PDCA Cycle-Driven Whole-Chain Optimization of the Pre-Prescription Audit System: A Multidimensional Approach from Pharmacist Competency Development to Dynamic Rule Database Management

PDCA Cycle-Driven Whole-Chain Optimization of the Pre-Prescription Audit System: A Multidimensional Approach from Pharmacist Competency Development to Dynamic Rule Database Management | 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 Research Article PDCA Cycle-Driven Whole-Chain Optimization of the Pre-Prescription Audit System: A Multidimensional Approach from Pharmacist Competency Development to Dynamic Rule Database Management huili zhang, Liang ZHAO, Conglin LIU, Zhenzhen XU, Jinghui TIAN This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8992981/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background This study aimed to enhance the operational efficacy of the pre-prescription review system by applying the PDCA (Plan-Do-Check-Act) cycle management model, with the goal of improving prescription review accuracy, strengthening rational drug use management, and bolstering patient medication safety. Methods Based on the PDCA cycle framework, the fishbone diagram was used to systematically identify potential factors affecting pre-prescription review accuracy across seven dimensions: physicians, pharmacists, competent authorities, systems, drugs, regulations, and interdepartmental collaboration. The 80/20 rule was applied to prioritize key factors, and targeted improvement strategies were formulated using the 5W1H method. Outcomes were quantitatively evaluated. Results Six critical factors influencing review accuracy were pinpointed: delays in registering off-label drug use, insufficient pharmacist competency, untimely analysis of irrational prescriptions, inadequate rule adjustment procedures, unclear rule revision/approval processes, and limited system intelligence. Key interventions included centralized rule database management, core competency development for prescription review pharmacists, accurate documentation of irrational prescriptions at dispensing windows, and optimization of pharmacist workflows. Post-implementation, the proportion of irrational prescriptions identified by window pharmacists dropped from 0.11% to 0.008% (χ² = 249.019, P < 0.005). The system's post-review irrational task rate decreased from 19.30% to 10.61% (χ² = 5668.134, P < 0.005). Physician prescription modification rates rose from 60.36% to 70.52% (χ² = 616.086, P < 0.005), and overall prescription compliance improved from 93.28% to 97.94% (χ² = 5653.660, P < 0.005). Physician satisfaction with the system increased significantly from 9.6% to 40.4% (Z = -2.180, P < 0.05). Conclusion The PDCA cycle management model effectively optimizes the pre-prescription review system. Its core value lies in enhancing alert accuracy and system adaptability through multidimensional analysis and targeted interventions, while promoting system intelligence and informatization. This model offers a practical, replicable framework for improving pharmaceutical service quality and for the continuous, refined optimization of pre-prescription review processes. PDCA cycle Pre-prescription review system Pharmacist capacity building Rule database management Figures Figure 1 Figure 2 Figure 3 1. Introduction With the ongoing deepening of medical reforms and the implementation of the zero-markup drug policy, pre-prescription review has emerged as a critical strategy and research priority for healthcare institutions aiming to enhance the quality and safety of medical services, reduce medical disputes, and improve patient satisfaction [ 1 -4 ]. Currently, medical institutions at all levels nationwide are actively advancing related initiatives [ 5 ]. However, numerous challenges persist in practical implementation: limited pharmacist review capacity and efficiency may disrupt prescription workflows and elicit feedback from physicians and patients; system rules are frequently based solely on drug labeling or developed by technology vendors without incorporating essential pharmaceutical principles, resulting in poor alignment with clinical practice [ 6,7 ]; high false-positive rates (contributing to alert fatigue or overlooking critical alerts) and false-negative rates (posing direct threats to medication safety) remain prevalent [ 8 – 13 ]; inadequate system integration or technical flaws among systems such as hospital information systems (HIS) or laboratory information systems (LIS) may lead to rule execution failures; and inappropriate calibration of review rules may result in missed detections or erroneous approvals. Addressing these issues necessitates systemic solutions grounded in comprehensive strategic thinking. The PDCA cycle, also referred to as the quality loop, is a widely recognized model for continuous quality improvement in management. It consists of four sequential phases: Plan (defining objectives and methods), Do (implementing actions through training and