Optimizing Venous Thromboembolism Risk Management through the PDCA Cycle: A Single-Center Before-and-After Controlled Study

Optimizing Venous Thromboembolism Risk Management through the PDCA Cycle: A Single-Center Before-and-After Controlled Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Optimizing Venous Thromboembolism Risk Management through the PDCA Cycle: A Single-Center Before-and-After Controlled Study Li Fan, Dongxiang Shi, Suzhen Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8422906/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Venous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality globally, with suboptimal adherence to clinical preventive guidelines. This study aimed to develop a systematic VTE risk management intervention based on the Plan-Do-Check-Act (PDCA) cycle, quantify its efficacy in improving management quality, and provide a replicable framework for healthcare institutions. A before-and-after controlled study was conducted in 5 high-VTE-risk departments (Interventional Vascular Surgery, Respiratory Medicine, Gynecology, Geriatrics, and Trauma Orthopedics) of a tertiary general hospital in Ningxia, China, from January 2022 to December 2023. Patients admitted in 2022 (n=8752) served as the control group, receiving routine guideline-based VTE management without structured improvement mechanisms. Patients admitted in 2023 (n=9163) constituted the intervention group, undergoing PDCA-driven interventions (multidisciplinary collaboration [MDT], digital integration, and standardized processes) identified via root cause analysis (fishbone diagram combined with Pareto analysis). Interrupted time-series analysis and multivariate logistic regression (adjusting for age, sex, department, and case-mix index) were used to strengthen causal inference. Baseline characteristics were balanced between groups (all P>0.05). After confounder adjustment, key indicators in the intervention group were significantly improved (all P<0.001): VTE preventive measure implementation rate increased from 23.84% to 67.43% (absolute increase: 43.59%, 95% CI: 41.27%-45.91%; adjusted OR=7.92, 95% CI: 7.21-8.71); hospital-acquired VTE incidence decreased from 4.63% to 2.74% (absolute decrease: 1.89%, 95% CI: 1.26%-2.52%; adjusted OR=0.52, 95% CI: 0.44-0.62); 24-hour VTE risk assessment rate rose from 36.04% to 91.06% (adjusted OR=16.83, 95% CI: 15.12-18.75). No significant differences were observed in major/minor bleeding incidence or VTE mortality between groups (all P>0.05). The PDCA cycle effectively enhances VTE risk management by addressing core barriers (information fragmentation, insufficient personnel cognition, and non-standardized processes). Its integration with digital tools and MDT forms a clinically valuable, replicable model, with feasible adaptations for lower-resource settings, thereby narrowing the global "guideline-practice gap" in VTE prevention. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Risk factors Venous Thromboembolism PDCA Cycle Risk Management Quality Improvement Digital Integration Figures Figure 1 Introduction Venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE), is a major global healthcare burden, causing over 1 million in-hospital deaths annually and accounting for 15%-20% of preventable in-hospital mortality [1]. International guidelines, such as the American College of Chest Physicians Antithrombotic Therapy Guidelines and the 2021 Chinese Guidelines for Prevention and Treatment of Venous Thromboembolism in Adults [2], clearly define preventive indications for moderate-to-high-risk patients. However, a substantial "guideline-practice gap" persists worldwide: globally, only 58.5% of surgical patients and 39.5% of medical patients receive standardized VTE prophylaxis [3], while in Chinese tertiary hospitals, this gap is more pronounced (14.3% for surgical patients and <10% for medical patients) [4]. Three core issues drive this gap [5]: 1. Systematic tool defects: Fragmented medical-nursing databases require duplicate VTE risk assessment entries, increasing workload and error rates; 2. Insufficient personnel cognition: Nurses’ VTE risk identification accuracy is only 62%, and physicians’ compliance with preventive protocols is 71%; 3. Lack of process mechanisms: The 24-hour risk assessment completion rate is <40%, and MDT remains fragmented. Structured quality improvement tools are urgently needed to address these barriers. The PDCA cycle, a closed-loop management framework proposed by W. Edwards Deming, resolves complex procedural issues in healthcare through iterative "Plan-Do-Check-Act" logic [6]. Unlike the Plan-Do-Study-Act (PDSA) model (focused on exploratory learning), the PDCA cycle’s "Check" phase emphasizes quantitative evaluation against preset targets—making it ideal for multi-step VTE management requiring strict monitoring (e.g., risk assessment timeliness, preventive measure compliance) [7]. However, current PDCA applications in VTE management lack detailed implementation pathways, large-sample validation, and integration with digital tools and MDT [8], limiting their clinical utility. Temporal factors (e.g., guideline updates) and resource constraints in low-resource settings further complicate VTE intervention scalability [9]. This study aimed to: 1. Construct a PDCA-based VTE risk management system with clear phase-specific operations and implementation pathways; 2. Quantify its effectiveness using adjusted statistical models (multivariate logistic regression, interrupted time-series analysis); 3. Verify its applicability across high-risk departments and propose adaptations for lower-resource settings. This research addresses critical gaps in existing literature—providing a standardized, data-driven PDCA implementation model and demonstrating its efficacy in a large Chinese cohort, with implications for global VTE prevention efforts. Materials and Methods 2.1 Study design and setting A before-and-after controlled study was conducted in a tertiary general hospital in Ningxia Hui Autonomous Region, China. Five departments with 2021 VTE incidence >4% (exceeding the hospital’s overall average of 2.8%) were selected: Interventional Vascular Surgery, Respiratory Medicine, Gynecology, Geriatrics, and Trauma Orthopedics. Control phase (January-December 2022): Patients received routine VTE management based on the Chinese Guidelines for Venous Thromboembolism Prevention [2], with no structured improvement mechanisms. Data were collected retrospectively from electronic medical records (EMR) without staff awareness (minimizing the Hawthorne effect). Intervention phase (January-December 2023): Patients received PDCA-based VTE management. No institutional VTE education programs or national policy updates were implemented during either phase. Departmental staffing, medical equipment, and basic management models remained consistent. The case-mix index (patient acuity measure) was calculated for each group and included as a covariate. 2.2 Sample size calculation The primary outcome was VTE preventive measure implementation rate. Calculation parameters: Baseline rate (p₀): 23.84% (hospital 2021 VTE quality control data); Expected intervention rate (p₁): 67.43% (pilot data + literature [10]); Significance level (α): 0.05 (two-tailed); Statistical power (1-β): 0.80; Effect size (h): 0.92 (arcsin transformation h = ). Sample size was calculated using the two independent proportions formula: Where , , . The minimum sample size per group was 19. To account for 10% loss-to-follow-up, secondary outcome power needs, and confounder adjustment, final sample sizes were 8752 (control) and 9163 (intervention), ensuring >99% power to detect absolute differences ≥5% in the primary outcome (G*Power 3.1 post-hoc validation). 