Correlation analysis between single disease quality management and efficiency indicators of a tertiary hospital in China

Abstract

Background

In the framework of continuous improvement of medical quality, specific (single) disease management has become a key priority. However, the current research on the correlation of various items for single disease management and efficiency indicators of hospitals is insufficient, and an in-depth discussion is urgently needed to optimize management strategies. Therefore, we explored the impact of single disease quality management based on its implementation upon single disease reporting rate, average length of stay (ALOS), and time consumption index (TCI) in clinical departments, to provide a reference for the management of improving hospital efficiency.

Methods

A single-center, repeated cross-sectional study was conducted across all clinical departments of Chaozhou Central Hospital. Data on the implementation of single disease quality management items, ALOS, and TCI were collected from clinical departments over the second half of 2024. The t-tests were used to compare the single disease reporting rates, ALOS, and TCI in different groups, and the correlation between the management items and the efficiency indicators was further analyzed by using multiple linear regression methods.

Results

In this study, the collected data from 174 management records showed that the single disease reporting rate, ALOS, and TCI were (85.330 ± 26.171)%, (7.657 ± 3.708) days, and (1.022 ± 0.246), respectively. Compared with the unqualified groups, the qualified groups in the disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, and training plans all showed increased single disease reporting rates with statistically significant differences (P < 0.05). The results of the multiple linear regression models showed that across the three models, the single disease reporting rate was negatively correlated with both ALOS (Beta= -4.882, 95% CI: -6.959 to -2.804) and TCI ( Beta= -0.382, 95% CI: -0.522 to -0.242), with statistically significant differences (P < 0.05). Meanwhile, the scoring self-audit was correlated with TCI ( Beta= -0.106, 95% CI: -0.193 to -0.018), and this correlation was also statistically significant (P < 0.05).

Conclusions

Our single-center study demonstrates that there were differences in single disease reporting rates resulting from different implementations of single disease quality management items. Improving single disease reporting rates contributed to reducing ALOS and TCI. Multi-center studies in other healthcare institutions are still needed to verify the association between single disease quality management and efficiency indicators.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-025-13619-3.

Background

Enhancing healthcare quality and efficiency is a core issue in the current field of healthcare [1]. Since 2009, China has begun to establish a National Specific (Single) Disease Monitoring System aimed at monitoring and improving the quality of healthcare for specific diseases [2]. Specific (single) disease quality management is a model of comprehensive quality control and management from all aspects of healthcare services for a specific single disease. In China, documents such as the Evaluation Standard for Tertiary Hospitals (2022 Edition / 2025 Edition) and the Single Disease Monitoring Information Items (2020 Edition) have guided healthcare institutions in implementing healthcare quality management. According to Donabedian’s quality of healthcare framework [3], which comprises structure, process, and outcome, structural factors - such as hospital- and department-level working groups, equipment like computer systems, and institutional procedures - play a crucial role in single disease quality management. Additionally, the Single Disease Monitoring Information Items (2020 Edition) contain a large number of process quality indicators and outcome quality indicators, which require focused attention on the interactions between the structure, process, and outcome dimensions. Based on the concept of lean healthcare [4, 5], single disease quality management can be defined as the integration of multiple processes, including diagnosis and treatment, post-discharge reporting, review by clinical departments, management activities of clinical departments, review by the department of medical quality management, reporting to the National Specific (Single) Disease Monitoring System, as well as audit and feedback by the department of medical quality management. This approach enables full employee participation in identifying and eliminating problems in single disease quality management. The implementation of single disease quality management can promote the rational utilization of medical resources and enhance hospitals’ service awareness [6].

Tertiary hospitals are capable of providing high-level specialized medical services [7]. Healthcare quality plays a critical role in evaluating the performance of these institutions [8]. Many hospitals are undergoing audits in areas such as efficiency, quality, and patient outcomes to improve their performance [9]. In modern hospital management, quality and efficiency are two key elements that are interrelated and mutually restrictive. In global health systems, measurable quality indicators are increasingly used to assess and improve healthcare quality [10]. Performance indicators enable the conversion of quality into quantifiable indicators, while providing more streamlined information and facilitating cross-organizational comparisons [11]. Evaluating hospital efficiency through quality indicators can help hospital policymakers address inefficiencies through rational policies and assist administrators in understanding whether medical resources are optimally allocated and fully utilized [12]. Tertiary hospitals need to strive to reduce the length of stay and medical costs for inpatients while ensuring their medical safety [13]. Therefore, in-depth analysis of the relationship between single disease quality management and efficiency indicators such as average length of stay (ALOS) and time consumption index (TCI) holds significant practical significance for optimizing medical processes, allocating resources rationally, and improving the overall level of medical services.

