Development and Evaluation of a Data Validation System for Healthcare Quality Indicators within China's Tiered Hospital Accreditation Framework

Development and Evaluation of a Data Validation System for Healthcare Quality Indicators within China's Tiered Hospital Accreditation Framework | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 23 September 2025 V1 Latest version Share on Development and Evaluation of a Data Validation System for Healthcare Quality Indicators within China's Tiered Hospital Accreditation Framework Authors : Pang Wenwen , Qin Rong , Luo Ling , Zhang Xue , Gan Ming , and Wang Jinian [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.175860012.23607202/v1 335 views 126 downloads Contents Abstract Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 【Abstract】Background: Data validation is essential for ensuring the authenticity and reliability of healthcare quality monitoring indicators. China’s 2022 Tertiary Hospital Accreditation Standards require internal validation of quality monitoring data. However, hospitals often face challenges such as ambiguous definitions, fragmented data collection, and insufficient IT support, which hinder effective validation. Methods: This study designed and implemented a comprehensive data validation system in a tertiary hospital. The framework included: (1) an organizational structure centered on the Quality Control Office; (2) a dynamic trigger mechanism based on six scenarios; (3) a standardized five-step validation process (data collection, consistency comparison, root cause analysis, corrective action, re-validation) using stratified sampling; and (4) an information platform for automated sampling, comparison, and root cause tracking. Results: A case study on the Admission Rate of Patients with APACHE II Score ≥15 within 24 hours of ICU admission demonstrated system effectiveness. Initial consistency was 69.26%. Root cause analysis and corrective actions (including role clarification, EHR integration, and training) led to 100% consistency upon re-validation, and the indicator improved from 61.19% to 85.94%. Hospital-wide, data consistency increased from 65% to 89%, external reporting pass rates reached 100%, validation efficiency improved by 40%, and manual effort decreased by 30%. Conclusion: The data validation system significantly enhances data reliability, supports accreditation compliance, and facilitates continuous quality improvement. Success depends on integrating institutional protocols, technology, and staff engagement. Future work should focus on adapting to evolving standards, incorporating AI and big data, and strengthening data quality culture. Development and Evaluation of a Data Validation System for Healthcare Quality Indicators within China’s Tiered Hospital Accreditation Framework Pang Wenwen 1,2 Qin Rong 2 Luo Ling 2 Zhang Xue 2 Gan Ming 2 Wang Jinian 1,3 1.Anhui Medical University, Hefei 230000, Anhui Province, China 2.The Third People’s Hospital Of Hefei,Hefei 230000, Anhui Province, China 3.Department of Education, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Corresponding author: Wang Jinian,Department of Education, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China Email: [email protected] Fund Project: Open Project of ”Guoyi Technology” by Institute of Hospital Management, Anhui Medical University (2024gykjyy08) 【Abstract】Background: Data validation is essential for ensuring the authenticity and reliability of healthcare quality monitoring indicators. China’s 2022 Tertiary Hospital Accreditation Standards require internal validation of quality monitoring data. However, hospitals often face challenges such as ambiguous definitions, fragmented data collection, and insufficient IT support, which hinder effective validation. Methods: This study designed and implemented a comprehensive data validation system in a tertiary hospital. The framework included: (1) an organizational structure centered on the Quality Control Office; (2) a dynamic trigger mechanism based on six scenarios; (3) a standardized five-step validation process (data collection, consistency comparison, root cause analysis, corrective action, re-validation) using stratified sampling; and (4) an information platform for automated sampling, comparison, and root cause tracking. Results: A case study on the Admission Rate of Patients with APACHE II Score ≥15 within 24 hours of ICU admission demonstrated system effectiveness. Initial consistency was 69.26%. Root cause analysis and corrective actions (including role clarification, EHR integration, and training) led to 100% consistency upon re-validation, and the indicator improved from 61.19% to 85.94%. Hospital-wide, data consistency increased from 65% to 89%, external reporting pass rates reached 100%, validation efficiency improved by 40%, and manual effort decreased by 30%. Conclusion: The data validation system significantly enhances data reliability, supports accreditation compliance, and facilitates continuous quality improvement. Success depends on integrating institutional protocols, technology, and staff engagement. Future work should focus on adapting to evolving standards, incorporating AI and big data, and strengthening data quality culture. Keywords: Hospital Accreditation; Quality Indicators, Health Care; Data Accuracy; Data Validation; Quality Improvement; Information Systems Highlights: • A structured data validation system enhances healthcare indicator reliability. • System improved data consistency from 65% to 89% in a tertiary hospital. • Combines organizational design, dynamic triggers, and IT for validation. • Supports accreditation compliance and continuous quality improvement culture. 1. Introduction Tiered hospital accreditation serves as a pivotal mechanism for enhancing standardized management and service capabilities within healthcare institutions, promoting high-quality development through a structured evaluation system [1]. The 2022 edition of China’s Tertiary Hospital Accreditation Standards (hereafter ”the Standards”) emphasizes quality monitoring indicators (QMIs) as core assessment components, accounting for over 60% of the total evaluation weight [1, 2]. These indicators encompass key hospital functions, focusing on enhancing service capacity (e.g., case mix complexity measured by DRG-CMI) and strengthening quality and safety measures (e.g., postoperative mortality, unplanned readmission rates within 0-31 days) [1, 3]. Subsequent standards (e.g., the 2025 edition) further expand the scope to include complications, specialized quality control metrics, single-disease quality measures, and key technical application indicators, forming a comprehensive and dynamic evaluation framework [1]. Thus, QMIs function not only as benchmarks for accreditation but also as essential navigational tools for internal continuous quality improvement (CQI). The effectiveness of QMIs is fundamentally dependent on the authenticity and consistency of the underlying data. However, significant challenges persist in healthcare data management [4, 5]: (1) Ambiguous indicator definitions lacking unified operational criteria (e.g., inconsistent classification of postoperative complications); (2) Fragmented data collection methods involving parallel manual entries and disparate information systems; (3) Inadequate IT infrastructure in some settings, leading to high error rates (8%-12%) in manual data entry [2, 3]. Although the Standards require hospitals to establish internal data validation mechanisms, most lack systematic experience and robust frameworks [4, 6]. Consequently, constructing a scientific validation system that addresses accreditation requirements while integrating institutional design, technological tools, and staff training remains a critical practical challenge for healthcare institutions. This study describes the development, implementation, and empirical evaluation of such a data validation system within a tertiary hospital. 2 Construction of the Data Validation System 2.1 Organizational Structure Design A scientific data validation system relies on a clear organizational framework. With the Quality Control Management Office serving as the core coordinating department, a three-tiered data validation collaborative network was established by integrating data collectors and validators. By formulating the Medical Quality Information Data Collection and Validation System, the roles and responsibilities of all parties, along with standardized operational procedures, were systematically defined, achieving closed-loop management from top-level design to implementation. 2.2 Data Validation Timing and Trigger Mechanism Data validation must dynamically respond to both hospital management and accreditation requirements. In accordance with the Standards, the hospital designed six types of trigger scenarios (Table 1), supported by an automated early warning mechanism. Table 1. Data Validation Trigger Scenarios and Response Protocols Trigger Scenario Initiation Condition Response Time Example Case Initial Data Collection Within 3 working days after indicator definition release Within 48 hours Validation for the new indicator ”Completion Rate of Multidisciplinary Discussion Before Level IV Surgery” upon introduction in 2023. External Audit/Review Upon notification As required Full-sample verification of all specialist quality control indicator data prior to provincial review. Collection Tool Change Upon first data generation after system switch Real-time Logic adjustment for ”Number of End-Stage Renal Disease Patients on Hemodialysis” after HIS change; validation identified missing emergency surgery data. Data Extraction Process Change Upon first data generation after