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Article

Construction of Hospital Diagnosis-Related Group Refinement Performance Evaluation Based on Delphi Method and Analytic Hierarchy Process

1
College of Public Health, Chongqing Medical University, Chongqing 400016, China
2
Medical Records Department, The People’s Hospital of Tongliang District, Chongqing 402560, China
3
Medical Record Room of Medical Department, Kaizhou District People’s Hospital, Chongqing 405400, China
4
Audit Department, The People’s Hospital of Tongliang District, Chongqing 402560, China
*
Author to whom correspondence should be addressed.
Hospitals 2025, 2(3), 20; https://doi.org/10.3390/hospitals2030020 (registering DOI)
Submission received: 28 February 2025 / Revised: 9 July 2025 / Accepted: 21 July 2025 / Published: 2 August 2025

Abstract

Objective: This study aimed to develop a performance evaluation index system for a district-level public hospital in Chongqing, China, based on Diagnosis-Related Groups (DRGs), to provide a benchmark for performance assessment in similar hospitals. The system was constructed using a literature analysis, the Delphi method, and the Analytic Hierarchy Process (AHP) to identify and weight relevant indicators. Results: The evaluation system consists of three primary indicators and eighteen secondary indicators. Key secondary indicators include the Case Mix Index (CMI), cost consumption index, low-risk group mortality rate, the proportion of patients with three- or four-level surgeries at discharge, and the proportion of medical service revenue to medical income. In 2020, significant improvements were observed in several indicators, such as a decrease in the low-risk group mortality rate to 0% and increases in the proportion of patients with three- or four-level surgeries and CMI by nearly 10% and 13%, respectively. Conclusions: This study successfully developed a comprehensive and scientifically sound performance evaluation index system for a district-level public hospital in Chongqing. The system has proven effective in objectively assessing inpatient medical care performance and providing valuable guidance for improving healthcare services in similar settings.

1. Introduction

In the realm of healthcare management and resource allocation, the Diagnosis-Related Groups (DRGs) system has emerged as a crucial tool for classifying patients and assessing hospital performance [1]. DRG classification is based on the International Classification of Diseases (ICD-10) and considers various factors, such as diagnosis, surgical procedures, complications, comorbidities, and patient age, to group patients with similar clinical conditions and resource utilization [2]. Currently, DRGs are widely used for medical cost reimbursement in healthcare systems worldwide, and some of its indicators are adopted for performance evaluation by health administrative authorities [3]. Evidence from the U.S., Germany, and Japan underscores that a unified framework with dynamic adjustment mechanisms is foundational to successful DRG implementation. The U.S. adopted gradual reforms with tripartite supporting mechanisms (prepayment/compensation/oversight); Germany established a nationally standardized system for coding, weight assignment, and pricing; Japan pioneered the hybrid payment DPC system, integrating fixed-case payments with fee-for-service components, incorporating phase-down payment rates based on hospitalization duration. The common challenges across these nations include medical quality deterioration, patient selection bias (e.g., the avoidance of high-cost cases in the U.S.), and cost-shifting behaviors (e.g., inflated outpatient expenditures in Korea) [4]. A plethora of literature has explored the utilization of DRGs for assessing hospital performance, particularly in developed regions like Beijing, Shanghai, and Zhejiang, as well as in large tertiary teaching hospitals [5]. These studies have shown that DRG-based performance assessment is not only scientifically robust but effective in controlling medical costs [6]. There remains a notable gap in research regarding the application of DRG-based performance assessment in district-level hospitals [7]. These hospitals often face unique challenges, such as having limited resources for research, education, and medical technology, while catering to a wide range of medical needs, including both common diseases and complex critical conditions [8,9]. Against the backdrop of performance assessment in public hospitals and the practical implementation of the medical insurance DRG payment system, this study endeavors to address the aforementioned gap by developing a tailored and refined performance evaluation index system based on DRGs specifically designed for district-level hospitals [10,11]. By taking into account the distinct positioning and characteristics of these hospitals, the proposed index system aims to provide a more accurate and comprehensive evaluation of their performance [12]. The ultimate goal is to enhance medical service quality, optimize resource allocation, and facilitate cost management [13].
The research methodology involves analyzing public hospital performance evaluation policies, determining assessment indicators through a literature review and the Delphi method, and using the Analytic Hierarchy Process (AHP) to assign weights to the indicators. Total Technical Loss (TTL) values will be calculated to derive weight coefficients for each indicator. The index system will be empirically validated using data from district-level hospitals in Chongqing. This study aims to construct a performance evaluation index system for district-level public hospitals, providing a framework for assessing their performance and contributing to the improvement of healthcare services.

