Disparities in gynecological care capacity in China: a national assessment using the structure-process-outcome framework.

This study is based on the SPO model, originally proposed by Avedis Donabedian in 1966 [ 12 ]. The model categorizes healthcare quality into three dimensions: Structure (foundational resources, including human capital, equipment, infrastructure, and information systems), Process (healthcare implementation, focusing on the execution of evidence-based diagnosis and treatment protocols), and Outcome (effectiveness of care, reflected by patient health improvements and experiences). The SPO model has been widely applied in healthcare quality evaluation, management, and improvement. Using this model, we systematically evaluated the gynecological care capacity in Chinese hospitals(Supplementary Fig.  1 ). A nationwide cross-sectional survey was conducted from February to December 2024 across 31 provinces, municipalities, and autonomous regions in mainland China. An online survey platform for mobile devices (self-adaptation for both smartphones and tablets) was developed with logic checks to identify illogical responses and incomplete records. A predefined coding system was embedded using a composite key index for participant identification, which is derived from healthcare facility identity document, questionnaire category, data acquisition time, interviewer number and respondent sequence number. Questionnaire administration A computerised direct data entry method was used for all questionnaires. We specifically developed a WeChat mini-programme for this survey, enabling participants to fill out the questionnaire by scanning a quick-response (QR) code directly through WeChat. This approach ensured the survey’s feasibility, ease of use and awareness of the time needed for completion [ 2 ]. China has a hierarchical healthcare system that operates across the country, composed of an extensive network extending from central to local levels. Public healthcare facilities are the foundation of China’s healthcare system. Considering the geographic diversity and willingness to participate, we included at least six county-level public hospitals, six municipal public hospitals and one provincial public hospital per province (municipal and provincial-level hospitals called non-county-level hospital). These public hospitals included general hospitals, maternal and child health hospitals and traditional Chinese medicine hospitals. This stratified sampling approach, combining geographic diversity with voluntary participation, aimed to ensure broad representativeness across different levels of China’s healthcare system, though the voluntary nature of participation may introduce some degree of selection bias. Three tailored questionnaires were designed respectively for healthcare institutions, healthcare providers, and patients in Supplementary Materials (Additional file 2 ) [ 2 ]. The institutional questionnaire collected data on departmental infrastructure, diagnostic capabilities, human resources, patient volume, and medication availability. The healthcare provider questionnaire assessed training background, workload, diagnostic knowledge, and familiarity with clinical guidelines. The patient questionnaire collected sociodemographic characteristics, clinical information, medical costs, and satisfaction. Data were directly entered through a smartphone-based system with embedded logic checks. According to the requirements of the CPEQ questionnaire, patients aged ≥ 15 years were included in this study, with additional inclusion criteria for the CPEQ-A (outpatients who had completed and paid for their medical consultation) and the CPEQ-I (inpatients who received a discharge notice) [ 13 ]. Descriptive statistics were used to summarize sample characteristics. Continuous variables were presented as mean±standard deviation if normally distributed, or as median (interquartile range) otherwise. Categorical variables were expressed as frequencies (percentages). Intergroup comparisons were conducted using the Student’s t-test, the Mann-Whitney U test, or the χ² test, as appropriate. Patient satisfaction was dichotomized into low satisfaction (0–8 points) and high satisfaction (9–10 points) based on the Global Rating Scale. This cutoff reflects standard practice in patient experience measurement, where scores of 9–10 are conventionally defined as high satisfaction in accordance with the “top-box” scoring approach widely used in this field [ 14 ]. Inpatient health outcomes were assessed using the EQ-5D visual analogue scale (VAS), a patient-reported instrument. At discharge, patients rated their health status at both admission and discharge on a scale of 0-100. Outcomes were categorized as improvement (discharge score > admission score), deterioration (discharge score < admission score), or no change (discharge score = admission score). Subgroup analyses were performed by patient type (outpatient/inpatient) and geographic region (Eastern/Central/Western China) [ 15 ]. A two-tailed p -value < 0.05 was considered statistically significant. All analyses were performed using R version 4.2.1 (The R Foundation for Statistical Computing Vienna, Austria) .