process change Real-time For ”Incidence of Venous Thromboembolism in Inpatients”: validation found missing numerator cases due to incomplete discharge diagnoses; process adjusted to dual-path data collection including ultrasound system data. Data Source Change Upon completion of first data cycle using new source Within 7 working days After EMR upgrade, ”Incidence of Catheter-Associated Urinary Tract Infection” data for 2021 and prior showed 0 due to missing template fields. Personnel Change (Collector/Validator) Immediately upon change Within 3 working days Re-validation of ”Colonoscopy Bowel Preparation Excellent/Good Rate” after validator change in endoscopy center; identified need to exclude cases with any bowel segment score=0. 2.3 Standardized Validation Process 2.3.1 Key Control Points in the Data Collection Phase (1) Standardized Definitions: An Indicator Operational Manual was developed to clearly define the scope and collection rules for data. For example, the unplanned readmission rate within 0–31 days after discharge was defined as patients readmitted within 0–31 days according to the electronic medical record system, requiring matching 18-digit ID numbers, identical names, consistency in the first four digits of the principal diagnosis, and exclusion of patients with a principal or other diagnosis of any of Z51.0, Z51.1, Z51.2, or Z51.8. Finally, manual screening was applied to exclude planned readmissions. (2) Tool Standardization: Manual collection uses structured forms, while system-based collection requires pre-validation through scripts. 2.3.2 Sampling Rules and Consistency Calculation Stratified sampling was applied and dynamically adjusted based on total sample size to ensure statistical significance (Table 2). Data collectors gather Data A according to indicator definitions; another group of statistical personnel sample Data B using the same criteria.Data Consistency Rate = 100% - |Verified Result - Collected Result| / Collected Result × 100%. A threshold of ≥90% was set. Results below this threshold trigger root cause analysis. Table 2. Data Validation Sampling Standards Total Population (N) Minimum Validation Sample (n) Applicable Scenario ≤ 16 Full Sample Low-frequency indicators (e.g., low-risk mortality) 17 - 160 16% Routine monitoring indicators 161 - 480 10% Routine monitoring indicators > 480 48 cases High-frequency indicators (e.g., prescription compliance rate) 2.3.3 Root Cause Analysis and Improvement Measures (1) Fishbone Diagram Method: Deviations were analyzed from five dimensions: personnel, equipment, materials, methods, and environment. (2) Closed-Loop Improvement: After revising protocols, optimizing systems, or enhancing training, re-validation was conducted in the next cycle. 2.4 Application of Information-Based Tools To support the above processes, the hospital developed a data validation management platform with the following functions: (1) Automatic Sampling: Random selection of samples from the HIS according to predefined rules; (2) Consistency Comparison: The system automatically calculates consistency rates and generates reports; (3) Root Cause Repository: Accumulates historical issue cases to facilitate rapid diagnosis. 3 Practical Case Study of Data Validation 3.1 Indicator: Admission rate of patients with an Acute Physiology and Chronic Health Evaluation (APACHE Ⅱ) score ≥ 15 (within 24 hours of ICU admission). This is a key indicator reflecting the severity of illness in ICU patients. In 2020, the average admission rate across 3,919 hospitals in China was 49.57%, which did not meet the requirement of exceeding 50% set by the National Critical Care Medicine Center standards. 3.2 Trigger Scenario: Initial data collection and statistics. 3.3 Validation Process: 3.3.1 Raw Data: Based on medical record review, 41 patients in the first quarter of 2024 had completed APACHE Ⅱ scoring with results ≥ 15 (total cases: 67, completion rate: 61.19%). This aligns with findings from studies on the correlation between APACHE Ⅱ scores and nosocomial infections in ICU patients, which indicate that patients with scores exceeding 15 have significantly higher rates of nosocomial infection and mortality. 3.3.2 Independent Validation: A physician from the Department of Critical Care Medicine performed stratified sampling (16%) for review, resulting in an admission rate of 80%. 3.3.3 Consistency Analysis: Consistency Rate = 100% - |Verified Result - Collected Result| / Collected Result × 100%= 100% - |80% - 61.19%| / 61.19% × 100% = 69.26% Conclusion: Consistency rate < 90% threshold. Root cause analysis was initiated. 