2. Method

2.1. Delphi Method

(1) The research team has been formed, comprising seven highly experienced members who possess a deep knowledge of DRGs (Diagnosis-Related Groups) and performance assessment in national public hospitals. The primary tasks include formulating a preliminary set of performance assessment indicators, selecting appropriate consulting experts, preparing expert consultation forms, organizing consulting activities, and conducting a statistical analysis of the results. The expert group is composed of leaders from various departments, such as hospital management, clinical department heads, medical affairs, finance, and quality control. The members of the expert group have different professional titles, educational backgrounds, and years of experience, as shown in Table 1.
(2) Determining the following selection criteria for consulting experts: (1) having more than 5 years of relevant management experience in clinical, medical, quality control, and financial aspects of hospitals; (2) being familiar with performance assessment in public hospitals; (3) possessing extensive practical experience; (4) showing interest in the research topic.
(3) Conducting a literature analysis to search for relevant literature on the performance assessment indicator systems of medical institutions both domestically and internationally. Combining this with the actual positioning of a district-level public hospital in Chongqing, the factors affecting its service performance are clarified. Based on the reference of China’s public hospital performance evaluation indicator system, a preliminary set of performance assessment indicators is proposed.
(4) Carrying out expert consultation activities: Following the process of the Delphi method, expert consultation is conducted through the distribution and retrieval of questionnaires using paper correspondence.
Two rounds of questionnaire surveys were conducted through paper correspondence, with 28 questionnaires distributed and retrieved in each round, achieving a 100% response rate, indicating high enthusiasm among the experts.
(i)
Response to round 1 Delphi Results
Indicator Refinement Process:
  • Response Rate: 100% questionnaire recovery (28/28 experts).
  • Exclusion Criteria: Eliminated all indicators with coefficient of variation (CV) > 0.25 (statistical threshold for low consensus). Conducted structured brainstorming sessions to align indicators with national standards.
  • Indicator Reduction: Initial pool of 125 indicators → 32 retained (74.4% reduction). The rationale was streamlining for operational feasibility while maintaining policy compliance.
(ii)
Round 2 Delphi Outcomes
  • Expert Engagement: All 28 indicators scored ≥ 3.5/5 (mean ± SD: 4.2 ± 0.3), demonstrating strong consensus.
  • Top-Performing Indicators: Case Mix Index (CMI), mean score: 4.5 ± 0.2, reflects clinical complexity. Rate of surgeries and interventions, mean score: 4.2 ± 0.3, significance measures procedural intensity. Proportion of Grade 3–4 surgeries, mean score: 4.3 ± 0.2, significance proxies technical capability.
This two-stage Delphi process achieved rigorous indicator optimization, balancing statistical validity with policy alignment.
The performance evaluation indicator system includes 3 primary indicators and 32 s indicators. The feasibility and sensitivity of each indicator will be evaluated and categorized into 5 levels with corresponding scores: very good feasibility/sensitivity—5 points, good feasibility/sensitivity—4 points, moderate feasibility/sensitivity—3 points, poor feasibility/sensitivity—2 points, and very poor feasibility/sensitivity—1 point. Experts are also required to assess their familiarity level and judgment basis for the proposed indicators. The familiarity level is divided into 5 levels, and their corresponding quantitative values are as follows: very familiar—0.9, familiar—0.7, moderately familiar—0.5, unfamiliar—0.3, and very unfamiliar—0.1. The experts’ judgment basis for the questions includes four dimensions: practical experience, theoretical analysis, reference to domestic and foreign literature, and intuitive perception. Each dimension is categorized into large, medium, and small degrees, and different scores can be assigned as follows: practical experience—large (0.5), medium (0.4), small (0.3); theoretical analysis—large (0.3), medium (0.2), small (0.1); reference to domestic and foreign literature—large (0.1), medium (0.1), small (0.1); intuitive perception—large (0.1), medium (0.1), small (0.1). The familiarity level and judgment basis are cross-summarized. From Table 2, it can be seen that the cross-analysis of the familiarity level and judgment basis for the content of this questionnaire shows no significant difference in the judgment basis for the four items (chi = 2.650, p = 0.851 > 0.050). Refer to Table 2.