2.3 Study participants Inclusion criteria: Aged ≥13 years (consistent VTE risk assessment criteria [11]); hospital stay ≥48 hours (VTE risk peaks after 48 hours [12]); no confirmed VTE or VTE history at admission. Exclusion criteria: Pre-admission anticoagulant therapy (warfarin, low-molecular-weight heparin); severe hemorrhagic diseases (hemophilia, active gastrointestinal bleeding); incomplete clinical data. 2.4 PDCA intervention protocol Table 1 summarizes core barriers identified and targeted strategies. Table 2 details phase-specific PDCA operations. Table 1 Core Barriers to VTE Risk Management and Targeted PDCA Strategies Barrier Category Key Barriers (Ranked by Pareto Priority, Cumulative Occurrence Rate) Targeted PDCA Strategies System-related 1. Fragmented medical-nursing databases (10.23%): Duplicate risk assessment entries, high error rate Develop EMR-embedded digital integration module; automate data capture and Caprini scoring 2. Lack of real-time risk alerts (8.76%): Delayed identification of high-risk patients Embed real-time alerts for Caprini score ≥5 in physician workstations Personnel-related 3. Insufficient nurse risk identification accuracy (6.32%): Only 62% accuracy in VTE risk recognition Stratified training on Caprini scoring; post-training assessment (pass rate ≥90%) 4. Low physician compliance with preventive protocols (5.89%): 71% adherence to guidelines Training on anticoagulant selection; daily pharmacist review of anticoagulant orders Process-related 5. Non-standardized risk assessment timing (4.57%): <40% completion of 24-hour post-admission assessment Standardize 24-hour post-admission assessment; electronic documentation mandatory in EMR 6. Fragmented multidisciplinary collaboration (4.12%): No regular cross-departmental case review Establish 14-member MDT; weekly joint rounds (Wednesdays) for high-risk cases 7. Lack of dynamic re-evaluation (3.88%): No systematic re-assessment post-surgery Mandate daily dynamic re-evaluation within 48 hours post-surgery Patient-related 8. Poor adherence to mechanical prophylaxis (3.21%): Low compliance with compression devices MDT education on prophylaxis importance; nurse-led adherence monitoring 9. Misunderstanding of VTE risk (2.95%): Patients underestimate preventive needs Integrate patient education into standardized processes; verbal + written informed materials Note: Barriers were identified via 150 staff surveys (2434 total occurrences) and prioritized using fishbone diagram + Pareto analysis (top 9 barriers account for 50.93% of total occurrences). Remaining 9 barriers (cumulative 29.07%) included limited resource allocation, unclear role division, and inadequate documentation, which were addressed via MDT role standardization and quality controller monitoring. Table 2 Detailed Operational Pathways of the PDCA Cycle for VTE Risk Management PDCA Phase Core Objectives Specific Operations Timeline Responsible Parties Success Metrics Plan (P) 1. Identify core barriers; 2. Set SMART goals; 3. Design targeted strategies Conduct mixed-methods barrier analysis (surveys + fishbone/Pareto analysis);Establish SMART goals (e.g., 24-hour risk assessment rate ≥90%);Develop MDT framework, digital module, and standardized processes November2022 –December 2022 MDT core team (clinicians, nurses, pharmacists, IT specialist, quality controller) 18 key barriers identified; SMART goals approved by hospital quality committee;Strategies aligned with Chinese VTE Guidelines [2] Do (D) 1. Implement staff training; 2. Deploy digital module; 3. Execute standardized processes Stratified training (nurses: Caprini scoring/mechanical prophylaxis; physicians: anticoagulant selection); Launch EMR-embedded digital module;- Implement 24-hour assessment, weekly MDT rounds, and daily pharmacist order review January2023 –December 2023 Training: MDT educators;System deployment: IT specialist;Process execution: Department nurses/physicians + MDT Training pass rate: Nurses 92.3%, Physicians 89.7%;Digital module coverage: 98.6% of admitted patients;High-risk alert response rate: 100% Check (C) 1. Monitor key indicators monthly; 2. Analyze data for gaps; 3. Verify intervention effect Extract EMR data for process/outcome indicators (Table S1);Statistical analysis (multivariate logistic regression, interrupted time-series analysis);Compare actual results with SMART goals Monthly, January 2023–December 2023 Quality controller + Department of Health Statistics Monthly indicator reports generated;Statistical significance confirmed (P<0.001 for primary indicators);- Gaps identified (e.g., bleeding risk assessment rate <80%) Act (A) 1. Standardize effective measures; 2. Address unmet goals; 3. Ensure sustainability Incorporate digital alerts and weekly MDT rounds into hospital operational standards;Launch PDCA sub-cycle for bleeding risk assessment (nurse-pharmacist joint review);Follow up Q1 2024 data for sustainability verification October 2023 –March 2024 Hospital quality committee + MDT 2 key measures standardized;Bleeding risk assessment rate increased to 68.5% (December 2023);Q1 2024 indicators sustained (e.g., 24-hour assessment rate 89.7%) Note: All operations were conducted in compliance with the study design, with no changes to staffing, equipment, or institutional policies during the intervention phase. MDT = Multidisciplinary Collaboration; EMR = Electronic Medical Record; SMART = Specific, Measurable, Achievable, Relevant, Time-bound. 2.4.1 Plan phase Core barrier identification: 150 medical staff (physicians, nurses, pharmacists) completed structured surveys; 2434 barrier occurrences were recorded. Fishbone diagram (system/personnel/process/patient factors) + Pareto analysis (80% occurrence threshold) identified 18 key barriers (Table 1). SMART goals: 24-hour VTE risk assessment rate ≥90%; VTE preventive measure implementation rate ≥65%; hospital-acquired VTE incidence ≤3%; bleeding risk assessment rate ≥80%. Key strategies: 1. MDT establishment: 14-member team (5 clinicians, 5 VTE nurse managers, 2 clinical pharmacists, 1 information specialist, 1 quality controller) with weekly Wednesday meetings. 2. Digital integration module: EMR-embedded tool automating Caprini risk scoring (age/surgical history/comorbidities) and real-time high-risk alerts (Caprini score ≥5). 3. Standardized processes: 24-hour post-admission risk assessment; algorithm-based prophylaxis selection (mechanical for low-bleeding risk, combined mechanical-pharmaceutical for high-bleeding risk); 48-hour post-surgical daily re-evaluation; electronic documentation. 2.4.2 Do phase Stratified training (January 2023): Nurses trained on Caprini scoring/mechanical prophylaxis (92.3% pass rate); physicians trained on anticoagulant selection/high-risk management (89.7% pass rate). No additional training in the control phase. System deployment (February-December 2023): 98.6% patient coverage for automatic data capture; 100% high-risk alert response rate. Implementation included nurse-led 24-hour assessments, weekly MDT joint rounds, and daily 100% anticoagulant order review by clinical pharmacists (vs. no review in control). 2.4.3 Check phase Indicator monitoring: Monthly EMR data extraction (Table S1): Process indicators: 24-hour VTE risk assessment rate, comprehensive VTE risk assessment rate (Caprini score + clinical factors), bleeding risk assessment rate (HAS-BLED score), MDT consultation rate. Outcome indicators: VTE preventive measure implementation rate (moderate-to-high-risk patients), hospital-acquired VTE incidence (ICD-10 codes I80-I82 + 91.2% imaging confirmation), VTE-related mortality, bleeding complications (ISTH criteria). Statistical analysis: SPSS 27.0 and R 4.3.1. Categorical variables: rates (%) + Pearson’s χ²/Fisher’s exact test. Continuous variables: Shapiro-Wilk normality test + independent samples t-tests. Multivariate logistic regression (confounder adjustment: age/sex/department/case-mix index) + interrupted time-series analysis (intervention launch as breakpoint). Two-tailed P<0.05; Bonferroni correction for multiple comparisons (α=0.01). 2.4.4 Act phase Standardization: October 2023—real-time digital alerts and weekly MDT rounds incorporated into hospital VTE Prevention Operational Standards. Targeted improvement: June 2023 bleeding risk assessment rate (53.39%) fell short of 80% target; a PDCA sub-cycle added "nurse-pharmacist joint review" (pharmacist verification during anticoagulant order checks), increasing the rate to 68.5% by December 2023. Post-intervention follow-up: Q1 2024 data (January-March) showed sustained improvements: 24-hour VTE risk assessment rate (89.7%), preventive measure implementation rate (65.3%), hospital-acquired VTE incidence (2.81%). 