A total of 468 institutional questionnaires were initially collected, and after excluding duplicates and low-quality responses, 376 were retained, stratified by region (Eastern China n  = 120, Central China n  = 124, Western China n  = 132) and hospital level (non-county-level n  = 173, county-level n  = 203). Among the 7,437 healthcare provider questionnaires, 86 were excluded for ineligibility. A total of 13,385 outpatient and 10,741 inpatient questionnaires were included (Fig.  1 ). The institutional questionnaire took a median of 64.64 min to complete, while the healthcare provider and patient questionnaires took a median of 20.16 min, 8.11 min for outpatient questionnaire and 9.13 min for inpatient questionnaire respectively. Fig. 1 Flow chart for the inclusion and exclusion of research participants Flow chart for the inclusion and exclusion of research participants Overall, the median number of gynecological staff was 27.0 (IQR:20.0–39.0). Hospitals in Eastern China had significantly larger gynecological staff members than those in Central and Western China (32.0 vs. 25.0 vs. 24.0, p  < 0.001). Non-county-level hospitals also had significantly larger gynecological staff members compared with county-level hospitals (median: 33.0 vs. 22.0, p  < 0.001). Similar disparities were observed in the number of gynecologists (median: Eastern 15.0 vs. Western 10.0; non-county-level 16.0 vs. county-level 10.0, p  < 0.001), as well as the allocation of senior-level gynecologists (median: Eastern 6.0 vs. Western 3.0; non-county-level 6.0 vs. county-level 4.0, p  < 0.001) and intermediate-level gynecologists (median: Eastern 6.0 vs. Western 3.0; non-county-level 6.0 vs. county-level 4.0, p  < 0.001) (Table  1 ). Furthermore, the proportion of staff with a master’s degree or higher was greater in Eastern (21.1%) and non-county-level hospitals (20.8%) compared to Western (8.0%) and county-level hospitals (4.6%) ( p  < 0.001) (Table  1 ). Almost all hospitals were equipped with gynecological examination beds and bedside monitors. Also, availability of basic surgical instruments such as colposcopes and vacuum aspirators did not differ significantly by hospital levels. However, significant disparities were observed between non-county-level and county-level hospitals in the provision of key surgical and diagnostic equipment (Supplementary Fig.  2 ). Non-county-level hospitals demonstrated significantly higher availability of hysteroscopes (97.1% vs. 89.7%, p  = 0.008), laparoscopes (96.0% vs. 86.7%, p  = 0.003), LEEP devices (93.1% vs. 84.2%, p  = 0.013), ultrasonography machines (82.1% vs. 62.6%, p  < 0.001)and vaginal microecological testing equipment (64.2% vs. 48.8%, p  = 0.004) compared to county-level hospitals. Similar trends were observed for microscopes and pelvic floor rehabilitation devices ( p  < 0.05). In contrast, no significant regional differences in equipment allocation were identified (Supplementary Fig.  2 ). Eastern hospitals had the highest median outpatient visits (35,000.0 vs. 21,636.5 Central and 22,896.0 Western, p  = 0.009). Non-county-level hospitals saw significantly higher volumes than county-level hospitals (42,877.0 vs. 20,594.0, p  < 0.001). Regional and hierarchical disparities were also significant for inpatient discharges. Eastern hospitals had the highest median number of patients discharged (69.0 [IQR: 47.9–85.2]) compared to Central (66.7 [IQR: 47.7–84.4]) and Western hospitals (56.3 [IQR: 30.3–71.4], p  < 0.001); non-county-level hospitals also showed significantly higher discharge volumes than county-level hospitals (66.7 [IQR: 51.8–84.2] vs. 59.5 [IQR: 34.0-76.7], p  = 0.001) (Table  2 ). Although the proportion of minimally invasive surgeries was similar across groups (Eastern 50.0% [IQR: 34.8–66.7%], Central 48.2% [IQR: 33.9–65.9%], Western 50.0% [IQR: 33.3–80.0%], p  = 0.623; non-county-level 50.0% [IQR: 37.4–75.0%] vs. county-level 48.7% [IQR: 33.1–70.0%], p  = 0.172), surgical complexity differed. Hospitals in Western China had the highest proportion of Grade III surgeries (45.3%), while the proportion