2.2. Weight Determination for Public Hospital KPIs: Pairwise Mean Comparison and TTL Normalization Approach

In the multi-indicator evaluation research of public hospitals, the construction of indicator weights is essential. We need to build a ranking table of weights. The construction process is as follows: 1. Calculate the average values of each analysis item. 2. Compare the average values pairwise based on their magnitudes. 3. If one average value is relatively larger than the other, assign 1 point; if it is relatively smaller, assign 0 points; if the average values are exactly the same, assign 0.5 points. 4. The larger the average value, the higher the importance, and consequently, the higher the weight. 5. After completing the ranking table of weights, we can calculate the TTL (total) value to obtain the final weight. The specific steps are as follows: 1. Sum up the data for each row to obtain the TTL value. 2. Normalize the TTL value to obtain the weight value.

2.3. Integrating Excel-Based Descriptive Analysis with SPSS Reliability Testing

Statistical methods can be used to establish a database and enter data using Excel 2010. Then, calculate the arithmetic mean and coefficient of variation in the indicator scores. Additionally, calculate the judgment coefficient (Ca), familiarity coefficient (Cs), and authority coefficient (Cr) of the experts’ opinions. Furthermore, use SPSS 20.0 to calculate the agreement coefficient (W) and Cronbach’s α coefficient of the expert opinions and test them. In the testing process, a significance level of p < 0.05 is usually used for judgment.

3. Construction of a Fine-Grained Performance Assessment Indicator System for District-Level Hospitals Based on DRG

3.1. Enthusiasm, Authority, and Reliability of Experts

The enthusiasm of experts is represented by the expert enthusiasm coefficient, which reflects the degree of attention and participation of experts in the research. The recovery rate of expert consultation forms can be used to measure this. In this study, experts demonstrated high engagement and evaluative consistency, as reflected by the 100% response rate. The authority of the expert group is also an important factor influencing the consulting results. The authority can be represented by the authority coefficient (Cr), which is the average value of the judgment coefficient (Ca) and the familiarity coefficient (Cs) of the experts. In this study, the calculated Cr value is 0.796, indicating that the expert group has a high level of authority, and that the research results are reliable. The reliability of each indicator can be represented by Cronbach’s α coefficient, which is commonly used to test the internal consistency of indicators. Generally, a Cronbach’s α coefficient greater than 0.8 indicates excellent reliability, 0.6 to 0.8 indicates good reliability, and less than 0.6 indicates poor reliability. In this study, the entire indicator system was tested for reliability, and the obtained Cronbach’s α coefficient is 0.911, indicating a very high correlation among the indicators and excellent reliability. Additionally, after two rounds, the coefficient of concordance (W) is 0.800, indicating a very high degree of correlation between the indicators, strong consistency, and excellent reliability. The stratified analysis showed no scoring disparity by seniority (Junior vs. Senior: F =1.361, p = 0.293).

3.2. Modification of the Performance Assessment Indicator System

Based on the existing performance evaluation indicators of a district-level hospital in Chongqing and incorporating DRG-based performance assessment indicators, a total of 32 evaluation indicators were designed with the input and suggestions from hospital leaders and relevant management experts. Under the premise of ensuring the interrelatedness of the assessment indicators and avoiding subjective indicators, two-round Delphi consensus achieved a 100% response rate (28/28), with pre-defined inclusion criteria: >70% agreement per indicator. Then, 17 indicators were removed through discussions within the project workgroup. The TTL (indicator score) was calculated based on the ranking table of weights, and the final weight values were obtained—see Table 3.

4. Result

4.1. Inclusion Criteria

(1)
Patient Scope: All discharged patients from the study hospital (2019–2020).
(2)
Data Completeness: Medical records with sufficient detail for CN-DRG grouping.
(3)
Hospitalization Duration: Length of stay (LOS) between 1 and 60 days.