Unlike the relatively large number of existing Chinese-language literatures, there are currently few studies on single disease quality management outside of China. Furthermore, there remains a scarcity of analyses focusing on the correlation between specific single disease quality management and efficiency indicators in tertiary hospitals. Therefore, our study takes a tertiary hospital in China as the research object, and preliminarily explores the intrinsic connection between single disease quality management and efficiency indicators by integrating multi-source data from the results of the checklist, the hospital’s operation management system, and the hospital’s single disease management system. Our study aims to provide a reference for hospital administrators to develop differentiated improvement strategies that promote synergistic improvements in healthcare quality and efficiency by identifying the potential drivers that influence the efficacy of single disease management.

Methods

Data sources

In this study, data on single disease quality management and efficiency indicators were collected (Using a repeated cross-sectional study design) for 29 departments in a tertiary hospital in China (Chaozhou Central Hospital) from July to December 2024, and a total of 174 data were obtained. Single disease quality management items data is obtained from monthly on-site audit forms. Epidata double-entry checklist data and consistency checks are used to ensure that the data is true and reliable. Monthly single disease reporting rates for each department are derived from the hospital’s single disease management system. Monthly efficiency indicators data for each department are exported from the hospital diagnosis related groups (DRG) management system.

Chaozhou Central Hospital’s single disease quality management implementation process

To strengthen single disease quality management, Chaozhou Central Hospital established a hospital-level working group for single disease quality management within the department of medical quality management, with the deputy hospital director serving as the group leader. As the department responsible for coordinating hospital-wide medical data, the department of medical quality management oversees the supervision of single disease quality management. Based on the fishbone diagram (Fig. S1) and Pareto chart (Fig. S2) derived from prior quality control circle (QCC) analysis, the department of medical quality management carried out quality improvements and developed a flowchart for the implementation of single disease quality management (Fig. S3). The specific implementation process is as follows.

Each clinical department develops a catalog list of single disease following the requirements of the National Specific (Single) Disease Monitoring System. When a patient with a specific single disease is hospitalized in a clinical department, the first doctor determines whether the patient matches the criteria for single disease management. Then the patient is treated according to the single disease diagnosis and treatment standard, implementation standard, and quality control requirements. At the same time, each quality control point is documented in the medical record as to whether or not it was performed according to the standard. Superior doctors and department directors carefully review the diagnosis and treatment process of each medical record through ward rounds to see if it conforms to the requirements of the single disease implementation standard, supervise and guide the diagnosis and treatment work of junior doctors, discover diagnosis and treatment defects promptly and rectify them, and continuously strengthen the self-monitoring consciousness of doctors and nurses at all grades. Within 10 days after the patient is discharged from the hospital, the doctor in charge of the bed will report the single disease quality control indicator information in the hospital’s single disease management system promptly, and the department administrator reviews the information and then submits it for reporting. Each department will query the single disease process and outcome quality indicators monthly through the hospital’s single disease management system, and fill in the indicator monitoring form for data monitoring. After discussing the management of single disease at the regular departmental quality control meeting, monthly self-audit scoring of single disease data, system tracking, case tracking, and effectiveness data are archived, and corrective measures are implemented.

The department of medical quality management regularly audits, summarizes, analyzes, and gives feedback on the implementation of single disease quality management in each clinical department, and implements corrective actions. Functional departments strengthen the education, training, and work guidance on single disease quality management for all medical staff in the hospital. Clinical department directors and management group members are trained and assessed to enable medical staff to master the steps, links, measures, tasks, time, and target requirements for the implementation of single disease quality management. The department of medical quality management provides monthly feedback on the implementation of single disease quality management in each department at regular hospital-level medical quality control meetings and in the Quality Control Bulletin. The department of medical quality management incorporates the monitoring results into the medical quality audit and assessment evaluation system, as well as subsequent follow-up on the implementation of corrective measures for medical quality management by each clinical department.