of Grade IV surgeries was higher in Eastern and Central China compared to Western China ( p  < 0.01). Non-county-level hospitals performed significantly higher proportions of both Grade III (41.6% vs. 34.9%) and Grade IV surgeries (15.4% vs. 8.7%) than county-level hospitals ( p  < 0.01) (Table  2 ). Implementation of clinical pathways varied significantly by hospital level. Except for uterine leiomyoma, all other conditions showed disparities. Non-county-level hospitals reported higher pathway implementation for cervical cancer (43.9% vs. 20.7%, p  < 0.001), benign ovarian tumors (69.9% vs. 55.2%, p  = 0.005), tubal pregnancy (75.7% vs. 59.6%, p  = 0.001), and adenomyosis (50.9% vs. 38.4%, p  = 0.02) (Supplementary Table 1 ). Among 3,094 gynecologists surveyed regarding 19 major gynecological and obstetric guidelines/consensus statements, most reported a high level of familiarity with the guidelines (combined “Familiar”/“Very familiar”: 70–80%). For visual clarity and clinical relevance, we showed 8 guidelines, which represented the most common and widely applied clinical scenarios in routine gynecological practice in China (Fig.  2 ). However, disparities were evident across regions and hospital levels ( p  < 0.05 for all guidelines). Physicians in Eastern and non-county-level hospitals more frequently reported “Very familiar”. For example, 28.7% in Eastern vs. 18.3% in Western hospitals, and 28.5% in non-county-level vs. 17.6% in county-level hospitals, reported full understanding of the Guideline for Diagnosis and Treatment of Cervical Cancer. Areas with lower awareness included female stress urinary incontinence (only 16.6% “Very familiar”) and female fertility preservation (17.7%). County-level hospitals also reported higher “unfamiliar” rates for specialized guidelines (e.g. various cancer guidelines and fertility preservation consensus). For example, for the Guideline for Diagnosis and Treatment of Endometrial Cancer, county-level hospitals reported 6.3% “unfamiliar”, while the “unfamiliar” rate was only 2.1% in non-county-level hospitals ( p  < 0.001) (Supplementary Table 2 ). Non-county-level hospitals reported more available beds (46.0 vs. 30.0, p  < 0.001). Small but significant differences were observed in the admission–discharge diagnostic consistency rate across regions ( p  = 0.008) and hospital levels ( p  = 0.039). In contrast, no significant difference was found in the consistency rate between preoperative and postoperative pathological diagnoses. The vast majority of hospitals (91.2%) had established follow-up systems for discharged patients (Supplementary Table 3 ). Table 1 Human resource allocation across different regions and hospital levels Variables Total surveyed hospitals ( n  = 376) Regions P Levels P Eastern region ( n  = 120) Central region ( n  = 124) Western region ( n  = 132) Non-district/county-level ( n  = 173) District/county-level ( n  = 203) Number of staff members in gynecology departments in 2023 (median [IQR])* 27.0 [20.0, 39.0] 32.0 [24.5, 49.0] 25.0 [18.0, 37.0] 24.0 [17.0, 32.0] < 0.001 33.0 [26.0, 54.0] 22.0 [16.0, 30.0] < 0.001 Number of gynecologists (median [IQR]) 12.0 [9.0, 18.0] 15.0 [11.0, 25.0] 12.0 [8.0, 17.0] 10.0 [8.0, 14.0] < 0.001 16.0 [11.0, 25.0] 10.0 [7.0, 14.0] < 0.001 Senior professional title** 4.5 [3.0, 8.0] 6.0 [4.0, 10.0] 5.0 [3.0, 8.0] 3.0 [2.0, 5.0] < 0.001 6.0 [4.0, 11.0] 4.0 [2.0, 6.0] < 0.001 Intermediate professional title** 4.0 [2.0, 8.0] 6.0 [3.0, 10.0] 5.0 [2.0, 8.0] 3.0 [2.0, 5.0] < 0.001 6.0 [3.0, 10.0] 4.0 [2.0, 6.0] < 0.001 Number of nurses (median [IQR]) 14.0 [10.0, 22.0] 17.0 [12.0, 28.2] 14.0 [10.0, 20.0] 13.5 [10.0, 19.0] 0.001 18.0 [13.0, 30.0] 13.0 [9.0, 17.0] < 0.001 Senior professional title 1.0 [0.0, 2.0] 2.0 [1.0, 4.0] 1.0 [0.0, 2.0] 1.0 [0.0, 1.2] < 0.001 2.0 [1.0, 3.0] 1.0 [0.0, 2.0] < 0.001 Intermediate professional title 5.0 [3.0, 10.0] 8.0 [4.0, 12.2] 6.0 [3.0, 9.0] 4.0 [2.0, 8.0] < 0.001 8.0 [5.0, 13.0] 4.0 [2.0, 7.0] < 0.001 Highest educational level Below bachelor’s degree 128 (1.7) 57 (2.5) 28 (1.1) 43 (1.6) < 0.001 45 (1.1) 83 (2.5) < 