.2924302
Hospital 297SecondaryBazhong0.671401,010.520.721.93.39−0.0619−2.4777−4.2090−1.4732303
Hospital 239SecondaryZigong0.482830.160.373.110−2.0697−2.89430.1513−1.7821304
Hospital 164SecondaryGuangyuan0.85981,987.641.072.297.051.0135−2.8653−9.2218−2.0083305
Hospital 269SecondaryNanchong0.69801,233.740.61.116.61−0.59460.7846−8.9117−2.0427306

The information of the top 10 and the bottom 10 hospitals based on EFA.

Table 6

Hospital codeHospital levelRegionCF1CF2CF3CESRanking
Hospital 1TertiaryChengdu5.20254.3696−2.23613.44281
Hospital 2TertiaryChengdu3.78402.5935−1.15922.47292
Hospital 18TertiaryLuzhou3.05541.7628−0.67401.97853
Hospital 3TertiaryChengdu2.26731.8086−0.35341.61024
Hospital 37TertiarySuining2.11741.8244−0.29691.54085
Hospital 22TertiaryDeyang2.00971.6686−0.15761.47476
Hospital 4TertiaryChengdu2.44190.7886−0.49561.45267
Hospital 26TertiaryMianyang2.01061.6718−0.27771.45058
Hospital 41TertiaryNanchong2.35610.5564−0.12101.43169
Hospital 48TertiaryYibin1.81561.6012−0.17931.345310
Hospital 5TertiaryChengdu1.98730.87170.06071.331111
Hospital 42TertiaryNanchong2.16810.4517−0.04411.318112
Hospital 53TertiaryDazhou1.70001.07700.00221.201813
Hospital 6TertiaryChengdu1.71600.90650.07021.187314
Hospital 27TertiaryMianyang1.75910.79410.03121.178515
Hospital 40TertiaryLeshan1.74130.8727−0.07921.162616
Hospital 11TertiaryZigong1.62220.70460.19971.116717
Hospital 51TertiaryGuang’an1.38311.3595−0.05051.074018
Hospital 49TertiaryYibin1.60300.60010.10101.061919
Hospital 16TertiaryPanzhihua1.59050.7828−0.06941.059420
Hospital 7TertiaryChengdu1.56510.5220−0.00591.000521
Hospital 12TertiaryZigong1.59200.10630.32540.993322
Hospital 8TertiaryChengdu1.44970.48520.14270.958423
Hospital 9TertiaryChengdu1.6433−0.22350.34350.953024
Hospital 28TertiaryMianyang1.43850.37880.20600.941725
Hospital 46TertiaryMeishan1.28690.85090.11410.941326
Hospital 35TertiaryGuangyuan1.29740.57430.25470.915627
Hospital 10TertiaryChengdu1.44840.06660.24050.885328
Hospital 57TertiaryYa’an1.29600.42890.20680.872429
Hospital 65TertiaryLiangshan1.5367−0.55470.36130.822930
Hospital 58TertiaryBazhong1.3280−0.15970.19770.757931
Hospital 23TertiaryDeyang1.05800.48890.23900.757832
Hospital 61TertiaryZiyang1.1637−0.18990.44820.711133
Hospital 38TertiaryNeijiang1.1945−0.33690.48870.704434
Hospital 36TertiaryGuangyuan1.1283−0.17100.32220.668635
Hospital 29TertiaryMianyang0.96530.32740.19050.659236
Hospital 47TertiaryMeishan0.66331.08550.20120.658837
Hospital 43TertiaryNanchong1.0970−0.24050.36550.644738
Hospital 20TertiaryLuzhou1.1149−0.58300.58300.624639
Hospital 66TertiaryLiangshan1.1909−0.75960.44270.598840
Hospital 68TertiaryChengdu0.9853−0.32710.50670.592041
Hospital 54TertiaryDazhou0.82940.14850.42130.591342
Hospital 94TertiarySuining0.8878−0.08080.48370.586643
Hospital 24TertiaryDeyang0.8616−0.00870.39180.568444
Hospital 25TertiaryDeyang0.76220.27360.34620.565245
Hospital 14TertiaryZigong0.81020.13540.33570.559546
Hospital 15TertiaryZigong1.0301−0.48850.38850.556647
Hospital 117TertiaryGuang’an0.46340.92400.34970.541248
Hospital 55TertiaryDazhou0.8162−0.04320.40680.538249
Hospital 52TertiaryGuang’an0.8572−0.01870.18670.520450
Hospital 13TertiaryZigong0.60280.44020.36600.516151
Hospital 30TertiaryMianyang0.9403−0.57070.45620.501852
Hospital 39TertiaryNeijiang0.8367−0.21530.35590.500953
Hospital 120TertiaryDazhou0.8648−0.04760.07330.494454
Hospital 21TertiaryLuzhou0.50450.44850.51660.494155
Hospital 69TertiaryChengdu0.64420.27740.29580.488656
Hospital 45TertiaryNanchong1.1158−0.90690.20050.472557
Hospital 31TertiaryMianyang0.9674−0.76460.44330.471358
Hospital 19TertiaryLuzhou0.52280.26130.53360.466559
Hospital 126TertiaryZiyang0.9267−0.77640.52940.463960
Hospital 80TertiaryLuzhou0.65780.04750.37010.461061
Hospital 124TertiaryBazhong0.41410.63320.37670.454462
Hospital 97TertiaryNeijiang0.6146−0.05720.52120.445263
Hospital 44TertiaryNanchong0.9641−0.76410.31150.441864