Single disease quality management audit

Before the formal audit, we conducted a pilot survey to refine the content of the checklist. The final version of the checklist was confirmed when three doctors in the department of medical quality management independently evaluated ten departments and achieved identical scoring rates. An instruction manual was prepared for the final version of the inspection form; after being uniformly confirmed by all doctors in the department of medical quality management, the document was stored in the hospital’s internal shared drive. During the hospital’s single disease training sessions, clinical doctors were instructed on the audit form and its content. Before the formal audit, doctors in the department of medical quality management are trained on the purpose, content, and precautions of the single disease quality management audit based on a consistent standard. After explaining the purpose and content of the audit to the single disease quality administrator of each clinical department, the doctor of the department of medical quality management will make an appointment in advance for the time and place of the audit. Face-to-face queries were used on the day of the audit to conduct on-site interviews, as well as paper and electronic data review and instruction. The implementation of single disease quality management items is captured and recorded on a paper audit form. After confirming the results of the audit with the single disease quality administrator of each clinical department on-site, the hospital-wide audit forms are retrieved and organized.

Variable of single disease quality management, efficiency indicators and department-related characteristics

Based on the relevant items in China’s Evaluation Standard for Tertiary Hospitals (2022 Edition), the department of medical quality management compiled and developed a Chinese version audit form for single disease quality management, which is used to conduct monthly evaluations of clinical departments. The results of the audit are categorized as qualified and unqualified. The type, unit, coding, and definition of variables such as the indicators for single disease quality management, ALOS, and TCI are presented in Table 1.

Table 1.

Summary table of variables

Variable	Type	Unit	Coding	Definition
Disease Catalog	Dichotomous variable	/	0 = No;1 = Yes	The department has a complete list of reported catalogs for single disease management.
Data monitoring	Dichotomous variable	/	0 = No;1 = Yes	The monitoring, collection, and reporting of data on single disease quality management indicators for the department be carried out on time.
Scoring self-audit	Dichotomous variable	/	0 = No;1 = Yes	The department conducts monthly self-audit and scoring for single disease quality management, focusing on department management and the trends of indicators
System tracking	Dichotomous variable	/	0 = No;1 = Yes	The department has a monthly evaluation of the trends in outcome quality indicators (trend graphs) for each disease category.
Case tracking	Dichotomous variable	/	0 = No;1 = Yes	The department has monthly case tracking, summarizing, and analyzing the quality of the single disease process in randomly selected cases for each disease type.
Effectiveness data	Dichotomous variable	/	0 = No;1 = Yes	The department has information (data or examples) to demonstrate the effectiveness of the improvement.
Training plans	Dichotomous variable	/	0 = No;1 = Yes	The department has material on the establishment of annual single disease quality management-related knowledge and management system training plans.
Training archive	Dichotomous variable	/	0 = No;1 = Yes	The department conducts single disease quality management training at least once a quarter and organizes materials about training sign-in, photos, learning materials, and assessment results.
Single disease reporting rate	Continuous variable	%	/	The ratio of the number of cases reported to the single disease management system for diseases qualified for single disease inclusion per unit of time to the cumulative total of the number of cases for diseases qualified for single disease inclusion in the same period
ALOS	Continuous variable	day	/	The value of the total number of bed days occupied by patients discharged in a certain category divided by the number of cases of a certain type of disease in the same period
TCI	Continuous variable	/	/	A weighted average of the ratio of the ALOS of each DRG group in the department to the ALOS of the corresponding DRG group in the region
CMI	Continuous variable	/	/	The average case weight of discharged patients in a hospital. A higher value indicates a higher degree of difficulty and severity of the diseases treated.
Bed utilization rate	Continuous variable	%	/	The ratio of actually occupied total bed days to the total number of bed days actually available.
Medical record archiving rate	Continuous variable	%	/	The proportion of medical records of discharged patients that are filed within 3 working days to the total number of medical records of discharged patients in the same period

Statistical analysis

SPSS 25.0 software was used for statistical analysis. Measurements are described as mean ± standard deviation; two independent samples t-tests were used for comparison of means between the 2 groups. The effect size, Cohen’s d value, was calculated (to measure the magnitude of the practical effect: |d| ≈ 0.2 indicates a small effect, |d| ≈ 0.5 indicates a medium effect, and |d| ≈ 0.8 indicates a large effect). The factors affecting ALOS and TCI were analyzed using multiple linear regression, and the method of screening variables was chosen as enter method. Model 1 was a univariate analysis; model 2 was a multivariate analysis using all variables; and model 3 was adjusted for variables with P < 0.05 in model 1 Sensitivity analysis was conducted via multivariate analysis: the identified influencing factors were incorporated with department-related variables to construct multiple linear regression Model 1; the identified influencing factors were incorporated with department-related variables and other single disease quality management variables to construct multiple linear regression Model 2 The test level α = 0.05 (two-sided).