3.3.4 Closed-Loop Improvement Measures: Using the fishbone diagram tool, root causes were analyzed in depth from the five dimensions personnel, equipment, materials, methods, and environment and closed-loop improvement measures were formulated (Table 3). Table 3. Corrective Actions and Implementation Plan Improvement Action Specific Implementation Verification Method Closure Mechanism Clarify Roles & Collaboration Doctors responsible for scoring; nurses responsible for scoring timing; senior doctors (director/deputy) monthly audit samples. Check scoring form completion during record archiving; feedback audit results in monthly meetings. Monthly meetings to address issues, adjust roles; optimize audit frequency based on feedback. Integrate Scoring into EHR; Device Calibration Collaborate with IT to embed electronic APACHE II score sheet in EMR; calibrate blood gas analyzers for direct PaO₂ data import. Ensure automatic data pull from blood gas analyzer; verify consistency between device output and EHR value. Regular device calibration; re-test data accuracy after system updates. Optimize Process; Enhance Training Discuss and verify scores during next-day handover for new patients; monthly training on scoring method; monthly result review meetings. Review handover meeting records for score verification; post-training knowledge tests; audit scoring accuracy. Monthly analysis of scoring error rates, revise training; incorporate handover verification into routine. Embed in Workflow; System Optimization Include APACHE II score in handover content; EHR supports real-time recording; optimize archiving process. Check handover reports for scores; track scoring completion rate via system logs. Monthly review of handover/archiving data; adjust workflow; verify efficiency post-system upgrade. 3.4 Effectiveness Verification Secondary Trigger Scenario: First data submission after informatization upgrade (Second Quarter of 2023) Raw Data: Admission rate of patients with APACHE Ⅱ score ≥ 15 (within 24 hours of ICU admission): 85.94% (55 cases / 64 cases) Independent Validation: A 16% sampling review confirmed an admission rate of 85.94% Consistency Rate: 100% (|85.94% - 85.94%| = 0) Conclusion: The improvement measures significantly enhanced data consistency and increased the admission rate by 24.75 percentage points. 4. Effectiveness Analysis and Discussion In tiered hospital accreditation, data validation serves as a core process that directly affects the credibility of medical quality management and accreditation outcomes [4-8]. The ”Tertiary Hospital Accreditation Standards (2022 Edition)” are policy- and reform-oriented, establishing an evaluation model that integrates routine monitoring, objective indicators, and on-site inspection. Data indicator assessment accounts for over 60% of the total score, highlighting the strategic importance of data governance. Domestic and international studies indicate that effective data collection is a prerequisite for quality improvement [9-11]. If data contain systematic bias, improvement measures will lack a solid scientific basis, compromising their effectiveness. This reality compels healthcare institutions to establish standardized data validation procedures to ensure end-to-end reliability from data collection to application. 4.1 Implementation Outcomes Systematic development led to improvements in three major areas. First, data quality improved significantly: the hospital-wide data consistency rate increased from 65% to 89%, and the pass rate for data reporting to the provincial platform reached 100%. Second, management efficiency improved: through stratified sampling (e.g., fixed sample size of 48 when population > 480) and automated comparison tools, validation efficiency increased by 40%, and manual effort decreased by 30%. Finally, clinical standardization improved: by developing key-node forms for single diseases within the information system, with designated data entry and review personnel, the accuracy rate of single-disease indicators increased by 50%. 4.2 Core Innovations 4.2.1 Integration of System and Technology The Medical Quality Information Data Collection and Validation System was established to unify data definitions, collection pathways, and validation rules. Taking the unplanned readmission rate as an example, it is defined as readmission within 31 days based on identical 18-digit ID number, identical name, and the first four digits of the principal diagnosis being the same, while excluding patients with any principal or other diagnosis of Z51.0, Z51.1, Z51.2, or Z51.8. Logic checks embedded in the electronic medical record (EMR) system reduced statistical deviation by 30%. Furthermore, using stratified sampling methods and automated comparison tools increased data validation efficiency by 40% while reducing manual effort by 30%. 