Hospital 62TertiaryZiyang0.8275−0.44230.33900.441765
Hospital 17TertiaryPanzhihua0.8712−0.75150.50530.432966
Hospital 56TertiaryDazhou0.5786−0.02640.50810.428867
Hospital 33TertiaryMianyang1.0514−1.11300.28870.408968
Hospital 119TertiaryGuang’an0.6779−0.37620.50980.407769
Hospital 59TertiaryBazhong0.54520.20130.23670.403270
Hospital 84TertiaryDeyang0.5354−0.03860.45220.389971
Hospital 83TertiaryDeyang0.60550.06670.12860.384672
Hospital 81TertiaryLuzhou0.22710.60840.57320.384573
Hospital 127TertiaryZiyang0.53130.36020.00800.382374
Hospital 71TertiaryChengdu0.43520.41020.15850.370976
Hospital 32TertiaryMianyang0.9569−1.21500.42450.361477
Hospital 118TertiaryGuang’an0.39620.34330.28450.360578
Hospital 104TertiaryNanchong0.7022−0.79770.63600.354579
Hospital 74TertiaryChengdu0.9750−1.22340.31220.346180
Hospital 72TertiaryChengdu0.5346−0.22460.38360.333781
Hospital 64TertiaryGanzi0.8441−1.12790.44080.320482
Hospital 75TertiaryChengdu0.5211−0.54150.60810.303083
Hospital 50TertiaryYibin0.5234−0.48370.47880.289984
Hospital 70TertiaryChengdu0.5260−0.50130.48040.287885
Hospital 76TertiaryChengdu0.3163−0.03020.52460.283087
Hospital 73TertiaryChengdu0.5268−0.57250.46480.269288
Hospital 91TertiaryGuangyuan0.41120.1785−0.05830.260189
Hospital 95TertiarySuining0.3398−0.00300.32280.259890
Hospital 86TertiaryMianyang0.22910.29190.28850.255391
Hospital 105TertiaryNanchong0.4405−0.36300.35620.243992
Hospital 87TertiaryMianyang0.22230.39090.11030.235993
Hospital 85TertiaryDeyang0.2786−0.01770.34310.226295
Hospital 60TertiaryBazhong0.6400−1.15350.49390.210496
Hospital 67TertiaryLiangshan0.5441−1.01690.59790.208497
Hospital 106TertiaryNanchong0.24830.20700.04110.1952100
Hospital 101TertiaryLeshan0.31110.1159−0.07920.1851102
Hospital 90TertiaryGuangyuan0.2543−0.17050.32550.1747103
Hospital 98TertiaryNeijiang0.5158−1.18370.65800.1680104
Hospital 99TertiaryNeijiang0.3324−0.28760.03110.1309108
Hospital 92TertiaryGuangyuan0.3758−0.75380.31700.1123109
Hospital 89TertiaryMianyang0.2225−0.14150.08190.1118110
Hospital 63TertiaryAba0.5281−1.25460.39250.1032113
Hospital 103TertiaryLeshan0.2443−0.54460.36430.0943114
Hospital 102TertiaryLeshan0.4995−1.09670.25960.0940115
Hospital 109TertiaryYibin−0.0216−0.02000.52010.0931116
Hospital 133TertiaryLiangshan−0.21210.35940.59780.0859119
Hospital 34TertiaryMianyang0.1786−0.31890.25680.0845120
Hospital 77TertiaryChengdu0.4888−0.92970.03370.0774121
Hospital 130TertiaryLiangshan−0.23030.42290.45810.0602123
Hospital 93TertiaryGuangyuan0.7241−1.97390.39200.0543125
Hospital 88TertiaryMianyang0.1172−0.41690.34760.0471126
Hospital 108TertiaryYibin−0.24810.44740.37170.0374129
Hospital 131TertiaryLiangshan−0.16730.01480.57730.0304133
Hospital 129TertiaryLiangshan−0.33840.53820.43570.0199135
Hospital 107TertiaryNanchong0.3526−0.2634−0.58100.0185136
Hospital 78TertiaryPanzhihua−0.0231−0.39600.54810.0147139
Hospital 113TertiaryYibin−0.20050.05780.4753−0.0004142
Hospital 96TertiarySuining0.0077−0.13480.0477−0.0155146
Hospital 112TertiaryYibin−0.1166−0.07160.2639−0.0262148
Hospital 100TertiaryNeijiang0.2177−1.20540.5585−0.0265149
Hospital 123TertiaryYa’an0.1381−0.88630.3621−0.0422150
Hospital 111TertiaryYibin−0.47650.65520.3737−0.0454151
Hospital 121TertiaryYa’an−0.1621−0.29280.4659−0.0585155
Hospital 79TertiaryPanzhihua0.1494−0.72690.0797−0.0600156
Hospital 122TertiaryYa’an−0.2647−0.21050.5731−0.0756161
Hospital 114TertiaryYibin−0.70781.00110.4330−0.0870164
Hospital 115TertiaryYibin−0.3386−0.12140.5005−0.1130168
Hospital 132TertiaryLiangshan−0.0733−0.97540.6648−0.1178169
Hospital 110TertiaryYibin−0.3006−0.22040.4384−0.1266170
Hospital 82TertiaryLuzhou0.0150−0.8756−0.1161−0.2104183
Hospital 125TertiaryBazhong−0.26430.0982−0.5099−0.2354189
Hospital 128TertiaryAba−0.2540−0.95500.5669−0.2361190
Hospital 116TertiaryYibin0.1580−2.79670.2745−0.4735243

Overall ranking results of the study hospitals.