Results

Comparison of the single disease reporting rate in different groups of single disease quality management items

The single disease reporting rate was (85.330 ± 26.171)% for 174 management data. The differences in single disease reporting rates for the qualified group in items of disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, and training plans were all statistically significant compared to the unqualified group (P < 0.05). The corresponding effect sizes were large, medium, medium, medium, medium, medium, and medium, respectively(Table 2).

Table 2.

Comparison of single disease reporting rate in different groups of single disease quality management items()

Single disease quality management item	Disease catalog	Data monitoring	Scoring self-audit	System tracking	Case tracking	Effectiveness data	Training plans	Training archive
Unqualified groups	(62.250 ± 39.197)%	(77.170 ± 30.711)%	(77.570 ± 33.103)%	(77.870 ± 31.921)%	(83.470 ± 27.984)%	(74.520 ± 34.210)%	(73.440 ± 32.470)%	(83.830 ± 27.172)%
Qualified groups	(89.020 ± 21.414)%	(92.770 ± 18.434)%	(89.510 ± 20.505)%	(90.980 ± 19.099)%	(92.970 ± 14.739)%	(91.020 ± 18.417)%	(88.430 ± 23.425)%	(87.850 ± 24.397)%
t value	-3.269	-4.015	-2.564	-3.155	-2.744	-3.471	-2.598	-0.980
P value	0.003*	< 0.001*	0.012*	0.002*	0.007*	0.001*	0.013*	0.328
Cohen’s d	-0.848	-0.616	-0.434	-0.498	-0.425	-0.601	-0.529	-0.156
Effect size	large	medium	medium	medium	medium	medium	medium	small

Comparison of efficiency indicator levels in different groups of single disease management items

The 174 management data showed an ALOS of 7.657 ± 3.708 and a TCI of 1.022 ± 0.246. The differences in ALOS among the departments with different groups of disease catalog, scoring self-audit, system tracking, effectiveness data, training plans, and training archive items were statistically significant (P < 0.05). The corresponding effect sizes were medium, medium, medium, small, medium, and small, respectively(Table 3). The differences in TCI among the departments with different groups of data monitoring, scoring self-audit, system tracking, effectiveness data, and training plan items were statistically significant (P < 0.05). The corresponding effect sizes were small, medium, medium, medium, and medium, respectively(Table 4).

Table 3.

Comparison of ALOS in different groups of single disease quality management items ()

Single disease quality management item	Disease catalog	Data monitoring	Scoring self-audit	System tracking	Case tracking	Effectiveness data	Training plans	Training archive
Unqualified groups	9.845 ± 5.326	8.053 ± 4.142	9.099 ± 4.953	8.841 ± 4.638	7.695 ± 3.757	8.808 ± 4.888	9.537 ± 4.668	8.111 ± 3.712
Qualified groups	7.307 ± 3.269	7.296 ± 3.243	6.879 ± 2.519	6.760 ± 2.477	7.502 ± 3.545	7.501 ± 2.738	7.167 ± 3.259	6.895 ± 3.601
t value	2.267	1.350	3.280	3.806	0.270	2.580	2.869	2.114
P value	0.032*	0.179	0.002*	0.001*	0.787	0.012*	0.006*	0.036*
Cohen’s d	0.574	0.204	0.565	0.560	0.053	0.330	0.589	0.333
Effect size	medium	small	medium	medium	small	small	medium	small

Table 4.

Comparison of TCI in different groups of single disease quality management items()

Single disease quality management item	Disease catalog	Data monitoring	Scoring self-audit	System tracking	Case tracking	Effectiveness data	Training plans	Training archive
Unqualified groups	1.140 ± 0.383	1.062 ± 0.288	1.117 ± 0.328	1.087 ± 0.307	1.026 ± 0.252	1.093 ± 0.322	1.109 ± 0.327	1.044 ± 0.249
Qualified groups	1.003 ± 0.212	0.985 ± 0.194	0.971 ± 0.167	0.973 ± 0.173	1.006 ± 0.222	0.985 ± 0.185	0.999 ± 0.215	0.985 ± 0.237
t value	1.706	2.053	3.253	2.890	0.432	2.395	2.418	1.522
P value	0.100	0.042*	0.002*	0.005*	0.666	0.019*	0.017*	0.130
Cohen’s d	0.443	0.314	0.561	0.458	0.084	0.411	0.398	0.243
Effect size	medium	small	medium	medium	small	medium	medium	small

Trends in ALOS and TCI between the low and high single disease reporting rate groups

The hospital-wide single disease reporting rate of Chaozhou Central Hospital showed a generally upward trend during the period from July to December 2024(Fig. 1A). Trends in single disease reporting rates for each department are shown in Fig. S1. Compared to the low reporting rate group, the high reporting rate group showed decreased ALOS (P < 0.05, Cohen’s d= -0.507)and TCI (P < 0.05, Cohen’s d= -0.531). See Fig. 1B and C.