0.001 Bachelor’s degree / Vocational school diploma 6295 (84.6) 1738 (76.4) 2180 (86.1) 2377 (90.4) 3244 (78.1) 3051 (92.8) Master’s or doctoral degree 1014 (13.6) 480 (21.1) 324 (12.8) 210 (8.0) 862 (20.8) 152 (4.6) *Staff members in gynecology departments include gynecologists and nurses **Gynecologists’ professional titles comprises three levels: junior (resident), intermediate (attending), and senior (associate chief /chief) Human resource allocation across different regions and hospital levels *Staff members in gynecology departments include gynecologists and nurses **Gynecologists’ professional titles comprises three levels: junior (resident), intermediate (attending), and senior (associate chief /chief) Table 2 Number of patients treated and surgical composition across different regions and hospitals Variables Total surveyed hospitals ( n  = 376) Regions P Levels P Eastern region ( n  = 120) Central region ( n  = 124) Western region ( n  = 132) Non-district/county-level ( n  = 173) District/county-level ( n  = 203) Total number of patient serviced (median [IQR]) 25234.5 [11419.5, 59120.2] 35000.0 [16583.0, 81235.0] 21636.5 [10946.2, 50241.2] 22896.0 [10325.5, 45160.5] 0.009 42877.0 [18326.8, 80000.0] 20594.0 [7814.0, 35000.0] < 0.001 Number of patients discharged (median [IQR]) 63.2 [44.5, 80.7] 69.0 [47.9, 85.2] 66.7 [47.7, 84.4] 56.3 [30.3, 71.4] < 0.001 66.7 [51.8, 84.2] 59.5 [34.0, 76.7] 0.001 Proportion of discharged patients undergoing minimally invasive surgery (median [IQR]) 50.0 [33.9, 71.4] 50.0 [34.8, 66.7] 48.2 [33.9, 65.9] 50.0 [33.3, 80.0] 0.623 50.0 [37.4, 75.0] 48.7 [33.1, 70.0] 0.172 Proportion of discharged patients undergoing level III surgeries* (median [IQR]) 37.5 [22.8, 56.2] 40.0 [21.5, 51.7] 31.3 [20.0, 48.5] 45.3 [24.9, 67.2] 0.003 41.6 [27.3, 59.8] 34.9 [18.1, 50.9] 0.003 Proportion of discharged patients undergoing grade IV surgeries* (median [IQR]) 11.7 [5.5, 20.0] 12.9 [7.3, 21.4] 12.9 [6.5, 23.2] 9.0 [3.7, 16.7] 0.007 15.4 [9.0, 25.0] 8.7 [3.8, 14.6] < 0.001 * Surgical complexity was classified according to China’s National Surgical Procedure Classification System (National Health Commission of China), in which procedures are graded from I to IV based on technical difficulty and clinical risk; Grade III and Grade IV represent moderately complex and highly complex surgeries, respectively Number of patients treated and surgical composition across different regions and hospitals * Surgical complexity was classified according to China’s National Surgical Procedure Classification System (National Health Commission of China), in which procedures are graded from I to IV based on technical difficulty and clinical risk; Grade III and Grade IV represent moderately complex and highly complex surgeries, respectively Fig. 2 Gynecologists’ self-reported familiarity with gynecological clinical guidelines and expert consensus documents, stratified by hospital level (non-county vs. county) Gynecologists’ self-reported familiarity with gynecological clinical guidelines and expert consensus documents, stratified by hospital level (non-county vs. county) Overall, outpatient satisfaction (measured by a High Global Rating score of 9–10) was (74.5%). Significant regional differences were observed ( p  < 0.001), with the highest satisfaction rate reported by Central hospitals (78.2%), followed by Eastern (73.7%) and Western hospitals (72.4%). No significant difference was observed by hospital level (74.8% vs. 74.2%, p  = 0.433) (Supplementary Table 4 ). Most inpatients reported health improvements (62.8%). Health deterioration was more frequently observed in Western hospitals (14.0%) compared to Eastern (10.9%) and Central hospitals (12.4%, p  < 0.001). County-level inpatients had slightly higher improvement (63.5% vs. 62.3%) but also marginally higher deterioration (12.9% vs. 11.9%, p  = 0.016). Overall satisfaction was high (79.3%) and did not differ by hospital level ( p  = 0.645). Regional differences were significant ( p  < 0.001), with the highest satisfaction in Central hospitals (81.2%) (Supplementary Table 4 ).