4.2. Exclusion Criteria and Sample Characteristics

(1)
Exclusions: LOS > 60 days or costs > JPY 2,000,000.
(2)
Final Cohort: 92,807 cases (Table 4).

4.3. DRG Grouping Methodology

(1)
System: CN-DRG (2019 Edition, National Health Commission).
(2)
Grouping Logic: Based on primary diagnosis, procedures, complications, and comorbidities.
(3)
Output: 786 DRGs.
(4)
Coverage Gaps: Missing MDC groups P (psychiatry), A (multiple trauma), T (transplants) vs. region standards (Figure 1).

4.4. Significant Improvement in Healthcare Service Capacity

The empirical analysis demonstrates that the implementation of the refined DRG-based performance model in county-level hospitals yielded significant positive outcomes. Specifically, following implementation, the hospital-wide Case Mix Index (CMI) and the proportion of level 3–4 surgeries showed marked increases (Table 5). The DRG numbers and weights also improved for the majority of departments (Figure 2).
The hospital-wide CMI increased from 0.76 to 0.86, representing a 13% increase. The proportion of level 3–4 surgeries rose from 39.35% to 49.11%. These improvements indicate that the DRG-based performance model effectively standardizes medical practices and enhances healthcare service quality.

4.5. Progress in Healthcare Quality

Healthcare quality also demonstrated improvements: The infection control efficacy was notable (Table 6). The infection rate for Class I incisions decreased by 72.7%, confirming the effectiveness of infection prevention measures. The elective surgery complication risk requires attention. The complication rate increased by 19%. Further investigation is warranted to determine if this stems from increased surgical complexity or perioperative management issues. Low-risk patient management was optimized. The mortality rate in the low-risk group reduced to zero, validating the efficacy of tiered diagnosis and treatment coupled with risk-stratified management. Incorporation into standardized hospital operating procedures is recommended. The reduced readmissions reflect economic and quality gains. The number of 15-day readmissions decreased by 36.9%, directly indicating improved quality of care. Analyzing the cost increase rationale, the average cost per case increased by 14.3%. A further breakdown of the cost structure (Table 6—proportions of drugs, consumables, and services), combined with the rise in Grade 3–4 surgeries, suggests that this increase is associated with the escalation in technical complexity.

4.6. Marked Enhancement in Service Efficiency

Service efficiency improved significantly post implementation: Although the time consumption index remained largely stable, the average length of stay (ALOS) decreased from 8.06 days to 7.78 days (Table 7). A comparative analysis revealed reductions in the proportion of drug costs (from 28.59% to 27.18%) and consumable costs (from 12.83% to 11.36%) associated with DRGs. While the average hospitalization cost increased, this rise was attributable to the proportion of medical service income (excluding drugs, consumables, and tests), indicating improvements in cost control metrics. These findings demonstrate that the DRG-based performance model aids hospitals in better cost containment and enhances both economic and service efficiency.

4.7. Improved Patient Satisfaction

The patient satisfaction survey results indicated enhanced satisfaction with hospital services following model implementation (Table 8). This improvement signifies that, while elevating service quality and efficiency, the DRG-based model did not lead to patient refusal or shifting—a concern observed in some international DRG implementations—and contributed to a better patient experience.
The implementation of the refined DRG-based performance model in county-level hospitals has yielded positive outcomes, providing robust support for enhancing hospital management efficiency and service quality. However, areas for improvement exist: The 13% CMI increase was primarily driven by the 24.81% rise in Grade 3–4 surgeries, reflecting the model’s success in incentivizing resource allocation towards complex cases. Nevertheless, vigilance is required regarding the potential cost pressures indicated by the increase in the cost consumption index (0.78 → 0.83). The optimization of DRG cost accounting is recommended. Future efforts will focus on refining this performance model to facilitate broader adoption across hospitals and achieve even greater effectiveness.