Fig. 1.

Trends in ALOS and TCI between the low and high single disease reporting rate groups

Multiple regression analysis of factors influencing ALOS in different departments

With ALOS as the dependent variable, univariate regression analysis was conducted in Model 1, where each factor related to single disease quality management served as an independent variable respectively; in Model 2, multivariate regression analysis was performed using disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, training plans, and training archive as independent variables; in Model 3, multivariate regression analysis was carried out with disease catalog, scoring self-audit, system tracking, effectiveness data, training plans, and training archive (variables with P < 0.05 in Model 1) as independent variables. In all three models, a higher single disease reporting rate in the department was associated with a significantly lower risk of elevated ALOS (Model 1: Beta=-6.019, 95%CI: -7.949 ~ -4.089; Model 2: Beta=-5.443, 95%CI: -7.508 ~ -3.377; Model 3: Beta=-4.882, 95%CI: -6.959 ~ -2.804)(Fig. 2).

Fig. 2.

Forest plot of factors influencing ALOS

To exclude the influence of confounding factors such as department-specific characteristics, a sensitivity analysis was further conducted on the screened variable of single disease reporting rate. With ALOS as the dependent variable, multivariate regression analysis was performed in Model 1, using single disease reporting rate, departmental case mix index(CMI), bed utilization rate, and medical record archiving rate as independent variables; in Model 2, multivariate regression analysis was conducted with single disease reporting rate, departmental CMI, bed utilization rate, medical record archiving rate, disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, training plans, and training archive as independent variables. The results showed that a higher single disease reporting rate in the department was associated with a significantly lower risk of elevated ALOS (Model 1: Beta=-6.194, 95%CI: -8.088 ~ -4.300; Model 2: Beta=-5.746, 95%CI: -7.225 ~ -3.767)(Table S1).

Multiple regression analysis of factors influencing TCI in different departments

With TCI as the dependent variable, univariate regression analysis was conducted in Model 1, where each factor related to single disease quality management served as an independent variable respectively; in Model 2, multivariate regression analysis was performed using disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, training plans and training archive as independent variables; in Model 3, multivariate regression analysis was carried out with disease catalog, scoring self-audit, system tracking, effectiveness data, training plans and training archive (variables with P < 0.05 in Model 1) as independent variables. In all three models, the risk of elevated TCI was significantly lower in the scoring self-audit qualified group compared to the scoring self-audit unqualified group (model 1: Beta=-0.146, 95%CI: -0.220 ~ -0.072; model 2: Beta=-0.113, 95%CI: 0.201~ -0.025; model 3: Beta=-0.106, 95%CI: -0.193 ~ -0.018). The higher the single disease reporting rates in the department, the risk of elevated TCIs was significantly lower (model 1: Beta=-0.416, 95%CI: -0.543 ~ -0.290; model 2: Beta=-0.389, 95%CI: -0.530 ~ -0.249; model 3: Beta=-0.382, 95%CI: -0.522 ~ -0.242)(Fig. 3).

Fig. 3.

Forest plot of factors influencing TCI

A sensitivity analysis was further conducted on the screened variable of scoring self-audit and single disease reporting rate. With TCI as the dependent variable, multivariate regression analysis was performed in Model 1, using scoring self-audit, single disease reporting rate, departmental CMI, bed utilization rate, and medical record archiving rate as independent variables; in Model 2, multivariate regression analysis was conducted with scoring self-audit, single disease reporting rate, departmental CMI, bed utilization rate, medical record archiving rate, disease catalog, data monitoring, system tracking, case tracking, effectiveness data, training plans, and training archive as independent variables. The results showed that the risk of elevated TCI was significantly lower in the scoring self-audit qualified group compared to the scoring self-audit unqualified group (model 1: Beta=-0.102, 95%CI: -0.174 ~ -0.030; model 2: Beta=-0.112, 95%CI: -0.202~ -0.022). The higher the single disease reporting rates in the department, the risk of elevated TCI was significantly lower (model 1: Beta=-0.380, 95%CI: -0.512 ~ -0.248; model 2: Beta=-0.392, 95%CI: -0.536 ~ -0.248)(Table S2).