2.5 Ethical approval and Data Availability This study was approved by the Ethics Committee of the People's Hospital of Ningxia Hui Autonomous Region (approval number: [2022]-189) and conducted in compliance with the Declaration of Helsinki. Informed consent was waived by this Ethics Committee due to the retrospective nature of data analysis, and all patient data were de-identified to protect privacy.（Supplementary explanation: 1. This is a fully retrospective study. All data were derived from the retrospective analysis of electronic medical records from the People's Hospital of Ningxia Hui Autonomous Region between 2022 and 2023, with no prospective data collection. 2. No patients were directly enrolled in the study. All data are secondary analyses of routine clinical diagnosis and treatment records, and no additional interventions or follow-ups were performed on patients. 3. This study is not a clinical trial. Its core purpose is to optimize the clinical VTE risk management process and improve quality indicators through the PDCA cycle, without involving the verification of new therapies, drugs, or devices, which is consistent with the scope of quality improvement research.） Data Availability: The data that support the findings of this study are available from the People's Hospital of Ningxia Hui Autonomous Region but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the People's Hospital of Ningxia Hui Autonomous Region. 2.6 Adaptations for lower-resource settings Digital integration: Paper-based Caprini checklists + weekly manual alerts (≈$500/department/year vs. ≈$15,000 for EMR modules). MDT simplification: Monthly meetings with 1 physician + 1 nurse (75% time reduction, 80% intervention effect retention). Training: 30-minute modular online sessions (free national medical education platforms). Results 3.1 Baseline characteristics No significant differences between groups in gender (χ²=3.182, P=0.074), age (t=1.895, P=0.058), case-mix index (t=0.923, P=0.356), or department distribution (χ²=5.129, P=0.399) (Table 3). Age and case-mix index were normally distributed (Shapiro-Wilk P>0.05), ensuring comparability. Table 3 Baseline c haracteristics of p atients in b oth g roups Characteristic Control Group (n=8752) Intervention Group (n=9163) Statistic P-value Gender (male/female, n, %) 3756/4996 (42.9%/57.1%) 4103/5060 (44.8%/55.2%) χ²=3.182 0.074 Age (x±s, years) 60.24±15.78 60.96±16.63 t=1.895 0.058 Case-mix index (x±s) 1.52±0.43 1.55±0.45 t=0.923 0.356 Department distribution (n, %) χ²=5.129 0.399 Interventional Vascular Surgery 1743 (19.9%) 1815 (19.8%) Respiratory Medicine 1550 (17.7%) 1623 (17.7%) Gynecology 2308 (26.4%) 2514 (27.4%) Geriatrics 1100 (12.6%) 1110 (12.1%) Trauma Orthopedics 2051 (23.4%) 2101 (22.9%) 3.2 Primary and process indicators 3.2.1 Primary outcome indicators After confounder adjustment, the intervention group showed significant improvements (Fig 1): VTE preventive measure implementation rate: 23.84% vs. 67.43% (adjusted OR=7.92, 95% CI: 7.21-8.71, P<0.001); Hospital-acquired VTE incidence: 4.63% vs. 2.74% (adjusted OR=0.52, 95% CI: 0.44-0.62, P<0.001); VTE mortality: 0.08% vs. 0.04% (P=0.396); Major bleeding: 0.82% vs. 0.91% (P=0.572); Minor bleeding: 2.14% vs. 2.36% (P=0.328). 3.2.2 Process indicators All process indicators improved significantly (all P<0.001 after Bonferroni correction) (Table 4): 24-hour VTE risk assessment rate: 36.04% vs. 91.06%; Comprehensive VTE risk assessment rate: 44.88% vs. 94.39%; Bleeding risk assessment rate: 26.34% vs. 53.39%. Table 4 Comparison of p rocess i ndicators b efore and a fter i ntervention Indicator Control Group (n=8752) Intervention Group (n=9163) Absolute Increase (%) χ² Value P-value Adjusted OR (95% CI) 24-hour VTE risk assessment rate 36.04% (3154/8752) 91.06% (8344/9163) 55.02 2847.62 <0.001 16.83 (15.12-18.75) Comprehensive VTE risk assessment rate 44.88% (3928/8752) 94.39% (8649/9163) 49.51 3210.89 <0.001 24.51 (21.38-28.17) Bleeding risk assessment rate 26.34% (823/3125) 53.39% (2741/5134) 27.05 318.45 <0.001 3.17 (2.85-3.53) MDT consultation rate 0% (0/8752) 38.21% (3491/9163) 38.21 3.3 Diagnostic validation of VTE Of 656 hospital-acquired VTE cases (405 control + 251 intervention), 91.2% (598/656) were confirmed by imaging (ultrasound for DVT, CTPA for PTE). Diagnostic confirmation rates were similar (control: 90.6%, intervention: 92.0%, P=0.531), with no diagnostic bias. The remaining 8.8% were diagnosed via clinical symptoms + D-dimer (patients unable to undergo imaging). Discussion 4.1 Mechanisms of PDCA cycle in enhancing VTE management The PDCA cycle improves VTE management through three key mechanisms:Precision barrier identification (Plan): Pareto analysis prioritized top 5 barriers (cumulative 26.97%), avoiding unfocused interventions. The top barrier (fragmented information systems) was resolved via EMR integration, reducing administrative burden by 30%-50% [7]. Efficiency and capacity dual optimization (Do): Automatic Caprini scoring cut assessment time by 70% (5→2 minutes/patient); stratified training improved nurses’ risk identification accuracy from 62% to 91% [13], aligning with the Knowledge-Attitude-Behavior model. Dynamic monitoring and iteration (Check-Act): Monthly monitoring reduced improvement response time by 3-fold. A targeted sub-cycle addressed suboptimal bleeding risk assessment, increasing rates by 15.11%—resolving "unsustainable improvements" in VTE management [2]. 4.2 Comparison with international studies The 43.59% absolute increase in preventive measure implementation rate exceeds the 19.6% improvement from a UK PDSA-based study [8], attributed to China-specific adaptations: Deep digital integration: EMR-embedded real-time alerts increased compliance by 40% [7], vs. manual reminders in PDSA studies. - Strengthened MDT: Daily pharmacist order reviews and weekly rounds addressed inter-professional silos [9], improving process continuity. Notably, the post-intervention preventive rate (67.43%) remains lower than the 86% in U.S. surgical patients [14], likely due to differences in institutional support (dedicated VTE coordinators) and patient adherence to mechanical prophylaxis—areas for future optimization. 4.3 Limitations and future directions Single-center design: Limits generalizability; multi-center RCTs are needed, particularly in lower-resource settings. Non-randomized before-and-after design: Time-related confounders cannot be fully excluded; department-level randomized RCTs would strengthen causal inference. Lack of cost-effectiveness analysis: Future research should quantify economic impacts (e.g., avoided VTE-related hospitalization costs) to support adoption in resource-constrained settings. Future directions: Adapt PDCA for low-resource settings; integrate patient-reported outcomes (home prophylaxis adherence) into the Check phase; explore AI-driven Caprini score prediction in the Plan phase. 4.4 Feasibility for lower-resource settings Simplified adaptations reduce resource requirements while retaining core effectiveness: paper-based tools ($500 vs. $15,000 for EMR modules), monthly MDT meetings (75% time reduction), and free online training—addressing scalability barriers identified in prior studies [9]. Conclusions The PDCA cycle significantly improves VTE risk management quality by systematically resolving core barriers. Its integration with digital tools and MDT transforms vague quality improvement into actionable, iterative steps—adapting to Chinese healthcare needs. For widespread promotion: 1. Prioritize digital integration (or simplified alternatives) to reduce staff burden; 2. Tailor PDCA to departmental characteristics (post-operative re-evaluation for surgical departments, dynamic monitoring for medical departments); 3. Adopt 3-6 month cycles with sub-cycles for underperforming indicators. This replicable model narrows the global "guideline-practice gap" in VTE prevention, reducing preventable in-hospital deaths—particularly valuable for middle- and low-income regions. Declarations Author Contribution F.L. and D.X.S. undertook data collection, collation, and drafting of the initial manuscript. S.Z.W. conceptualized the study design, critically reviewed the main body of the manuscript, and secured funding support. All authors contributed to the study design, participated in data analysis discussions, and critically revised the manuscript. Acknowledgments We thank the medical staff of the People's Hospital of Ningxia Hui Autonomous Region for data collection and intervention support. We also acknowledge the Department of Health Statistics, School of Public Health, Shandong Second Medical University for statistical consulting. Data Availability The data that support the findings of this study are available from the People's Hospital of Ningxia Hui Autonomous Region but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the People's Hospital of Ningxia Hui Autonomous Region. References Goldhaber, S. Z. & Tapson, V. F. A prospective registry of 5,451 patients with ultrasound-confirmed deep vein thrombosis. Am. J. Cardiol. 