3.2 HCA results

3.2.1 HCA for TGHs

Three distinct clusters were identified among TGHs: the “Excellent” cluster (Cluster 1, n = 41, 30.83%), “Middle” cluster (Cluster 2, n = 76, 57.14%), and “Inferior” cluster (Cluster 3, n = 16, 12.03%). Performance metrics showed significant inter-cluster differences: (1) the “Excellent” cluster outperformed “Middle” and “Inferior” clusters in CMI (1.02 vs. 0.80 vs. 0.68, p < 0.001), ND (663.00 vs. 575.00 vs. 517.50, p < 0.001), TW (77,231.31 vs. 28,130.14 vs. 19,023.35, p < 0.001), and TEI (0.92 vs. 1.06 vs. 1.02, p < 0.001); (2) the “Inferior” cluster demonstrated the best CEI (0.61 vs. 0.82 vs. 0.85, p < 0.001) and MMLRG (0.05 vs. 0.09 vs. 0.09, p = 0.011). Detailed clustering results and inter-cluster comparisons are presented in Tables 7, 8.

Table 7

Hospital codeHospital levelRegionClusterCluster definition
Hospital 1TertiaryChengdu1Excellent
Hospital 2TertiaryChengdu1Excellent
Hospital 18TertiaryLuzhou1Excellent
Hospital 3TertiaryChengdu1Excellent
Hospital 37TertiarySuining1Excellent
Hospital 22TertiaryDeyang1Excellent
Hospital 4TertiaryChengdu1Excellent
Hospital 26TertiaryMianyang1Excellent
Hospital 41TertiaryNanchong1Excellent
Hospital 48TertiaryYibin1Excellent
Hospital 5TertiaryChengdu1Excellent
Hospital 42TertiaryNanchong1Excellent
Hospital 53TertiaryDazhou1Excellent
Hospital 6TertiaryChengdu1Excellent
Hospital 27TertiaryMianyang1Excellent
Hospital 40TertiaryLeshan1Excellent
Hospital 11TertiaryZigong1Excellent
Hospital 51TertiaryGuang’an1Excellent
Hospital 49TertiaryYibin1Excellent
Hospital 16TertiaryPanzhihua1Excellent
Hospital 7TertiaryChengdu1Excellent
Hospital 12TertiaryZigong2Middle
Hospital 8TertiaryChengdu1Excellent
Hospital 9TertiaryChengdu2Middle
Hospital 28TertiaryMianyang1Excellent
Hospital 46TertiaryMeishan1Excellent
Hospital 35TertiaryGuangyuan1Excellent
Hospital 10TertiaryChengdu2Middle
Hospital 57TertiaryYa’an1Excellent
Hospital 65TertiaryLiangshan2Middle
Hospital 58TertiaryBazhong2Middle
Hospital 23TertiaryDeyang1Excellent
Hospital 61TertiaryZiyang2Middle
Hospital 38TertiaryNeijiang2Middle
Hospital 36TertiaryGuangyuan2Middle
Hospital 29TertiaryMianyang1Excellent
Hospital 47TertiaryMeishan1Excellent
Hospital 43TertiaryNanchong2Middle
Hospital 20TertiaryLuzhou2Middle
Hospital 66TertiaryLiangshan2Middle
Hospital 68TertiaryChengdu2Middle
Hospital 54TertiaryDazhou2Middle
Hospital 94TertiarySuining2Middle
Hospital 24TertiaryDeyang2Middle
Hospital 25TertiaryDeyang2Middle
Hospital 14TertiaryZigong2Middle
Hospital 15TertiaryZigong2Middle
Hospital 117TertiaryGuang’an1Excellent
Hospital 55TertiaryDazhou2Middle
Hospital 52TertiaryGuang’an2Middle
Hospital 13TertiaryZigong1Excellent
Hospital 30TertiaryMianyang2Middle
Hospital 39TertiaryNeijiang2Middle
Hospital 120TertiaryDazhou2Middle
Hospital 21TertiaryLuzhou2Middle
Hospital 69TertiaryChengdu2Middle
Hospital 45TertiaryNanchong2Middle
Hospital 31TertiaryMianyang2Middle
Hospital 19TertiaryLuzhou2Middle
Hospital 126TertiaryZiyang2Middle
Hospital 80TertiaryLuzhou2Middle
Hospital 124TertiaryBazhong1Excellent
Hospital 97TertiaryNeijiang2Middle
Hospital 44TertiaryNanchong2Middle
Hospital 62TertiaryZiyang2Middle
Hospital 17TertiaryPanzhihua2Middle
Hospital 56TertiaryDazhou2Middle
Hospital 33TertiaryMianyang2Middle
Hospital 119TertiaryGuang’an2Middle
Hospital 59TertiaryBazhong2Middle
Hospital 84TertiaryDeyang2Middle
Hospital 83TertiaryDeyang2Middle
Hospital 81TertiaryLuzhou3Inferior
Hospital 127TertiaryZiyang1Excellent
Hospital 71TertiaryChengdu1Excellent
Hospital 32TertiaryMianyang2Middle
Hospital 118TertiaryGuang’an1Excellent
Hospital 104TertiaryNanchong2Middle
Hospital 74TertiaryChengdu2Middle
Hospital 72TertiaryChengdu2Middle
Hospital 64TertiaryGanzi2Middle
Hospital 75TertiaryChengdu2Middle
Hospital 50TertiaryYibin2Middle
Hospital 70TertiaryChengdu2Middle
Hospital 76TertiaryChengdu2Middle
Hospital 73TertiaryChengdu2Middle
Hospital 91TertiaryGuangyuan1Excellent
Hospital 95TertiarySuining2Middle
Hospital 86TertiaryMianyang3Inferior
Hospital 105TertiaryNanchong2Middle
Hospital 87TertiaryMianyang1Excellent
Hospital 85TertiaryDeyang2Middle
Hospital 60TertiaryBazhong2Middle
Hospital 67TertiaryLiangshan2Middle
Hospital 106TertiaryNanchong1Excellent
Hospital 101TertiaryLeshan1Excellent
Hospital 90TertiaryGuangyuan2Middle
Hospital 98TertiaryNeijiang2Middle
Hospital 99TertiaryNeijiang2Middle
Hospital 92TertiaryGuangyuan2Middle
Hospital 89TertiaryMianyang2Middle
Hospital 63TertiaryAba2Middle
Hospital 103TertiaryLeshan2Middle
Hospital 102TertiaryLeshan2Middle
Hospital 109TertiaryYibin3Inferior
Hospital 133TertiaryLiangshan3Inferior
Hospital 34TertiaryMianyang2Middle
Hospital 77TertiaryChengdu2Middle
Hospital 130TertiaryLiangshan3Inferior
Hospital 93TertiaryGuangyuan2Middle
Hospital 88TertiaryMianyang2Middle
Hospital 108TertiaryYibin3Inferior
Hospital 131TertiaryLiangshan3Inferior
Hospital 129TertiaryLiangshan3Inferior
Hospital 107TertiaryNanchong1Excellent
Hospital 78TertiaryPanzhihua2Middle
Hospital 113TertiaryYibin3Inferior
Hospital 96TertiarySuining2Middle
Hospital 112TertiaryYibin3Inferior
Hospital 100TertiaryNeijiang2Middle
Hospital 123TertiaryYa’an2Middle
Hospital 111TertiaryYibin3Inferior
Hospital 121TertiaryYa’an3Inferior
Hospital 79TertiaryPanzhihua2Middle
Hospital 122TertiaryYa’an3Inferior
Hospital 114TertiaryYibin3Inferior
Hospital 115TertiaryYibin3Inferior
Hospital 132TertiaryLiangshan2Middle
Hospital 110TertiaryYibin3Inferior
Hospital 82TertiaryLuzhou2Middle
Hospital 125TertiaryBazhong1Excellent
Hospital 128TertiaryAba2Middle
Hospital 116TertiaryYibin2Middle