5. Discussion

The performance evaluation of a district-level public hospital based on Diagnosis-Related Groups (DRGs) is of utmost importance in the healthcare sector, as it facilitates an objective assessment of medical care quality, resource allocation, and overall hospital efficiency [14,15].
This study aimed to construct a refined performance evaluation index system for a district-level public hospital in Chongqing, China, leveraging DRGs as the foundation [16]. The index system was designed to comprehensively evaluate the hospital’s inpatient medical care performance through a rigorous research process, including a literature analysis, expert consultation, and statistical analysis [16]. The establishment of the performance evaluation index system involved the formulation of three primary indicators and eighteen secondary indicators, focusing on medical quality and efficiency [17]. Among these, Case Mix Index (CMI), cost consumption index, and low-risk group mortality rate were identified as critical secondary indicators, representing the complexity and difficulty of admitted cases, cost efficiency, and patient outcomes, respectively. The consulting expert group, composed of leaders from various hospital departments, played a crucial role in validating the effectiveness and relevance of the index system. Through the Delphi method, valuable insights and feedback were gathered from the experts, ensuring the system’s alignment with the hospital’s specific needs and performance requirements. The diverse expertise and experiences of the expert group enriched the evaluation process, contributing to the robustness and credibility of the findings. The empirical verification of the index system demonstrated its effectiveness in promoting better patient outcomes and resource utilization. The reduction in the low-risk group mortality rate from 0.02% in 2019 to 0% in 2020 indicated an improvement in medical care quality [18]. Additionally, the increase in the proportion of level three and four surgeries (Grade 3–4 surgeries refer to high-complexity procedures classified under China’s National Health Commission (NHC) Surgical Grading System) and CMI by nearly 10% and 13%, respectively, compared with 2019, highlighted the system’s positive impact on medical efficiency and complexity. The fine-grained performance assessment indicators based on DRGs provided a more scientific and reasonable classification of patients admitted by clinical departments and doctors. By considering disease types, difficulty levels, and disease outcomes, this approach addressed the issue of performance evaluation results being incomparable due to differences in patient admission patterns [19]. Consequently, the fine-grained assessment ensured a more objective, fair, and persuasive performance evaluation. The implications of this study extend to various aspects of hospital performance management and policy development. The developed index system serves as a valuable tool for administrators in optimizing resource allocation, reducing costs, and improving patient care. Moreover, it can be utilized as a benchmarking tool for hospitals to compare their performance against similar institutions, fostering healthy competition and knowledge sharing within the healthcare sector [20,21].
It should be noted that the conclusions of this study are primarily derived from a comparative analysis of data spanning 1 year (2019–2020) at a single hospital site. Consequently, caution is warranted when extrapolating these findings regarding generalizability and long-term effects. Future research should incorporate multi-center cohorts spanning geographically diverse hospitals across different tiers, alongside longitudinal data collection over extended time frames (e.g., 3–5 years). Such approaches would enable the a comprehensive validation of the indicator system’s stability, adaptability, and sustained impact, while further examining its performance under varying external conditions such as healthcare policy reforms.
This study also highlights the importance of considering employee satisfaction as a critical component of the evaluation process. By incorporating employee feedback, hospitals can gauge their organizational health and employee engagement, both of which significantly impact overall hospital performance [4]. This system should augment not replace equity-focused allocation. Moving forward, the continuous application and improvement of the performance evaluation index system are essential. Longitudinal analysis will provide valuable insights into hospital performance trends over time, facilitating the development of long-term strategies for further enhancement. Additionally, the comprehensive application of the entire set of performance evaluation indicators should be explored, providing a holistic view of hospital performance and supporting continuous improvement efforts. In conclusion, the research on the performance evaluation of a district-level public hospital based on DRGs has yielded valuable insights and practical implications. The establishment of a refined index system, expert consultation, and empirical verification have demonstrated its effectiveness in promoting medical care quality, efficiency, and resource optimization. The findings from this study offer significant value for healthcare management, policy formulation, and overall medical service improvement, ultimately benefiting patients and healthcare providers alike.

6. Conclusions

This study successfully developed a refined performance evaluation index system based on DRGs for a district-level public hospital in Chongqing. The system demonstrated its objectivity and effectiveness in assessing medical care quality and resource utilization. The findings provide valuable insights for hospital administrators, guiding them in making data-driven decisions to enhance patient care and overall hospital performance.