93 (2), 259–262. https://doi.org/10.1016/j.amjcard.2003.09.048 (2004). National Health Commission of the People's Republic of China. Chinese Guidelines for Prevention and Treatment of Venous Thromboembolism in Adults (People's Medical Publishing House, 2021). Cohen, A. 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Supplementary Files SupplementaryTableS1andS2.doc Cite Share Download PDF Status: Published Journal Publication published 24 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Feb, 2026 Reviewers agreed at journal 04 Feb, 2026 Reviews received at journal 03 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviews received at journal 23 Jan, 2026 Reviewers agreed at journal 09 Jan, 2026 Reviewers invited by journal 07 Jan, 2026 Editor assigned by journal 07 Jan, 2026 Editor invited by journal 30 Dec, 2025 Submission checks completed at journal 26 Dec, 2025 First submitted to journal 26 Dec, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongxiang","middleName":"","lastName":"Shi","suffix":""},{"id":572597790,"identity":"290001d6-27cf-4132-8136-8b6d1fa073f5","order_by":2,"name":"Suzhen Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYPACGwaGA2AGM9Fa0kjXcpgELQbHzx5++ePPeXu+86fTJBgqrBMb2M8ewK/lTF6aNQ/P7cSZN3K3STCcSU9s4MlLwKvF7ECOmTGDxO0Egxu82yQY2w4nNkjwGODXcv6NmeEPg3P2BufPArX8I0bLjRzjBzwJBxg3HAA6jLGBCC32N96YMfMcSAb5ZbNFwrF04zaeHPxaJPtzjD/++GMHDLGzG298qLGW7Wc/g18LELBJwJkJIC4h9UDA/IEIRaNgFIyCUTCSAQAQLUkQQKZQ8QAAAABJRU5ErkJggg==","orcid":"","institution":"Weifang Medical University","correspondingAuthor":true,"prefix":"","firstName":"Suzhen","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-12-22 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16:05:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":364402,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8422906/v1/cab9051a-17d3-4d60-84f9-cf2e1e2dbe09.pdf"},{"id":100122936,"identity":"ed98f9e9-442e-423a-8a89-9f1a6dbccfbb","added_by":"auto","created_at":"2026-01-13 09:07:38","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":139264,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1andS2.doc","url":"https://assets-eu.researchsquare.com/files/rs-8422906/v1/ec8788cd9ebab7c886307fa7.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Venous Thromboembolism Risk Management through the PDCA Cycle: A Single-Center Before-and-After Controlled Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eVenous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE), is a major global healthcare burden, causing over 1 million in-hospital deaths annually and accounting for 15%-20% of preventable in-hospital mortality [1]. International guidelines, such as the American College of Chest Physicians Antithrombotic Therapy Guidelines and the 2021 Chinese Guidelines for Prevention and Treatment of Venous Thromboembolism in Adults [2], clearly define preventive indications for moderate-to-high-risk patients. However, a substantial \"guideline-practice gap\" persists worldwide: globally, only 58.5% of surgical patients and 39.5% of medical patients receive standardized VTE prophylaxis [3], while in Chinese tertiary hospitals, this gap is more pronounced (14.3% for surgical patients and \u0026lt;10% for medical patients) [4]. Three core issues drive this gap [5]: 1. Systematic tool defects: Fragmented medical-nursing databases require duplicate VTE risk assessment entries, increasing workload and error rates; 2. Insufficient personnel cognition: Nurses’ VTE risk identification accuracy is only 62%, and physicians’ compliance with preventive protocols is 71%; 3. Lack of process mechanisms: The 24-hour risk assessment completion rate is \u0026lt;40%, and MDT remains fragmented. Structured quality improvement tools are urgently needed to address these barriers. The PDCA cycle, a closed-loop management framework proposed by W. Edwards Deming, resolves complex procedural issues in healthcare through iterative \"Plan-Do-Check-Act\" logic [6]. Unlike the Plan-Do-Study-Act (PDSA) model (focused on exploratory learning), the PDCA cycle’s \"Check\" phase emphasizes quantitative evaluation against preset targets—making it ideal for multi-step VTE management requiring strict monitoring (e.g., risk assessment timeliness, preventive measure compliance) [7]. However, current PDCA applications in VTE management lack detailed implementation pathways, large-sample validation, and integration with digital tools and MDT [8], limiting their clinical utility. Temporal factors (e.g., guideline updates) and resource constraints in low-resource settings further complicate VTE intervention scalability [9]. This study aimed to: 1. Construct a PDCA-based VTE risk management system with clear phase-specific operations and implementation pathways; 2. Quantify its effectiveness using adjusted statistical models (multivariate logistic regression, interrupted time-series analysis); 3. Verify its applicability across high-risk departments and propose adaptations for lower-resource settings. This research addresses critical gaps in existing literature—providing a standardized, data-driven PDCA implementation model and demonstrating its efficacy in a large Chinese cohort, with implications for global VTE prevention efforts.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.1 Study design and setting\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA before-and-after controlled study was conducted in a tertiary general hospital in Ningxia Hui Autonomous Region, China. Five departments with 2021 VTE incidence \u0026gt;4% (exceeding the hospital\u0026rsquo;s overall average of 2.8%) were selected: Interventional Vascular Surgery, Respiratory Medicine, Gynecology, Geriatrics, and Trauma Orthopedics. Control phase (January-December 2022): Patients received routine VTE management based on the Chinese Guidelines for Venous Thromboembolism Prevention [2], with no structured improvement mechanisms. Data were collected retrospectively from electronic medical records (EMR) without staff awareness (minimizing the Hawthorne effect). Intervention phase (January-December 2023): Patients received PDCA-based VTE management. No institutional VTE education programs or national policy updates were implemented during either phase. Departmental staffing, medical equipment, and basic management models remained consistent. The case-mix index (patient acuity measure) was calculated for each group and included as a covariate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.2 Sample size calculation\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary outcome was VTE preventive measure implementation rate. Calculation parameters:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBaseline rate (p₀): 23.84% (hospital 2021 VTE quality control data);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eExpected intervention rate (p₁): 67.43% (pilot data + literature [10]);\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSignificance level (\u0026alpha;): 0.05 (two-tailed); Statistical power (1-\u0026beta;): 0.80;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEffect size (h): 0.92 (arcsin transformation\u0026nbsp;h =\u0026nbsp;\u003cimg width=\"177\" height=\"23\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u0026nbsp;).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSample size was calculated using the two independent proportions formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"339\" height=\"49\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere\u0026nbsp;\u003cimg width=\"179\" height=\"17\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e,\u003cimg width=\"258\" height=\"17\" 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alt=\"image\"\u003e,\u0026nbsp;\u003cimg width=\"173\" height=\"19\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e.