Results of HCA for TGHs.

Table 8

IndicatorsExcellent median (IQR)Middle median (IQR)Inferior median (IQR)pa
CMI1.02 (0.83, 1.10)0.80 (0.76, 0.88)0.68 (0.64, 0.71)<0.001
ND663.00 (590.00, 680.00)575.00 (520.75, 612.75)517.50 (486.25, 533.00)<0.001
TW77,231.31 (33,848.48, 113,706.55)28,130.14 (18,505.71, 39,746.17)19,023.35 (15,368.54, 21,079.08)<0.001
CEI0.85 (0.76, 0.97)0.82 (0.76, 0.87)0.61 (0.55, 0.66)<0.001
TEI0.92 (0.88, 0.96)1.06 (1.01, 1.12)1.02 (0.96, 1.06)<0.001
MMLRG0.09 (0.05, 0.13)0.09 (0.04, 0.18)0.05 (0.00, 0.07)0.011

Comparison analysis among different clusters of TGHs.

a

Based on the independent-sample Kruskal-Wallis test.

CMI, case-mix index; ND, number of DRGs; TW, total weight; CEI, cost efficiency index; TEI, time efficiency index; MMLRG, mortality of middle and low-risk groups cases.

3.2.2 HCA for SGHs

SGHs were categorized into three clusters: “Excellent” (Cluster 1, n = 45, 26.01%), “Middle” (Cluster 2, n = 121, 69.94%), and “Inferior” (Cluster 3, n = 7, 4.05%). Significant inter-cluster differences were identified: (1) the “Excellent” cluster outperformed in ND (392.00 vs. 293.00 vs. 186.00, p < 0.001) and TW (7,088.34 vs. 4,072.28 vs. 1,873.11, p = 0.001); (2) the “Inferior” cluster demonstrated the highest CMI (0.81 vs. 0.70 vs. 0.64, p < 0.001) but the worst MMLRG (3.39 vs. 0.18 vs. 0.04, p < 0.001); (3) the “Middle” cluster exhibited optimal CEI (0.57 vs. 0.72 vs. 0.64, p < 0.001) and TEI (0.99 vs. 1.16 vs. 1.19, p < 0.001). Clustering results and comparative analyses are provided in Tables 9, 10.