Gynecological healthcare is a cornerstone of women’s health and an indicator of healthcare system quality [ 1 ]. In China, with its vast population and diverse geography, ensuring high-quality gynecological care remains a substantial public health challenge. Robust gynecological care capacity within hospitals is essential for early detection, effective treatment, and improved outcomes [ 2 ]. However, a comprehensive national evaluation that integrate structural, process, and outcome dimensions of gynecological care remains scarce. Existing evidence reveals substantial differences in gynecological service capacity across regions and hospital tiers. Essential equipment-including ultrasound systems, laparoscopes, and hysteroscopes-is far less available in rural and county-level hospitals than in tertiary urban institutions [ 3 ]. Similarly, access to essential medicines and the implementation of standardized clinical protocols differ considerably across regions [ 4 – 6 ]. Patients in western provinces and those treated in primary facilities consistently experience poorer outcomes than those in eastern regions or higher-tier hospitals [ 7 ]. Concentration of resources in economically developed urban centers has created a dual-tier system that entrenches quality inequities [ 8 ].Prior studies have mainly examined single dimensions of gynecological care-such as resource allocation, service utilization or guideline adherence-rather than integrating these within a unified evaluative framework [ 4 , 9 ]. Most are confined to specific regions or hospital types and do not link structural inputs to process quality and patient outcomes at the national level [ 10 ], leaving the pathways through which resource disparities shape clinical performance and outcomes largely unclear. To systematically examine these disparities, we applied the Structure-Process-Outcome (SPO) model, a well-established framework for healthcare quality assessment first proposed by Avedis Donabedian in the 1960s [ 11 ]. Unlike unidimensional approaches that examine a single aspect of care in isolation, the SPO framework integrates three complementary dimensions within a unified evaluative model, allowing it to identify the specific pathways through which structural disparities translate into process-level gaps and ultimately into differential patient outcomes, thereby providing a basis for targeted, system-level policy intervention. The three dimensions of the SPO model are conceptually distinct but inherently interdependent. Structural quality refers to the material and organizational conditions of care, including human resources, equipment, institutional policies, and management systems, which shape the behaviors of both providers and patients. Process quality encompasses the continuum of clinical activities, including prevention, diagnosis, treatment, rehabilitation, and patient education, and evaluates whether services are delivered adequately, promptly, and according to established standards. Outcome quality reflects the results of care, including changes in health status, health knowledge and behaviors, quality of life, and patient satisfaction, as well as institutional indicators such as clinical improvement rates, mortality, and infection rates. By integrating all three dimensions, the SPO framework goes beyond single indicators to show how structural constraints and process deficiencies together shape patient outcomes, thereby providing a practical basis for targeted, multilevel policy interventions. This framework enables identification of how limitations in resources or clinical practice contribute to variations in patient outcomes and guides system-level improvement. Using this framework, we conducted a nationwide cross-sectional survey across 31 provinces in mainland China to quantify disparities in human resources and equipment availability, characterize differences in service volume, surgical capability, and case complexity, assess physicians’ familiarity with and adherence to clinical guidelines, and determine how institutional capacity relates to patient outcomes. This multidimensional analysis identifies substantial regional and hierarchical inequities and provides an evidence base for optimizing resource allocation, strengthening standardized clinical practice and advancing equitable, high-quality gynecological care nationwide.