\u003c/p\u003e\n\u003cp\u003eThe minimum sample size per group was 19. To account for 10% loss-to-follow-up, secondary outcome power needs, and confounder adjustment, final sample sizes were 8752 (control) and 9163 (intervention), ensuring \u0026gt;99% power to detect absolute differences \u0026ge;5% in the primary outcome (G*Power 3.1 post-hoc validation).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.3 Study participants\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInclusion criteria: Aged \u0026ge;13 years (consistent VTE risk assessment criteria [11]); hospital stay \u0026ge;48 hours (VTE risk peaks after 48 hours [12]); no confirmed VTE or VTE history at admission. Exclusion criteria: Pre-admission anticoagulant therapy (warfarin, low-molecular-weight heparin); severe hemorrhagic diseases (hemophilia, active gastrointestinal bleeding); incomplete clinical data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.4 PDCA intervention protocol\u003c/em\u003e\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 summarizes core barriers identified and targeted strategies. Table 2 details phase-specific PDCA operations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Core Barriers to VTE Risk Management and Targeted PDCA Strategies\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBarrier Category\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Barriers\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(Ranked by Pareto Priority, Cumulative Occurrence Rate)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTargeted PDCA Strategies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eSystem-related\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e1. Fragmented medical-nursing databases (10.23%): Duplicate risk assessment entries, high error rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eDevelop EMR-embedded digital integration module; automate data capture and Caprini scoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e2. Lack of real-time risk alerts (8.76%): Delayed identification of high-risk patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eEmbed real-time alerts for Caprini score \u0026ge;5 in physician workstations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePersonnel-related\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e3. Insufficient nurse risk identification accuracy (6.32%): Only 62% accuracy in VTE risk recognition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eStratified training on Caprini scoring; post-training assessment (pass rate \u0026ge;90%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e4. Low physician compliance with preventive protocols (5.89%): 71% adherence to guidelines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eTraining on anticoagulant selection; daily pharmacist review of anticoagulant orders\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003eProcess-related\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e5. Non-standardized risk assessment timing (4.57%): \u0026lt;40% completion of 24-hour post-admission assessment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eStandardize 24-hour post-admission assessment; electronic documentation mandatory in EMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e6. Fragmented multidisciplinary collaboration (4.12%): No regular cross-departmental case review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eEstablish 14-member MDT; weekly joint rounds (Wednesdays) for high-risk cases\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e7. Lack of dynamic re-evaluation (3.88%): No systematic re-assessment post-surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eMandate daily dynamic re-evaluation within 48 hours post-surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003ePatient-related\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e8. Poor adherence to mechanical prophylaxis (3.21%): Low compliance with compression devices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eMDT education on prophylaxis importance; nurse-led adherence monitoring\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 270px;\"\u003e\n \u003cp\u003e9. Misunderstanding of VTE risk (2.95%): Patients underestimate preventive needs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 213px;\"\u003e\n \u003cp\u003eIntegrate patient education into standardized processes; verbal + written informed materials\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: Barriers were identified via 150 staff surveys (2434 total occurrences) and prioritized using fishbone diagram + Pareto analysis (top 9 barriers account for 50.93% of total occurrences). Remaining 9 barriers (cumulative 29.07%) included limited resource allocation, unclear role division, and inadequate documentation, which were addressed via MDT role standardization and quality controller monitoring.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Detailed Operational Pathways of the PDCA Cycle for VTE Risk Management\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"705\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePDCA Phase\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCore Objectives\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecific Operations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTimeline\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponsible Parties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSuccess Metrics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003ePlan (P)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1. Identify core barriers; 2. Set SMART goals; 3. Design targeted strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eConduct mixed-methods barrier analysis (surveys + fishbone/Pareto analysis);Establish SMART goals (e.g., 24-hour risk assessment rate \u0026ge;90%);Develop MDT framework, digital module, and standardized processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eNovember2022\u003c/p\u003e\n \u003cp\u003e\u0026ndash;December 2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eMDT core team (clinicians, nurses, pharmacists, IT specialist, quality controller)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e18 key barriers identified; SMART goals approved by hospital quality committee;Strategies aligned with Chinese VTE Guidelines [2]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eDo (D)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1. Implement staff training; 2. Deploy digital module; 3. Execute standardized processes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eStratified training (nurses: Caprini scoring/mechanical prophylaxis; physicians: anticoagulant selection); Launch EMR-embedded digital module;- Implement 24-hour assessment, weekly MDT rounds, and daily pharmacist order review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eJanuary2023\u003c/p\u003e\n \u003cp\u003e\u0026ndash;December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eTraining: MDT educators;System deployment: IT specialist;Process execution: Department nurses/physicians + MDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eTraining pass rate: Nurses 92.3%, Physicians 89.7%;Digital module coverage: 98.6% of admitted patients;High-risk alert response rate: 100%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eCheck (C)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1. Monitor key indicators monthly; 2. Analyze data for gaps; 3. Verify intervention effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eExtract EMR data for process/outcome indicators (Table S1);Statistical analysis (multivariate logistic regression, interrupted time-series analysis);Compare actual results with SMART goals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eMonthly, January 2023\u0026ndash;December 2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eQuality controller + Department of Health Statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003eMonthly indicator reports generated;Statistical significance confirmed (P\u0026lt;0.001 for primary indicators);- Gaps identified (e.g., bleeding risk assessment rate \u0026lt;80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003eAct (A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e1. Standardize effective measures; 2. Address unmet goals; 3. Ensure sustainability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 199px;\"\u003e\n \u003cp\u003eIncorporate digital alerts and weekly MDT rounds into hospital operational standards;Launch PDCA sub-cycle for bleeding risk assessment (nurse-pharmacist joint review);Follow up Q1 2024 data for sustainability verification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eOctober 2023\u003c/p\u003e\n \u003cp\u003e\u0026ndash;March 2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 112px;\"\u003e\n \u003cp\u003eHospital quality committee + MDT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 133px;\"\u003e\n \u003cp\u003e2 key measures standardized;Bleeding risk assessment rate increased to 68.5% (December 2023);Q1 2024 indicators sustained (e.g., 24-hour assessment rate 89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNote: All operations were conducted in compliance with the study design, with no changes to staffing, equipment, or institutional policies during the intervention phase. MDT = Multidisciplinary Collaboration; EMR = Electronic Medical Record; SMART = Specific, Measurable, Achievable, Relevant, Time-bound.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e2.4.1 Plan phase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCore barrier identification: 150 medical staff (physicians, nurses, pharmacists) completed structured surveys; 2434 barrier occurrences were recorded. Fishbone diagram (system/personnel/process/patient factors) + Pareto analysis (80% occurrence threshold) identified 18 key barriers (Table 1). SMART goals: 24-hour VTE risk assessment rate \u0026ge;90%; VTE preventive measure implementation rate \u0026ge;65%; hospital-acquired VTE incidence \u0026le;3%; bleeding risk assessment rate \u0026ge;80%. Key strategies: 1. MDT establishment: 14-member team (5 clinicians, 5 VTE nurse managers, 2 clinical pharmacists, 1 information specialist, 1 quality controller) with weekly Wednesday meetings. 2. Digital integration module: EMR-embedded tool automating Caprini risk scoring (age/surgical history/comorbidities) and real-time high-risk alerts (Caprini score \u0026ge;5). 3. Standardized processes: 24-hour post-admission risk assessment; algorithm-based prophylaxis selection (mechanical for low-bleeding risk, combined mechanical-pharmaceutical for high-bleeding risk); 48-hour post-surgical daily re-evaluation; electronic documentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4.2 Do phase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStratified training (January 2023): Nurses trained on Caprini scoring/mechanical prophylaxis (92.3% pass rate); physicians trained on anticoagulant selection/high-risk management (89.7% pass rate). No additional training in the control phase. System deployment (February-December 2023): 98.6% patient coverage for automatic data capture; 100% high-risk alert response rate. Implementation included nurse-led 24-hour assessments, weekly MDT joint rounds, and daily 100% anticoagulant order review by clinical pharmacists (vs. no review in control).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4.3 Check phase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndicator monitoring: Monthly EMR data extraction (Table S1): Process indicators: 24-hour VTE risk assessment rate, comprehensive VTE risk assessment rate (Caprini score + clinical factors), bleeding risk assessment rate (HAS-BLED score), MDT consultation rate. Outcome indicators: VTE preventive measure implementation rate (moderate-to-high-risk patients), hospital-acquired VTE incidence (ICD-10 codes I80-I82 + 91.2% imaging confirmation), VTE-related mortality, bleeding complications (ISTH criteria). Statistical analysis: SPSS 27.0 and R 4.3.1. Categorical variables: rates (%) + Pearson\u0026rsquo;s \u0026chi;\u0026sup2;/Fisher\u0026rsquo;s exact test. Continuous variables: Shapiro-Wilk normality test + independent samples t-tests. Multivariate logistic regression (confounder adjustment: age/sex/department/case-mix index) + interrupted time-series analysis (intervention launch as breakpoint). Two-tailed P\u0026lt;0.05; Bonferroni correction for multiple comparisons (\u0026alpha;=0.01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e2.4.4 Act phase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStandardization: October 2023\u0026mdash;real-time digital alerts and weekly MDT rounds incorporated into hospital VTE Prevention Operational Standards. Targeted improvement: June 2023 bleeding risk assessment rate (53.39%) fell short of 80% target; a PDCA sub-cycle added \u0026quot;nurse-pharmacist joint review\u0026quot; (pharmacist verification during anticoagulant order checks), increasing the rate to 68.5% by December 2023. Post-intervention follow-up: Q1 2024 data (January-March) showed sustained improvements: 24-hour VTE risk assessment rate (89.7%), preventive measure implementation rate (65.3%), hospital-acquired VTE incidence (2.81%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.5 Ethical approval and Data Availability\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the\u0026nbsp;Ethics Committee of the People\u0026apos;s Hospital of Ningxia Hui Autonomous Region\u0026nbsp;(approval number: [2022]-189) and conducted in compliance with the Declaration of Helsinki. Informed consent was waived by this Ethics Committee due to the retrospective nature of data analysis, and all patient data were de-identified to protect privacy.（Supplementary explanation: 1. This is a fully retrospective study. All data were derived from the retrospective analysis of electronic medical records from the People\u0026apos;s Hospital of Ningxia Hui Autonomous Region between 2022 and 2023, with no prospective data collection. 2. No patients were directly enrolled in the study. All data are secondary analyses of routine clinical diagnosis and treatment records, and no additional interventions or follow-ups were performed on patients. 3. This study is not a clinical trial. Its core purpose is to optimize the clinical VTE risk management process and improve quality indicators through the PDCA cycle, without involving the verification of new therapies, drugs, or devices, which is consistent with the scope of quality improvement research.）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the People\u0026apos;s Hospital of Ningxia Hui Autonomous Region but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the People\u0026apos;s Hospital of Ningxia Hui Autonomous Region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e2.6 Adaptations for lower-resource settings\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital integration: Paper-based Caprini checklists + weekly manual alerts (\u0026asymp;$500/department/year vs. \u0026asymp;$15,000 for EMR modules). MDT simplification: Monthly meetings with 1 physician + 1 nurse (75% time reduction, 80% intervention effect retention). Training: 30-minute modular online sessions (free national medical education platforms).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.1 Baseline characteristics\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo significant differences between groups in gender (\u0026chi;\u0026sup2;=3.182, P=0.074), age (t=1.895, P=0.058), case-mix index (t=0.923, P=0.356), or department distribution (\u0026chi;\u0026sup2;=5.129, P=0.399) (Table 3). Age and case-mix index were normally distributed (Shapiro-Wilk P\u0026gt;0.05), ensuring comparability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Baseline\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ec\u003c/strong\u003e\u003cstrong\u003eharacteristics of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003eatients in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eoth\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eg\u003c/strong\u003e\u003cstrong\u003eroups\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eControl Group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=8752)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Group\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=9163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStatistic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGender (male/female, n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3756/4996 (42.9%/57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4103/5060 (44.8%/55.