Discussions

Currently, challenges such as the accelerated aging of the population and changes in the disease spectrum have posed new requirements for tertiary medical institutions [14]. Systematic and data-driven quality improvement can enhance the quality and outcomes of healthcare services [15]; therefore, this study explores the potential influencing factors of medical quality improvement by analyzing data on single disease quality management and efficiency indicators. Chaozhou Central Hospital, the first tertiary-level A hospital in Chaozhou, serves a crucial function in regional disease management and safeguarding residents’ health. To enhance the refined management level of single disease quality, Chaozhou Central Hospital established a hospital-level quality management working group, which is responsible for formulating implementation plans and work systems, organizing the implementation and evaluation of department-level work, conducting training, and holding regular work meetings to promote the continuous improvement of single disease quality management. Using the lean healthcare concept, we enhanced functionalities, including data export in the single disease management system, pop-up alerts for unreported cases, and data administration. Using quality management tools such as a fishbone diagram and a Pareto chart obtained through prior QCC analysis, the department of medical quality management ultimately identified improvement measures for single disease quality management. We carried out quality improvements according to the confirmed flowchart and and the effectiveness was reinforced through monthly audits.

Given the current scarcity of research examining how single disease quality management relates to efficiency indicators in tertiary hospitals, we used a repeated cross-sectional approach to carry out a preliminary investigation of 29 clinical departments at Chaozhou Central Hospital. Our analysis of single disease quality management data found that carrying out these management items of disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, and training plans promoted higher single disease reporting rates in the clinical departments. Disease management treats each disease as an independent entity, with distinct cost patterns, therapies, and interventions throughout its course [16]. Therefore, the establishment of a disease catalog in the clinical department that matches the characteristics of the professional may help to carry out better disease management, and at the same time remind clinicians to report single disease cases accurately and promptly, thus improving the single disease reporting rate in the department. As measures to assess and improve the quality of health services, the monitoring of clinical indicator data allows meaningfully engaged clinicians to take steps towards quality of care improvement [17]. The ability to collect, analyze, and use data is an enhancer of quality improvement, whereas regular monitoring of the quality management situation helps to identify weaknesses and plan for improvements [18]. Therefore, data monitoring of single disease management indicators by the working group may help to improve the single disease reporting rates and quality improvement.

Medical audit contributes to improving the quality of systems and operations [19]. Among these, self-audit helps promote changes in practices [20]. Healthcare organizations can improve the quality of care by enhancing learning from mistakes and boosting staff camaraderie and morale [21]. Thus, scoring self-audit potentially promotes the improvement of the single disease reporting rate. Additionally, tracking and controlling outcomes and intermediate factors are key to quality management systems [22]. Quality indicators related to structure, process, or outcome in the Donabedian model facilitate the shift from subjective self-judgment to objective evaluation for assessing healthcare services [23]. Accordingly, our management working group analyzed the single disease process quality indicator and outcome quality indicator data derived from the single disease management system. The exported data is based on Single Disease Monitoring Information Items (2020 Edition) released by China, which includes process quality indicators and outcome quality indicators for 51 diseases. Among them, enhanced performance of process indicators correlates with superior outcomes [11]. Therefore, system tracking may facilitate the motivation of departments to report single disease by analyzing trends in indicators and case tracking for individual cases, especially process indicators. Effective feedback and proactive intervention play a positive role in quality improvement [24]. Determining the best thing entities via data analysis enhances the direct sharing of experiences and the dissemination of best practices [11]. Therefore, the archiving of effectiveness data by the working group is possibly beneficial to increasing single disease reporting rates.

Hospital quality management training can help healthcare professionals acquire knowledge and skills in quality management, while also enhancing quality awareness, managerial competence, and team collaboration [25]. Therefore, the establishment of training plans by the working group may help to improve the quality management of single disease and thereby increase single disease reporting rate. However, there was no statistically significant difference in single disease reporting rate between the unqualified and qualified groups of training archive items in our study. This may be related to deficiencies in training methods and content. Hospitals can organize quality management training on a wider range of topics according to the competency requirements of different groups, such as occupational categories and professional titles [25]. Reducing training frequency for senior physicians while increasing it for junior physicians may be effective in enhancing the reporting awareness among them.