4.2.2 Dynamic Response Mechanism The six types of trigger scenarios cover key nodes throughout the data lifecycle, enabling real-time validation through automated alerts. Root cause analysis conducted using fishbone diagrams forms a closed loop of problem diagnosis, implementation of measures, and re-validation, demonstrating significant practical results. Efficiency improvement: The root cause repository has accumulated 127 cases, accelerating issue resolution. Quality improvement: For example, the compliance rate of APACHE II score validation increased from 69.26% to 100%, and the patient admission rate rose by 24.75%. This mechanism breaks through the limitations of fixed-cycle validation and achieves a transformation in quality control from a reactive to a proactive approach. 4.3 Challenges in Data Validation Hidden data bias is a major challenge. For instance, flawed EMR template design led to a severe omission rate of 70% for fields like clinical TNM staging assessment rate before cancer treatment. The hospital addressed this by setting mandatory fields and applying natural language processing (NLP) techniques to extract implicit information [12-15], increasing key field completeness to 98%. Dynamic policy response poses another difficulty. For example, with the DRG-CMI (Case Mix Index) indicator, the lack of a grouper prevents data provision; future plans include introducing a system for calculation and validation [16-18]. The application of AI technology also has limitations-current NLP parsing accuracy for unstructured text is only 75%. Future plans involve EMR system upgrades to establish structured text entry, aiming to improve parsing accuracy to over 90%. Practice has proven that data validation is not merely a technical process but also a reflection of the hospital’s quality culture. Through the trinity governance model of system-technology-talent, the hospital has not only enhanced data quality but also fostered a quality control awareness participated in by all staff. This shift from passive compliance with accreditation to actively driving quality improvement provides sustained momentum for the hospital’s high-quality development [19-20]. With the deep integration of medical big data and AI technologies, the data validation system will evolve towards greater intelligence and dynamism, becoming a key engine for enhancing medical service capabilities. 5. Conclusion This study demonstrates that a structured data validation system is fundamental to reliable hospital accreditation and continuous quality improvement. By establishing a clear organizational structure, dynamic triggers, standardized processes, and supportive IT tools, hospitals can significantly enhance data trustworthiness, thereby providing a solid foundation for scientific decision-making. Future directions should include: 1) Enhancing agility to adapt to evolving standards through dynamic indicator definition updates; 2) Deepening technological empowerment by embracing AI and big data for intelligent validation; 3) Cultivating a pervasive data quality culture, explicitly integrating data quality metrics into departmental performance evaluations. Fortifying this foundation of high-quality data is essential for driving substantive, high-quality hospital development. ACKNOWLEDGEMENTS：None. This research did not receive any specific grant from funding agencies in the public, commercial, or not-forprofit sectors. FUNDING: This work was supported by the Open Project of Guoyi Technology by the Institute of Hospital Management, Anhui Medical University (Grant No. 2024gykjyy08). CONFLICT OF INTERST: The authors declare no conflict of interest. AUTHOR CONTRIBUTIONS: Wenwen Pang: Conceptualization, Methodology, Investigation, Data Curation,Writing-Original Draft, Formal analysis. Ling Luo:Investigation, Data Curation,Formal analysis. Xue Zhang:Investigation, Data Curation. Ming Gan:Software,Resources, Data Curation. Tingyu Wen: Investigation, Data Curation.Jinian Wang: Supervision, Project administration, Writing - Review & Editing. ETHICS STATEMENT: Not applicable. DATA AVAILABILITY STATEMENT: Research data are not shared. 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Keywords data accuracy data validation hospital accreditation information systems quality improvement Authors Affiliations Pang Wenwen 1Anhui Medical University View all articles by this author Qin Rong People's Hospital Of Hefei View all articles by this author Luo Ling People's Hospital Of Hefei View all articles by this author Zhang Xue People's Hospital Of Hefei View all articles by this author Gan Ming People's Hospital Of Hefei View all articles by this author Wang Jinian [email protected] 1Anhui Medical University View all articles by this author Metrics & Citations Metrics Article Usage 335 views 126 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Pang Wenwen, Qin Rong, Luo Ling, et al. Development and Evaluation of a Data Validation System for Healthcare Quality Indicators within China's Tiered Hospital Accreditation Framework. Authorea . 23 September 2025. 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