Table 9

Hospital codeHospital levelRegionClusterCluster definition
Hospital 134SecondaryChengdu1Excellent
Hospital 184SecondaryDazhou2Middle
Hospital 186SecondaryDazhou2Middle
Hospital 138SecondaryChengdu1Excellent
Hospital 260SecondaryNanchong2Middle
Hospital 218SecondaryLiangshan1Excellent
Hospital 175SecondaryNanchong2Middle
Hospital 139SecondaryChengdu1Excellent
Hospital 136SecondaryChengdu1Excellent
Hospital 241SecondaryLuzhou2Middle
Hospital 151SecondaryLuzhou2Middle
Hospital 150SecondaryLuzhou2Middle
Hospital 292SecondaryBazhong2Middle
Hospital 142SecondaryChengdu1Excellent
Hospital 176SecondaryNanchong1Excellent
Hospital 185SecondaryDazhou2Middle
Hospital 145SecondaryZigong2Middle
Hospital 152SecondaryLuzhou2Middle
Hospital 230SecondaryChengdu2Middle
Hospital 226SecondaryChengdu1Excellent
Hospital 135SecondaryChengdu1Excellent
Hospital 277SecondaryDazhou1Excellent
Hospital 153SecondaryLuzhou2Middle
Hospital 146SecondaryPanzhihua1Excellent
Hospital 279SecondaryDazhou2Middle
Hospital 141SecondaryChengdu1Excellent
Hospital 165SecondaryNeijiang3Inferior
Hospital 144SecondaryZigong1Excellent
Hospital 167SecondaryLeshan1Excellent
Hospital 137SecondaryChengdu1Excellent
Hospital 228SecondaryChengdu2Middle
Hospital 187SecondaryDazhou2Middle
Hospital 217SecondaryLiangshan2Middle
Hospital 168SecondaryLeshan1Excellent
Hospital 169SecondaryLeshan2Middle
Hospital 155SecondaryDeyang1Excellent
Hospital 278SecondaryDazhou2Middle
Hospital 143SecondaryChengdu1Excellent
Hospital 298SecondaryZiyang2Middle
Hospital 271SecondaryGuang’an2Middle
Hospital 273SecondaryGuang’an2Middle
Hospital 227SecondaryChengdu1Excellent
Hospital 179SecondaryMeishan2Middle
Hospital 140SecondaryChengdu1Excellent
Hospital 274SecondaryGuang’an2Middle
Hospital 194SecondaryBazhong2Middle
Hospital 287SecondaryYa’an2Middle
Hospital 216SecondaryLiangshan2Middle
Hospital 170SecondaryLeshan2Middle
Hospital 177SecondaryNanchong1Excellent
Hospital 232SecondaryChengdu1Excellent
Hospital 242SecondaryLuzhou2Middle
Hospital 243SecondaryLuzhou2Middle
Hospital 272SecondaryGuang’an2Middle
Hospital 159SecondaryMianyang2Middle
Hospital 156SecondaryMianyang2Middle
Hospital 189SecondaryYa’an2Middle
Hospital 233SecondaryChengdu1Excellent
Hospital 158SecondaryMianyang1Excellent
Hospital 220SecondaryLiangshan2Middle
Hospital 181SecondaryMeishan2Middle
Hospital 251SecondaryGuangyuan2Middle
Hospital 229SecondaryChengdu1Excellent
Hospital 154SecondaryDeyang1Excellent
Hospital 252SecondaryGuangyuan2Middle
Hospital 234SecondaryChengdu1Excellent
Hospital 161SecondaryGuangyuan2Middle
Hospital 224SecondaryLiangshan2Middle
Hospital 219SecondaryLiangshan1Excellent
Hospital 231SecondaryChengdu2Middle
Hospital 163SecondaryGuangyuan2Middle
Hospital 162SecondaryGuangyuan1Excellent
Hospital 222SecondaryLiangshan2Middle
Hospital 235SecondaryChengdu2Middle
Hospital 147SecondaryPanzhihua2Middle
Hospital 195SecondaryZiyang1Excellent
Hospital 221SecondaryLiangshan2Middle
Hospital 182SecondaryYibin2Middle
Hospital 261SecondaryNanchong2Middle
Hospital 236SecondaryZigong2Middle
Hospital 188SecondaryYa’an2Middle
Hospital 180SecondaryMeishan2Middle
Hospital 223SecondaryLiangshan2Middle
Hospital 157SecondaryMianyang2Middle
Hospital 246SecondaryDeyang1Excellent
Hospital 299SecondaryAba2Middle
Hospital 183SecondaryGuang’an2Middle
Hospital 301SecondaryLiangshan2Middle
Hospital 192SecondaryYa’an2Middle
Hospital 253SecondaryGuangyuan2Middle
Hospital 166SecondaryNeijiang1Excellent
Hospital 263SecondaryNanchong2Middle
Hospital 281SecondaryDazhou2Middle
Hospital 288SecondaryYa’an2Middle
Hospital 293SecondaryBazhong2Middle
Hospital 255SecondaryGuangyuan2Middle
Hospital 160SecondaryMianyang2Middle
Hospital 191SecondaryYa’an2Middle
Hospital 264SecondaryNanchong2Middle
Hospital 172SecondaryLeshan1Excellent
Hospital 302SecondaryLiangshan2Middle
Hospital 303SecondaryLiangshan2Middle
Hospital 196SecondaryAba2Middle
Hospital 178SecondaryNanchong2Middle
Hospital 284SecondaryDazhou2Middle
Hospital 294SecondaryBazhong2Middle
Hospital 171SecondaryLeshan2Middle
Hospital 256SecondaryGuangyuan2Middle
Hospital 211SecondaryGanzi2Middle
Hospital 237SecondaryZigong2Middle
Hospital 208SecondaryGanzi2Middle
Hospital 206SecondaryGanzi2Middle
Hospital 304SecondaryLiangshan2Middle
Hospital 268SecondaryNanchong2Middle
Hospital 262SecondaryNanchong2Middle
Hospital 190SecondaryYa’an2Middle
Hospital 215SecondaryGanzi2Middle
Hospital 245SecondaryDeyang2Middle
Hospital 197SecondaryAba2Middle
Hospital 282SecondaryDazhou1Excellent
Hospital 248SecondaryMianyang2Middle
Hospital 285SecondaryDazhou2Middle
Hospital 244SecondaryLuzhou1Excellent
Hospital 289SecondaryYa’an2Middle
Hospital 199SecondaryAba2Middle
Hospital 259SecondaryNeijiang2Middle
Hospital 283SecondaryDazhou2Middle
Hospital 200SecondaryAba2Middle
Hospital 290SecondaryYa’an2Middle
Hospital 212SecondaryGanzi2Middle
Hospital 198SecondaryAba2Middle
Hospital 202SecondaryAba2Middle
Hospital 225SecondaryLiangshan2Middle
Hospital 210SecondaryGanzi2Middle
Hospital 254SecondaryGuangyuan1Excellent
Hospital 280SecondaryDazhou1Excellent
Hospital 295SecondaryBazhong2Middle
Hospital 148SecondaryPanzhihua2Middle
Hospital 291SecondaryYa’an1Excellent
Hospital 214SecondaryGanzi2Middle
Hospital 265SecondaryNanchong2Middle
Hospital 213SecondaryGanzi2Middle
Hospital 270SecondaryYibin3Inferior
Hospital 201SecondaryAba2Middle
Hospital 204SecondaryAba2Middle
Hospital 207SecondaryGanzi2Middle
Hospital 247SecondaryMianyang2Middle
Hospital 203SecondaryAba2Middle
Hospital 267SecondaryNanchong2Middle
Hospital 275SecondaryGuang’an2Middle
Hospital 300SecondaryGanzi2Middle
Hospital 305SecondaryLiangshan2Middle
Hospital 306SecondaryLiangshan2Middle
Hospital 296SecondaryBazhong1Excellent
Hospital 250SecondaryMianyang2Middle
Hospital 249SecondaryMianyang1Excellent
Hospital 205SecondaryAba2Middle
Hospital 173SecondaryLeshan2Middle
Hospital 174SecondaryLeshan2Middle
Hospital 240SecondaryPanzhihua2Middle
Hospital 238SecondaryZigong1Excellent
Hospital 209SecondaryGanzi2Middle
Hospital 258SecondarySuining2Middle
Hospital 193SecondaryYa’an2Middle
Hospital 257SecondaryGuangyuan1Excellent
Hospital 286SecondaryDazhou1Excellent
Hospital 149SecondaryPanzhihua1Excellent
Hospital 276SecondaryGuang’an3Inferior
Hospital 266SecondaryNanchong3Inferior
Hospital 297SecondaryBazhong3Inferior
Hospital 239SecondaryZigong1Excellent
Hospital 164SecondaryGuangyuan3Inferior
Hospital 269SecondaryNanchong3Inferior