Author Contributions

M.C. was primarily responsible for the study’s design, the development of the research plan, data collection and analysis, as well as drafting the initial manuscript. Z.Y. assisted in data compilation and analysis, provided valuable insights into real-life research cases, and contributed expertise in the medical context. X.W. participated in the formulation of the research plan, data organization, and provided significant insights into interpreting hospital data. B.M. was responsible for data integration and statistical analysis, as well as presenting the results. C.P., as the corresponding author, supervised and guided the entire research process, providing valuable feedback and suggestions for revising and enhancing the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by funding from Y2021-8, Hospital-Level Project, Chongqing Tongliang District People’s Hospital.

Institutional Review Board Statement

The research conducted in collaboration with Chongqing Tongliang District People’s Hospital has been granted ethical approval under reference Y2021-8 by the designated ethics committee, ensuring strict adherence to relevant guidelines and regulations, and upholding ethical principles throughout the study. Every participant from Chongqing Tongliang District People’s Hospital was fully informed about the study’s objectives, methodologies, potential risks, and data confidentiality. Informed consent was diligently obtained, including cases where legal guardians’ consent was necessary, with participants being fully aware of their voluntary participation right and the option to withdraw without any adverse consequences. The declaration section of this manuscript specifically highlights the ethical approval received under reference Y2021-8, underscoring the meticulous ethical review and oversight, particularly by Chongqing Tongliang District People’s Hospital. This study was approved by the Ethics Committee of Chongqing Tongliang District People’s Hospital. Informed consent was obtained from all participants and/or their legal guardians. All experiments were performed in accordance with relevant guidelines and regulations (such as the Declaration of Helsinki).

Informed Consent Statement

For this submission, the consent for publication statement is not applicable as no identifiable information of the participants from Chongqing Tongliang District People’s Hospital, including images, faces, or names, has been disclosed. Therefore, the consent for publication requirement does not pertain to the content of this manuscript. We affirm that the privacy and confidentiality of individuals from Chongqing Tongliang District People’s Hospital involved in this research have been diligently protected.

Data Availability Statement

The data utilized in this study originates from the clinical records of Chongqing Tongliang District People’s Hospital. The original data tables containing the relevant information for the analysis conducted in the manuscript are available. However, due to privacy and ethical considerations, complete raw data cannot be publicly disclosed. Interested researchers may request access to the data through appropriate channels and in compliance with relevant ethical guidelines.

Conflicts of Interest

There are no conflicts of interest associated with this project involving the individuals, institutions, or organizations mentioned in the manuscript.