Improving the single disease reporting rate probably enhances the monitoring of a broader range of disease data. Guaranteeing the rational documentation of quality indicator data, including epidemiological data (e.g., prevalence, diagnostic data, and treatment data), can increase clinicians’ engagement in quality management and facilitate best practices [26, 27]. The implementation of quality management system is one of the primary mechanisms for achieving performance improvement [9], while hospital efficiency plays a strategic role in healthcare organizations [28].

ALOS is a key indicator widely used in healthcare research to measure the amount of time a patient spends in the healthcare system from admission to discharge [29]. In all three regression models of our study, higher single disease reporting rates were associated with a significantly lower risk of elevated ALOS. This is similar to the results of Peng et al. [30] who studied the effects of single disease management in public hospitals in rural counties in Anhui Province, China, and found that the ALOS in the experimental group was 1.13 days to 8.83 days shorter than that of the control group, which may be related to the standard diagnostic and treatment procedures. Another study conducted in Fujian, China [31] also found that single disease payment improves medical quality by reducing ALOS. Given the current scarcity of research on single disease, most existing studies focus on how single disease payment systems and clinical treatment methods affect efficiency indicators. Based on the payment and diagnosis-treatment processes, we focused on the link of single disease reporting by hospitals after patients are discharged, excluded the influence of more confounding factors, and explored the association between single disease and efficiency indicators from a management perspective.

The TCI measures the ratio of a patient’s length of stay in the hospital to the regional standard, where a value of less than 1 indicates a short hospital stay [32]. In the three regression models of this study, the risk of elevated TCIs was significantly lower in the qualified group compared with the scoring self-audit item unqualified group. Self-audit scoring using checklists may facilitate self-reflection and quality improvement [33]. Our department-level self-audit checklist includes scores for various management items, process quality indicators, and outcome quality indicators. The working group can summarize key issues based on the scores of each item and assess the degree of work deficiencies according to the total score. Based on the analysis results, clinical departments may propose corrective actions, adjustments in treatment details, or other targeted improvement measures. Therefore, the scoring self-audit of single disease quality management possibly contributes to the improvement of single disease efficiency indicators. In the three regression models of this study, a higher single disease reporting rate in the department was associated with a significantly lower risk of an elevated TCI. Moderate evidence exists that public performance reporting informs healthcare provider selection and improves clinical outcomes and patient experience [34]. To avoid the influence of other confounding factors, we conducted sensitivity analysis using department-related variables, and the significant results remained unchanged. Therefore, an increase in the single disease reporting rate may help to improve efficiency indicators.

The ‘National Appraisal’ framework implemented in China encompasses three tiers of performance evaluation indicators [35]. Among the 26 key indicators, the single disease reporting rate and ALOS are included as indicators for healthcare quality and safety, and operational efficiency, respectively. Factors affecting the application of indicators comprise the selection of indicators, data gathering, analysis, the display of results, and the provision of information to decision-makers [36]. Therefore, to better implement national performance indicators, we enhanced the examination of eight items (such as the disease catalog, data monitoring, and scoring self-audit) within the single disease management program, which yielded positive outcomes.

Single disease quality management is a unique disease management model in China. Similarly, the Hospital Value-Based Purchasing (HVBP) in the United States and the Advancing Quality (AQ) measure in England also conduct process quality evaluations for multiple specific diseases, such as acute myocardial infarction and heart failure. Figueroa et al. [37] and Kristensen et al. [38] studied the effects of HVBP and AQ in practice, respectively, and identified no evidence that HVBP and AQ result in lower mortality. According to the Donabedian model, on the one hand, this suggests that the differences caused by disease management models across countries are related to structure; on the other hand, while focusing on the standardization of diagnosis and treatment processes, it remains necessary to explore how to more scientifically integrate patient outcome indicators into the evaluation system.

Implications for policy, practice, and research

Global disease surveillance systems are diverse and at different stages of maturity and integration [39]. In China, the National Specific (Single) Disease Monitoring System plays an important surveillance role in indicators for various diseases. Therefore, improving the single disease reporting rate contributes to the implementation of disease surveillance. Currently, China’s healthcare system attaches great importance to single disease quality management in areas such as the National Performance Appraisal for Tertiary Public Hospitals. Particularly in the context of the Evaluation Standard for Tertiary Hospitals (2025 Edition), increasing the proportion of monitoring indicators, the results of this study underscore even more strongly the greater in-depth application value of the single disease payment system, diagnosis and treatment workflows, and reporting management. This research offers a practical case for single disease quality management. Our study has a small sample size, and the practical significance of the significant findings acquired is yet to be determined. However, this suggests that beyond limited research on aspects such as payment systems, the potential impact of management details involved in the single disease reporting process on efficiency indicators deserves greater attention. Based on the research findings, we can provide a reference basis for policy-making. Developing checklists to improve self-audit and promoting the analysis of process quality indicators and outcome quality indicators before proposing corrective measures is conducive to hospital development. Hospital administrators and policymakers can improve the single disease reporting rate of clinical departments by introducing standardized training protocols or technology-enhanced solutions.