Results of HCA for SGHs.

Table 10

IndicatorsExcellent median (IQR)Middle median (IQR)Inferior median (IQR)pa
CMI0.70 (0.65, 0.76)0.64 (0.58, 0.70)0.81 (0.69, 0.85)<0.001
ND392.00 (291.50, 437.50)293.00 (245.50, 391.50)186.00 (98.00, 265.00)0.001
TW7,088.34 (4,073.04, 11,140.37)4,072.28 (2,371.38, 7,418.92)1,873.11 (1,075.33, 3,854.96)0.001
CEI0.72 (0.65, 0.84)0.57 (0.53, 0.62)0.64 (0.60, 0.72)<0.001
TEI1.16 (1.10, 1.44)0.99 (0.94, 1.06)1.19 (1.00, 1.90)<0.001
MMLRG0.18 (0.00, 0.34)0.04 (0.00, 0.25)3.39 (2.11, 6.61)<0.001

Comparison analysis among different clusters of SGHs.

a

Based on the independent-sample Kruskal-Wallis test.

CMI, case-mix index; ND, number of DRGs; TW, total weight; CEI, cost efficiency index; TEI, time efficiency index; MMLRG, mortality of middle and low-risk groups cases.

4 Discussion

4.1 Rationales and indicators for medical service performance evaluation

Since 2019, China has implemented a national initiative to strengthen performance evaluations of tertiary public hospitals, establishing a 55-indicator national evaluation framework as a reference for health authorities at all levels (35). In 2019, the Sichuan Provincial People’s Government launched a targeted evaluation of tertiary hospital performance (36), followed by the Sichuan Health Commission’s 2020 initiative for secondary public hospitals (37). These regional frameworks primarily relied on isolated health indicators, similar to historical approaches. In contrast, numerous studies have adopted DRGS indicators to develop comprehensive evaluation models for assessing medical service performance across hospitals or regions (14, 18–21, 23, 25, 26, 28, 29, 38). Adoption of DRGs indicators addresses the limitations associated with single-index methods, the challenges in horizontal comparisons (10), and thus enhances evaluation efficiency (39).