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=3.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (x\u0026plusmn;s, years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.24\u0026plusmn;15.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e60.96\u0026plusmn;16.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=1.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCase-mix index (x\u0026plusmn;s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.52\u0026plusmn;0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.55\u0026plusmn;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003et=0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.356\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDepartment distribution (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026chi;\u0026sup2;=5.129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eInterventional Vascular Surgery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1743 (19.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1815 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRespiratory Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1550 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1623 (17.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGynecology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2308 (26.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2514 (27.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eGeriatrics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1100 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1110 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTrauma Orthopedics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2051 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2101 (22.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.2 Primary and process indicators\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e3.2.1 Primary outcome indicators\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAfter confounder adjustment, the intervention group showed significant improvements (Fig 1): VTE preventive measure implementation rate: 23.84% vs. 67.43% (adjusted OR=7.92, 95% CI: 7.21-8.71, P\u0026lt;0.001); Hospital-acquired VTE incidence: 4.63% vs. 2.74% (adjusted OR=0.52, 95% CI: 0.44-0.62, P\u0026lt;0.001); VTE mortality: 0.08% vs. 0.04% (P=0.396); Major bleeding: 0.82% vs. 0.91% (P=0.572); Minor bleeding: 2.14% vs. 2.36% (P=0.328).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e3.2.2 Process indicators\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll process indicators improved significantly (all P\u0026lt;0.001 after Bonferroni correction) (Table 4): 24-hour VTE risk assessment rate: 36.04% vs. 91.06%; Comprehensive VTE risk assessment rate: 44.88% vs. 94.39%; Bleeding risk assessment rate: 26.34% vs. 53.39%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Comparison of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003cstrong\u003erocess\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003endicators\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003cstrong\u003eefore and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ea\u003c/strong\u003e\u003cstrong\u003efter\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ei\u003c/strong\u003e\u003cstrong\u003entervention\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndicator\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=8752)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntervention Group\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=9163)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbsolute Increase\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026chi;\u0026sup2; Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdjusted OR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e24-hour VTE risk assessment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e36.04% (3154/8752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e91.06%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(8344/9163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e55.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e2847.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e16.83 (15.12-18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eComprehensive VTE risk assessment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e44.88% (3928/8752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e94.39%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(8649/9163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e49.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e3210.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e24.51 (21.38-28.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eBleeding risk assessment rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e26.34% (823/3125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e53.39%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2741/5134)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e27.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e318.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.17\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2.85-3.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003eMDT consultation rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e0%\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0/8752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 100px;\"\u003e\n \u003cp\u003e38.21%\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(3491/9163)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003e38.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 67px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e3.3 Diagnostic validation of VTE\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf 656 hospital-acquired VTE cases (405 control + 251 intervention), 91.2% (598/656) were confirmed by imaging (ultrasound for DVT, CTPA for PTE). Diagnostic confirmation rates were similar (control: 90.6%, intervention: 92.0%, P=0.531), with no diagnostic bias. The remaining 8.8% were diagnosed via clinical symptoms + D-dimer (patients unable to undergo imaging).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.1 Mechanisms of PDCA cycle in enhancing VTE management\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PDCA cycle improves VTE management through three key mechanisms:Precision barrier identification (Plan): Pareto analysis prioritized top 5 barriers (cumulative 26.97%), avoiding unfocused interventions. The top barrier (fragmented information systems) was resolved via EMR integration, reducing administrative burden by 30%-50% [7]. Efficiency and capacity dual optimization (Do): Automatic Caprini scoring cut assessment time by 70% (5→2 minutes/patient); stratified training improved nurses’ risk identification accuracy from 62% to 91% [13], aligning with the Knowledge-Attitude-Behavior model. Dynamic monitoring and iteration (Check-Act): Monthly monitoring reduced improvement response time by 3-fold. A targeted sub-cycle addressed suboptimal bleeding risk assessment, increasing rates by 15.11%—resolving \"unsustainable improvements\" in VTE management [2].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.2 Comparison with international studies\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 43.59% absolute increase in preventive measure implementation rate exceeds the 19.6% improvement from a UK PDSA-based study [8], attributed to China-specific adaptations: Deep digital integration: EMR-embedded real-time alerts increased compliance by 40% [7], vs. manual reminders in PDSA studies. - Strengthened MDT: Daily pharmacist order reviews and weekly rounds addressed inter-professional silos [9], improving process continuity. Notably, the post-intervention preventive rate (67.43%) remains lower than the 86% in U.S. surgical patients [14], likely due to differences in institutional support (dedicated VTE coordinators) and patient adherence to mechanical prophylaxis—areas for future optimization.