This study reveals multi-level quality disparities within China’s gynecological healthcare system. Structural inequalities lead to variations in diagnostic and treatment processes and may ultimately affect patient outcomes. Addressing these disparities requires coordinated efforts in resource allocation, knowledge translation, and quality evaluation reform to ensure equitable and high-quality healthcare for all women, regardless of geographic location or hospital level.

This nationwide cross-sectional study is the first to apply the Structure-Process-Outcome (SPO) framework to systematically evaluate gynecological care capacity across China, revealing marked regional and hierarchical disparities. Key findings include: structural resources-including workforce, equipment, and surgical capability-were highly concentrated in Eastern and non-county-level hospitals; process quality indicators, such as clinical pathway implementation and guideline familiarity, followed similar regional and hierarchical patterns as structural capacity; and patient satisfaction was uniformly high (74.5% among outpatients; 79.3% among inpatients), yet showed no meaningful association with objective clinical outcomes, demonstrating a clear “satisfaction paradox.” The gradient disparities in human resources and equipment configuration found in this study align with evidence from health system research in low- and middle-income countries (LMICs) globally [ 16 – 19 ]. Similar gradients in healthcare infrastructure have been described in national surveys of maternal and reproductive health services, as well as in WHO regional analyses linking equipment density and workforce skill mix with service quality [ 20 , 21 ].In China, Anand et al. systematically demonstrated as early as 2008 the triple inequality in health human resources concerning “quantity-quality-distribution” [ 22 ]. Our findings extend this framework to the gynecology sector, showing that structural imbalances directly translate into differences in clinical capability. More notably, these structural differences directly translate into variations in clinical capability-our data show that the proportion of Grade IV surgeries in non-county-level hospitals (15.4%) is significantly higher than in county-level hospitals (8.7%). This aligns with the logic of many researches on the relationship between surgical volume and mortality, suggesting that resource concentration may affect outcomes in complex case management [ 23 – 25 ]. Our data show that only 17.6% of county-level physicians reported being “very familiar” with cervical cancer guidelines, compared with 28.5% in non-county-level hospitals. Similar gaps were noted for specialized domains such as fertility preservation and urinary incontinence, consistent with prior studies documenting limited continuing medical education and specialist exposure among primary-level physicians in LMICs [ 26 – 28 ]. A cross-country comparison found that access to professional training and tele-education was a key determinant of process quality in women’s health services [ 29 ]. These disparities also mirror international findings that structural constraints restrict process quality, particularly in cancer care, where multidisciplinary coordination and advanced diagnostics are essential. For example, guideline adherence in cervical cancer management has been linked to resource availability and institutional culture [ 30 , 31 ]. Thus, addressing process gaps requires a dual strategy: strengthening the material foundation (equipment and personnel) while promoting continuous professional development. Evidence from the UK and Australia demonstrates that digital clinical decision support systems, tiered specialist networks, and remote training platforms can enhance process quality in peripheral hospitals [ 32 , 33 ]. Adapting similar models-such as telecolposcopy and virtual guideline training-could substantially narrow China’s intra-regional disparities. Despite notable gaps in structure and process, patient satisfaction was uniformly high nationwide. This disconnect between satisfaction and objective outcomes-most evident in Western regions where health deterioration was also highest-reflects the “satisfaction paradox” observed globally [ 34 ]. Studies in both high-income and LMIC settings have shown that satisfaction ratings are heavily influenced by interpersonal and contextual factors, such as provider attitude, waiting times, and facility cleanliness, rather than by clinical effectiveness [ 35 – 37 ]. In settings with limited access to high-quality care, patients may report higher satisfaction due to lower expectations or adaptive reference standards, even when outcomes are suboptimal [ 38 ]. This phenomenon has been documented in patient experience studies from India, Kenya, and rural China [ 36 ]. Our findings therefore highlight the inadequacy of relying solely on satisfaction metrics for quality assessment.From a health administration perspective, an overreliance on satisfaction metrics in hospital performance evaluation may inadvertently