4.2 Rationality of the DRGs-based evaluation model integrating EFA and HCA

Our study developed a novel evaluation model incorporating EFA and HCA using DRGS indicators to evaluate 306 hospitals in Sichuan. The model’s scientific validity stems from two key strengths: data authenticity and methodological rigor. Hu et al. identified common issues in Chinese medical quality evaluations, such as unreliable data sources and low accuracy (40), which our study mitigates through DRGs indicators derived from the FPMR database (23). This ensures original data authenticity, consistency, and standardization, and thereby guaranteeing the credibility of the evaluation results.

Another notable issue concerning DRGS indicators is multicollinearity, defined by Mamouei et al. as inter-variable correlations that distort statistical inference (41). Compared to traditional evaluation indicators, DRGS indicators are interrelated and mutually constrained (10, 39). For instance, increasing CMI (by treating more severe cases) often elevates LOS and medical costs, thereby influencing CEI and TEI. To address this, our study adopted EFA to condense six original indicators into three significant CFs, reducing indicator interactions and enhancing evaluation reliability (10). This approach aligns with PCA methodologies recommended for multicollinearity mitigation (41).

4.3 Disparities in overall performances of the study hospitals

Significant disparities in the medical service performance of 306 study hospitals in Sichuan Province were observed in 2024. TGHs in the Chengdu region predominantly outperformed SGHs in northeastern Sichuan, findings contradict with the conclusion that minority-inhibited regions in Sichuan generally exhibited the worst performance (42–44), but partially consistent with previous studies (42, 45). These discrepancies may be attributed to the combined influences of hospital location and hospital level.

Geographically, Chengdu’s advantageous location, high economic development, strong government support (42), and advanced medical resources facilitate the attraction of skilled healthcare professionals (42, 45) and continuous enhancement of medical techniques. This enables the region to handle a larger patient volume, especially those with severe illnesses. Despite the high pressures of serving massive patient loads, hospitals here typically demonstrate higher management skills to maintain medical quality and retain their leading position in performance evaluations. However, as to northeastern Sichuan, their worst performance may be partially attributed to the underdeveloped economy, insufficient allocation of high-quality medical institutions (especially tertiary hospitals), and imbalanced healthcare talent structure. Relevant data show its GDP growth rate has been significantly lower than the provincial average, which may strain fiscal support for healthcare and hinder development (46). In 2022, tertiary hospitals accounted for only 11.4% in the region-far below the proportion in Chengdu, indicating a severe shortage of premium healthcare resources. Additionally, the region suffers a dearth of experienced practitioners, with only 9.2% of staff holding senior qualifications, far below the 38.7% in Chengdu (47, 48).

From the perspective of hospital level, China’s hospital system is categorized into three tiers (49). TGHs function as regional medical centers, integrating comprehensive capabilities in clinical care, education, and research to provide high-level specialized services across regions, cities, provinces, and even nationwide (50). Equipped with abundant medical resources and top-tier talent, they inherently achieve higher technical standards. Previous studies have also shown that tertiary hospitals dominate healthcare service provision in China, regardless of the severity of illness (51–53). Their large patient bases, advanced equipment, and skilled personnel confer a decisive advantage in performance evaluations. In contrast, SGHs primarily serve local communities while undertaking limited teaching and research roles (49). Compared to tertiary institutions, they lag in bed capacity, clinical department configuration, medical equipment, technical proficiency, talent reserves, and research capabilities (54, 55). These structural gaps place secondary hospitals at a competitive disadvantage, typically resulting in lower rankings in performance evaluations.

4.4 Disparities in performances among different clusters

4.4.1 Performance variations in TGHs

Our study observed that the majority of tertiary general hospitals were clustered into the “Middle” cluster, with over 10% classified into the “Inferior” cluster. In 2021, the Health Commission of Sichuan Province issued the “Implementation Rules for the Evaluation Criteria of Tertiary Hospitals in Sichuan Province (2021 edition),” instructing tertiary hospitals in the region to enhance daily management and consistently improve medical quality (10). Despite uniform construction standards for THGs in Sichuan, significant disparities in medical service performance were revealed. Hospitals in the “Excellent” cluster significantly outperformed those in the “Middle” and “Inferior” clusters in medical ability and time efficiency (10, 42). However, they demonstrated poorer performance in cost efficiency (10, 42) and medical security, which contrasts with previous studies (10).

Hospitals in the “Excellent” cluster typically serve as top-tier regional institutions, benefiting from stronger government support, advanced medical equipment, and highly skilled healthcare teams. This enables them to treat a larger volume of patients, particularly those with severe illnesses, thereby expanding their disease coverage. As a result, they exhibit higher medical abilities, as indicated by higher CMI, ND, and TW values compared to other hospitals. Previous studies have found that regions generally exhibit lower time efficiency regardless of their medical service levels (42). However, our study identified that “Excellent” cluster hospitals performed best in time efficiency. This finding contrasts with previous research and may be attributed to their superior hospital management skills and professional teams. Health authorities have set stringent and uniform requirements on inpatient LOS in tertiary hospitals (14). Our findings suggest that “Excellent” cluster hospitals, with their specialized management teams and advanced management skills, can achieve better performance in time efficiency performance.