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Figure 1. Missing MDC groups (P/A/T) compared with region benchmarks. (Blue indicates the total number of MDC cases in the region, while purple indicates the total number of MDC cases in this hospital).
Figure 1. Missing MDC groups (P/A/T) compared with region benchmarks. (Blue indicates the total number of MDC cases in the region, while purple indicates the total number of MDC cases in this hospital).
Hospitals 02 00020 g001
Figure 2. Distribution of DRG weights by department (2019–2020).
Figure 2. Distribution of DRG weights by department (2019–2020).
Hospitals 02 00020 g002
Table 1. Composition of basic information of expert group members.
Table 1. Composition of basic information of expert group members.
IndicatorNumber (n)Composition (%)
Age
30–49 years1614
Below 30 years157
50 years and above1129
Education
Bachelor’s degree2257
Doctorate or above214
Master’s degree429
Professional Title
Junior157
Associate Senior586
Full Senior1343
Intermediate914
Work Experience
≤10 years214
≥30 years857
10–19 years1286
20–29 years643
Table 2. Cross-tabulation summary.
Table 2. Cross-tabulation summary.
Judgment BasisFamiliarity with Questionnaire ContentTotal (n = 28)
(1) Very Familiar (n = 9)
Theory Analysis417
Practice Experience111
Understanding of Domestic and Foreign Peers416
Personal Intuition415
(2) Familiar (n = 15)
Theory Analysis1217
Practice Experience811
Understanding of Domestic and Foreign Peers1116
Personal Intuition915
(3) Moderately Familiar (n = 4)
Theory Analysis117
Practice Experience211
Understanding of Domestic and Foreign Peers116
Personal Intuition215
Chi-Square Test: χ2 = 2.650, p = 0.851
Table 3. Weight calculation results of the Analytic Hierarchy Process (AHP).
Table 3. Weight calculation results of the Analytic Hierarchy Process (AHP).
Primary IndicatorsSecondary IndicatorAverage ValueTotal Score (TTL)Weight Value
QualityDischarge patients’ surgery proportion4.46216.50.11419
Proportion of level 3–4 surgeries4.308140.09689
Surgery patient complication rate4.1156.50.04498
Low-risk group case mortality rate4.269120.08304
Number of DRGs4.308140.09689
Class I incision infection rate4.1156.50.04498
Elective surgery complication rate4.1156.50.04498
15-day readmission rate3.6151.50.01038
EfficiencyAverage cost per case4.1156.50.04498
Drug cost proportion of disease group (total drug expenditure/total medical revenue × 100%)3.9622.50.0173
Consumables cost proportion of disease group4.23110.50.07266
Proportion of medical service revenue (excluding drugs, consumables, and examination and test income) to medical income4.269120.08304
Average length of stay4.0773.50.02422
Time consumption index4.1929.50.06574
Cost consumption index4.34615.50.10727
Service attitudeDischarged patients’ satisfaction rate4.1156.500.04498
Number of effective complaints3.4230.50.00346
Table 4. Cohort distribution pre-/post-DRG implementation.
Table 4. Cohort distribution pre-/post-DRG implementation.
YearDischargesDRG-GroupedRate (%)MaleFemaleLow-Risk CasesLow-Risk (%)DeathsMortality (%)Medical (%)Surgical (%)
2019 (Pre)52,16451,99599.6827,27424,72116,34131.432130.4159.2540.75
2020 (Post)41,18740,81299.0920,86919,94312,82131.411940.4857.3842.62
Table 5. Changes in healthcare service capacity (2019–2020).
Table 5. Changes in healthcare service capacity (2019–2020).
YearCMIProportion of Level 3–4 Surgeries (%)Number of DRGs
2019 (Pre)0.7639.35664
2020 (Post)0.8649.11661
Table 6. Changes in healthcare quality indicators (2019–2020).
Table 6. Changes in healthcare quality indicators (2019–2020).
YearClass I Incision Infection Rate (%)Elective Surgery Complication Rate (%)Low-Risk Group Mortality Rate (%)15-Day Readmissions (n)Average Cost per Case (JPY)
2019 (Pre)0.110.210.0214887993.27
2020 (Post)0.030.2509399138.53
Table 7. Changes in healthcare service efficiency (2019–2020).
Table 7. Changes in healthcare service efficiency (2019–2020).
YearDrug Cost Proportion per DRG (%)Consumable Cost Proportion per DRG (%)Medical Service Income Proportion (%)Average Length of Stay (Days)Time Consumption IndexCost Consumption Index
2019 (Pre)28.5912.8331.87.780.980.78
2020 (Post)27.1811.3632.798.060.970.83
Table 8. Patient satisfaction pre and post implementation.
Table 8. Patient satisfaction pre and post implementation.
YearDischarged Patient Satisfaction (%)Number of Valid Complaints
2019 (Pre)97.5536
2020 (Post)98.4522
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Cai, M.; Yan, Z.; Wang, X.; Mao, B.; Pu, C. Construction of Hospital Diagnosis-Related Group Refinement Performance Evaluation Based on Delphi Method and Analytic Hierarchy Process. Hospitals 2025, 2, 20. https://doi.org/10.3390/hospitals2030020

AMA Style

Cai M, Yan Z, Wang X, Mao B, Pu C. Construction of Hospital Diagnosis-Related Group Refinement Performance Evaluation Based on Delphi Method and Analytic Hierarchy Process. Hospitals. 2025; 2(3):20. https://doi.org/10.3390/hospitals2030020

Chicago/Turabian Style

Cai, Mingchun, Zhengbo Yan, Xiaoli Wang, Bing Mao, and Chuan Pu. 2025. "Construction of Hospital Diagnosis-Related Group Refinement Performance Evaluation Based on Delphi Method and Analytic Hierarchy Process" Hospitals 2, no. 3: 20. https://doi.org/10.3390/hospitals2030020

APA Style

Cai, M., Yan, Z., Wang, X., Mao, B., & Pu, C. (2025). Construction of Hospital Diagnosis-Related Group Refinement Performance Evaluation Based on Delphi Method and Analytic Hierarchy Process. Hospitals, 2(3), 20. https://doi.org/10.3390/hospitals2030020

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