disincentivize structural improvements, particularly in facilities with poorer objective outcomes. If hospitals in underserved regions appear to perform well on satisfaction scores despite deficiencies in resources and clinical processes, administrators and policymakers may perceive less urgency to invest in structural upgrading. This misalignment between perceived and actual quality poses a systemic risk to equitable healthcare improvement. To ensure comprehensive evaluation, quality monitoring should integrate patient-reported outcome measures (PROMs) alongside satisfaction and clinical indicators [ 39 ]. Combining PROMs with process data could better capture whether improvements in service delivery translate into tangible health gains [ 40 ]. Such integration aligns with international frameworks like the OECD PaRIS [ 41 ]initiative and the WHO Quality of Care Network [ 42 ]. By constructing an evidence chain of “resource structure → clinical process → health outcomes” through the SPO framework, this study provides a basis for targeted interventions. Resource allocation should be tilted towards Western regions and county-level hospitals, with priority given to enhancing high-value equipment configuration of and specialist personnel training. To operationalize this, hub-and spoke referral networks could be established to connect county-level facilities with higher-tier hospitals, enabling timely patient transfer and specialist consultation. Process optimization should promote guideline implementation and quality control through digital platforms; including telemedicine services to extend specialist reach into underserved areas and continuous medical education programs to standardize clinical practice among primary care providers. Comprehensive quality assessment should integrate objective outcome indicators with patient-reported experiences to capture the full spectrum of care quality and equity, ensuring that performance evaluation systems create appropriate for structural improvement rather than relying solely on satisfaction metrics. These measures are highly consistent with the equity and quality goals emphasized in “Healthy China 2030“ [ 43 ] and also provide an evaluative framework that can be referenced for quality improvement in other medical specialties. This study has several notable strengths. First, it represents the largest and most comprehensive national evaluation of gynecological diagnostic and treatment capacity in China to date. Second, by applying the Structure-Process-Outcome (SPO) framework, it systematically elucidates the relationships among structural capacity, clinical processes, and patient outcomes, providing a holistic understanding of healthcare quality and equity. However, several limitations should be acknowledged. The cross-sectional design precludes causal inference and limits the ability to assess longitudinal changes in healthcare capacity. Voluntary hospital participation may have introduced selection bias, with better-resourced institutions more likely to respond. Finally, comparisons of health outcomes and clinical indicators across hospitals and regions ideally require case-mix adjustment for patient background characteristics-including diagnosis, disease severity, socioeconomic status, and treatment-to ensure comparability. Although hospital-level adjusted results are not reported in the present study, we have previously explored and published the risk adjustment methodology [ 44 ], and a dedicated follow-up study examining the comparison between unadjusted and risk-adjusted results is planned for future work.

Below is the link to the electronic supplementary material. Supplementary Material 1: Additional file 1.doc. Supplementary Fig. 1 : Conceptual framework for evaluating the quality of gynecological care based on the Structure-Process-Outcome (SPO) model. Supplementary Fig. 2 : Availability of key medical equipment in county-level and municipal-level hospitals. Supplementary Table 1 Types of diseases included in clinical pathway management across different regions and hospitals. Supplementary, Table 2 Knowledge of Clinical Practice Guidelines Among Gynecologists. Supplementary. Table 3 Types of diseases included in clinical pathway management across different regions and hospital. Supplementary Table 4 . Patient Outcomes and Satisfaction Supplementary Material 1: Additional file 1.doc. Supplementary Fig. 1 : Conceptual framework for evaluating the quality of gynecological care based on the Structure-Process-Outcome (SPO) model. Supplementary Fig. 2 : Availability of key medical equipment in county-level and municipal-level hospitals. Supplementary Table 1 Types of diseases included in clinical pathway management across different regions and hospitals. Supplementary, Table 2 Knowledge of Clinical Practice Guidelines Among Gynecologists. Supplementary. Table 3 Types of diseases included in clinical pathway management across different regions and hospital. Supplementary Table 4 . Patient Outcomes and Satisfaction Supplementary Material 2 Supplementary Material 2