Consistent with previous studies (10, 42, 45, 56), our study found that “Excellent” cluster hospitals exhibited higher medical costs and mortality of middle and low-risk patients, likely linked to their status as regional top-tier institutions. These hospitals are responsible for admitting and treating regional severe, complicated, and acute cases (57), which consume more medical resources (e.g., advanced equipment and experienced staff), require longer hospital stays, and have higher treatment failure rates due to the severity of conditions. Collectively, these factors may contribute to the elevated medical costs and mortality values observed in “Excellent” cluster hospitals.

4.4.2 Performance variations in SGHs

Our study revealed that, similar to TGHs, the majority of SGHs were clustered into the “Middle” cluster, with fewer than 5% clustered into the “Inferior” cluster. Unlike TGHs, SGHs in the “Excellent” cluster only outperformed in ND and TW. In contrast, those in the “Inferior” exhibited the best performance in CMI but the worst in MMLRG. Neither the “Excellent” nor the “Inferior” cluster hospitals performed best in CEI and TEI. These findings suggest that “Excellent” cluster hospitals outperformed in disease coverage and total inpatient output, whereas “Inferior” cluster hospitals performed relatively better in admitting and treating severely ill patients but struggled to maintain medical service quality, as evidenced by the disproportionately high MMLRG.

In 2019, the Health Commission of Sichuan Province issued the “Guidelines on Further Improving the Graded Diagnosis and Treatment System,” which explicitly stipulated the establishment of a scientific and efficient two-way referral mechanism between lower-level hospitals (secondary or grassroots) and tertiary hospitals within the region to meet the local medical service demands (58). Nevertheless, there are no mandatory regulations regarding patient referrals between SGHs. Patients may choose “Inferior” cluster hospitals based on geographic convenience rather than the hospital’s medical abilities for treating their conditions. As a result, these patients may be randomly admitted and treated by SGHs. The higher CMI values suggest that “Inferior” cluster hospitals may receive a great number of severe patients due to their convenient location. However, their medical abilities may fall short of the required standards for treating these patients, as indicated by the alarmingly high MMLRG values. Therefore, for SGHs in the “Inferior” cluster, enhancing medical abilities and maintaining medical quality should be prioritized as urgent tasks to ensure inpatient safety.

4.5 Limitations of our study

Several limitations of our study should be acknowledged. Firstly, although the data used in this study originated from the FPMR, potential errors may have occurred during the upload process to the SHDADSCP for generating DRGS indicators. These potential errors could influence the results of the medical service performance evaluation. Secondly, the algorithms for calculating DRGS indicators vary across different regions. For instance, BJ-DRG for Beijing and MS-DRG for Sichuan. Even with the same original data, the results of DRGS indicators may differ due to these regional variations. Therefore, caution should be exercised when applying our findings to other regions. Thirdly, this study did not consider the weights of DRGS indicators in constructing the performance evaluation model. Previous studies have demonstrated that different weights assigned to evaluation indicators can potentially affect the evaluation results (3). Therefore, future studies are anticipated to explore these underlying mechanisms.

5 Conclusion

DRGS serve as a widely adopted risk adjustment tool for evaluating the medical service performance both within and among hospitals. By implementing EFA, our study mitigated the multicollinearity inherent in DRGS indicators, yielding more reliable and accurate evaluation results. Significant disparities in medical service performance were observed across different regions and hospital levels in Sichuan Province, with Chengdu region demonstrating optimal performance. For TGHs, hospitals in the “Inferior” cluster are recommended to enhance their medical ability and efficiency compared to those in the “Excellent” cluster. Conversely, hospitals in the “Excellent” cluster should focus on controlling medical costs compared to those in the “Inferior” cluster. For SGHs, hospitals in the “Inferior” cluster should concentrate on improving medical security and ensuring patient safety compared to those in the “Middle” and “Excellent” clusters.

Statements

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors without undue reservation.

Author contributions

XL: Conceptualization, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. JC: Resources, Supervision, Writing – review & editing. RG: Data curation, Writing – review & editing. OJ: Project administration, Supervision, Writing – review & editing.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Acknowledgments

Many thanks to the Sichuan Health Data Analysis and Decision Support Cloud Platform for their support in extracting the original data.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The authors declare that no Gen AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Summary

Keywords

DRGS, exploratory factor analysis, hierarchical clustering analysis, Donabedian, hospital, performance evaluation

Citation

Liu X, Cao J, Ge R and Jiang O (2025) Evaluating medical service performance of hospitals in Sichuan Province, China: exploratory factor analysis and hierarchical clustering analysis based on diagnosis-related groups. Front. Public Health 13:1617945. doi: 10.3389/fpubh.2025.1617945

Received

25 April 2025

Accepted

11 July 2025

Published

24 July 2025

Volume

13 - 2025

Edited by

Bernadette Pfang, University Hospital Fundación Jiménez Díaz, Spain

Reviewed by

Sheng Ding, The Central Hospital of Wuhan, China

Ricardo De Moraes E. Soares, Instituto Politecnico de Setubal (IPS), Portugal

Updates

Copyright

© 2025 Liu, Cao, Ge and Jiang.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Xuedong Liu, Ou Jiang,

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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