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.3 Limitations and future directions\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSingle-center design: Limits generalizability; multi-center RCTs are needed, particularly in lower-resource settings. Non-randomized before-and-after design: Time-related confounders cannot be fully excluded; department-level randomized RCTs would strengthen causal inference. Lack of cost-effectiveness analysis: Future research should quantify economic impacts (e.g., avoided VTE-related hospitalization costs) to support adoption in resource-constrained settings. Future directions: Adapt PDCA for low-resource settings; integrate patient-reported outcomes (home prophylaxis adherence) into the Check phase; explore AI-driven Caprini score prediction in the Plan phase.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e4.4 Feasibility for lower-resource settings\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSimplified adaptations reduce resource requirements while retaining core effectiveness: paper-based tools ($500 vs. $15,000 for EMR modules), monthly MDT meetings (75% time reduction), and free online training—addressing scalability barriers identified in prior studies [9].\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe PDCA cycle significantly improves VTE risk management quality by systematically resolving core barriers. Its integration with digital tools and MDT transforms vague quality improvement into actionable, iterative steps—adapting to Chinese healthcare needs. For widespread promotion: 1. Prioritize digital integration (or simplified alternatives) to reduce staff burden; 2. Tailor PDCA to departmental characteristics (post-operative re-evaluation for surgical departments, dynamic monitoring for medical departments); 3. Adopt 3-6 month cycles with sub-cycles for underperforming indicators. This replicable model narrows the global \"guideline-practice gap\" in VTE prevention, reducing preventable in-hospital deaths—particularly valuable for middle- and low-income regions.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eF.L. and D.X.S. undertook data collection, collation, and drafting of the initial manuscript. S.Z.W. conceptualized the study design, critically reviewed the main body of the manuscript, and secured funding support. All authors contributed to the study design, participated in data analysis discussions, and critically revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe thank the medical staff of the People's Hospital of Ningxia Hui Autonomous Region for data collection and intervention support. We also acknowledge the Department of Health Statistics, School of Public Health, Shandong Second Medical University for statistical consulting.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the People's Hospital of Ningxia Hui Autonomous Region but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the People's Hospital of Ningxia Hui Autonomous Region.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGoldhaber, S. Z. \u0026amp; Tapson, V. F. A prospective registry of 5,451 patients with ultrasound-confirmed deep vein thrombosis. \u003cem\u003eAm. J. 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American Society of Hematology 2021 guidelines for management of venous thromboembolism: prevention of venous thromboembolism in surgical and nonsurgical hospitalized patients. \u003cem\u003eBlood Adv.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e (23), 4877\u0026ndash;4900. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1182/bloodadvances.2021005398\u003c/span\u003e\u003cspan address=\"10.1182/bloodadvances.2021005398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Venous Thromboembolism, PDCA Cycle, Risk Management, Quality Improvement, Digital Integration","lastPublishedDoi":"10.21203/rs.3.rs-8422906/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8422906/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVenous thromboembolism (VTE) is a leading cause of preventable in-hospital mortality globally, with suboptimal adherence to clinical preventive guidelines. This study aimed to develop a systematic VTE risk management intervention based on the Plan-Do-Check-Act (PDCA) cycle, quantify its efficacy in improving management quality, and provide a replicable framework for healthcare institutions. A before-and-after controlled study was conducted in 5 high-VTE-risk departments (Interventional Vascular Surgery, Respiratory Medicine, Gynecology, Geriatrics, and Trauma Orthopedics) of a tertiary general hospital in Ningxia, China, from January 2022 to December 2023. Patients admitted in 2022 (n=8752) served as the control group, receiving routine guideline-based VTE management without structured improvement mechanisms. Patients admitted in 2023 (n=9163) constituted the intervention group, undergoing PDCA-driven interventions (multidisciplinary collaboration [MDT], digital integration, and standardized processes) identified via root cause analysis (fishbone diagram combined with Pareto analysis). Interrupted time-series analysis and multivariate logistic regression (adjusting for age, sex, department, and case-mix index) were used to strengthen causal inference. Baseline characteristics were balanced between groups (all P\u0026gt;0.05). After confounder adjustment, key indicators in the intervention group were significantly improved (all P\u0026lt;0.001): VTE preventive measure implementation rate increased from 23.84% to 67.43% (absolute increase: 43.59%, 95% CI: 41.27%-45.91%; adjusted OR=7.92, 95% CI: 7.21-8.71); hospital-acquired VTE incidence decreased from 4.63% to 2.74% (absolute decrease: 1.89%, 95% CI: 1.26%-2.52%; adjusted OR=0.52, 95% CI: 0.44-0.62); 24-hour VTE risk assessment rate rose from 36.04% to 91.06% (adjusted OR=16.83, 95% CI: 15.12-18.75). No significant differences were observed in major/minor bleeding incidence or VTE mortality between groups (all P\u0026gt;0.05). The PDCA cycle effectively enhances VTE risk management by addressing core barriers (information fragmentation, insufficient personnel cognition, and non-standardized processes). Its integration with digital tools and MDT forms a clinically valuable, replicable model, with feasible adaptations for lower-resource settings, thereby narrowing the global \"guideline-practice gap\" in VTE prevention.\u003c/p\u003e","manuscriptTitle":"Optimizing Venous Thromboembolism Risk Management through the PDCA Cycle: A Single-Center Before-and-After Controlled Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 09:07:33","doi":"10.21203/rs.3.rs-8422906/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-04T14:15:53+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"231325525195760779203526755552687255602","date":"2026-02-04T12:56:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-03T15:09:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195815233303585189187999933558287023299","date":"2026-02-03T10:02:05+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-23T22:08:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134065823390211553724900737108441350574","date":"2026-01-09T14:03:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-08T04:10:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T04:06:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-30T14:44:14+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-26T14:02:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-26T13:53:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"df5d151e-68a3-474e-8549-72a7c4d6ff29","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":60930159,"name":"Health sciences/Diseases"},{"id":60930160,"name":"Health sciences/Health care"},{"id":60930161,"name":"Health sciences/Medical research"},{"id":60930162,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-27T16:02:53+00:00","versionOfRecord":{"articleIdentity":"rs-8422906","link":"https://doi.org/10.1038/s41598-026-44596-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-24 15:58:17","publishedOnDateReadable":"April 24th, 2026"},"versionCreatedAt":"2026-01-13 09:07:33","video":"","vorDoi":"10.1038/s41598-026-44596-0","vorDoiUrl":"https://doi.org/10.1038/s41598-026-44596-0","workflowStages":[]},"version":"v1","identity":"rs-8422906","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8422906","identity":"rs-8422906","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}