Strengths

There is still a lack of research on the correlation between single disease quality management and efficiency indicators. In our study, the correlation between single disease quality management with ALOS and TCI was analyzed comprehensively based on the important management approach of single disease quality management from multiple management items, such as disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, and training plans establishment, especially incorporating three regression statistical models. Our study is the first to explore the significant correlation between the single disease reporting rate with ALOS and TCI. By combining the Donabedian model with lean healthcare concepts, this study examines the improvement of process and outcome quality indicators on the basis of defined processes and structures. Our study can provide a reference for regional healthcare quality monitoring and health policy formulation, and further push forward the scientific and refined development of hospital management.

Limitations

First, our single-center study sample only includes clinical departments from one tertiary hospital in China and cannot represent other institutions. There are differences in medical resource allocation and management models among hospitals in different regions, which may lead to potential variations in the impact of single disease implementation in other hospitals. Second, this study only includes 174 data entries from 29 clinical departments, which may limit the statistical power, generalizability, and strength of causal associations of the research findings due to the small sample size. Third, this study is a repeated cross-sectional study, which cannot control for time-varying confounding factors at the department level. However, we adjusted for known confounding factors such as departmental CMI, bed utilization rate, and medical record archiving rate using a multivariable model, which supports the robustness of the conclusions to a certain extent. Meanwhile, the design of this study is limited by the uncertainty of causal inference in terms of internal validity and by the influence of sampling and temporal factors in terms of external validity. Therefore, when interpreting the results, overgeneralized causal conclusions should be avoided, and the scope of generalization should also be restricted to similar medical institutions and temporal contexts. Fourth, our single disease quality management situation was judged by the content of the interviews between different doctors from the department of medical quality management and doctors from clinical departments, and we cannot exclude the potential influence of subjective perceptions and bias. Although we have standardized perceptions as much as possible through a pilot survey and training, this may still partly affect the accuracy and authenticity of the data. Fifth, this study did not provide detailed breakdowns of the inspection findings concerning training content and frequency associated with training archives. More robust investigations are still needed to maximize the utilization of training strategies.

Conclusions

Based on the analysis of data from a repeated cross-sectional, single-center study, we conclude that variations in the implementation of single disease management components - including disease catalog, data monitoring, scoring self-audit, system tracking, case tracking, effectiveness data, and training plans - are associated with differences in the single disease reporting rate. Our study found that improving the single disease reporting rate may help reduce ALOS and TCI, as well as improve efficiency indicators, but further research is still needed (Fig. 4). Policymakers and medical institutions may consider strengthening the structural, process, and outcome management of single disease to improve resource allocation and patient outcomes. In subsequent follow-up studies, we will expand the sample size, incorporate additional variables such as department size, medical quality management state, collaborate with other medical institutions, and conduct in-depth, long-term multicenter cohort studies. Meanwhile, we will explore more objective indicators to assess the compliance status of departmental single disease management, thereby providing more reliable evidence for the association with efficiency indicators.

Fig. 4.

The implementation of single disease quality management has contributed to improving hospital efficiency

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

ALOS

Average length of stay

AQ

Advancing Quality

CMI

Case mix index

DRG

Diagnosis related groups

HVBP

Hospital Value-Based Purchasing

QCC

Quality control circle

TCI

Time consumption index

Author contributions

SL: Investigation, Methodology, Supervision, Writing–original draft, Writing–review & editing; JG: Investigation, Methodology, Supervision, Writing–original draft, Writing–review & editing; HC: Investigation, Methodology, Writing–review & editing; YJ: Investigation, Methodology, Writing–review & editing; LZ: Conceptualization, Formal analysis, Investigation, Methodology, Writing–original draft, Writing–review & editing.

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

The data and materials that support the findings of this study are available on reasonable request from the corresponding author.

Declarations

Ethics approval and consent to participate

In accordance with local legislation and institutional requirements, this study does not require ethical approval. This is because the study only uses aggregated clinical department management data and does not involve sensitive/private information of individual patients. The use of the data in this study by the department of medical quality management has been approved by Chaozhou Central Hospital, and the departments involved have been anonymized.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.