execution), Check (conducting real-time evaluations), and Action (implementing corrective measures and refining processes). Through iterative application of these stages, continuous intervention and improvement of management goals can be achieved [ 1 4 ]. As an effective management methodology, the PDCA cycle has been extensively applied in the field of pharmaceutical service quality management [ 1 5 –1 7 ]. Although prior studies have investigated related topics, key practical aspects—such as sharing the window prescription intervention registration form, verification of updates related to drug manufacturers or packaging alongside knowledge base revisions, and establishment of competency frameworks for dedicated prescription-review pharmacists—remain insufficiently addressed. This paper proposes a PDCA cycle–based management model designed to enhance prescription review accuracy by systematically identifying influencing factors and implementing targeted improvement strategies. A closed-loop improvement pathway encompassing “problem identification – intervention implementation – outcome validation” is thereby established. Practical implementation demonstrates that this model effectively addresses existing challenges in our institution’s prescription review process while offering replicable technical approaches and management frameworks that support the implementation of the Prescription Review Standards for Medical Institutions [ 1 8 ]. It also facilitates the transition from passive monitoring to proactive empowerment within pharmacy services, providing innovative insights for the dynamic development and management of pre-prescription rule libraries across the industry. 1 Materials and Methods 1.1 Outpatient Prescription Pre-review Model Our institution employs a commercial outpatient prescription pre-review system (Sichuan Meikang Pharmaceutical Software Research and Development Co., Ltd.). This system serves as a clinical pharmacy decision support platform, integrated with hospital prescription information systems and underpinned by extensive knowledge databases. It operates by automatically screening prescription content against a predefined rule library. These clinical rules are categorized into four distinct alert tiers based on severity: black, red, orange, and yellow. Upon generation by a physician, each prescription undergoes an initial automated review. Prescriptions that do not trigger any rule alerts, or contain elements outside the system’s assessment scope, proceed directly to the payment stage. Conversely, if the system identifies a discrepancy between the prescription and its rule set, an alert interface is generated. This interface highlights the potential issue, providing the prescribing physician with an opportunity to modify the prescription or, alternatively, to submit it for formal pharmacist review. Subsequently, a pharmacist conducts a manual review, synthesizing information from the system alert, the full prescription details, and any physician notes prior to determining the appropriate action (Fig. 1 ). 1.2 Data Sources and Statistical Methods A dedicated prescription pre-review working group was formed, composed of the director of the pharmacy department, clinical pharmacists, the head of the prescription review center, reviewing pharmacists, dispensing pharmacists, and pharmacy informaticists, to identify factors influencing the accuracy of the pre-review process. For comparative analysis, two sets of prescriptions were included as study subjects: those issued between November 2022 and January 2023 (prior to the implementation of PDCA management) and those issued between February 2025 and April 2025 (following three complete cycles of PDCA management). All statistical analyses were conducted using SPSS software (version 22.0). Continuous variables were compared using independent samples t-tests, while categorical variables were analyzed with the χ² test. A two-sided p-value of less than 0.05 was considered statistically significant. 2 PDCA Cycle Management Model 2.1 Planning Phase (Plan) 2.1.1 Cause Analysis All members of the working group were trained in the PDCA cycle methodology and employed a fishbone diagram to conduct a systematic root cause analysis. Potential factors affecting prescription pre-review accuracy were categorized according to the 5M framework: Man, Machine, Material, Method, and Environment (Fig. 2 ). Each identified factor was then scored for its relative importance using a 5-3-1 scoring system. The factors were ranked based on their aggregate scores, and in accordance with the Pareto principle (80/20 rule), the top 20% constituting the key influencers were selected for further intervention [ 19 ]. This process culminated in the identification of six primary factors:(1) delays in filing off-label use approval requests, primarily due to untimely submissions by prescribing physicians; (2) insufficient competency among reviewing pharmacists; (3) absence of a systematic retrospective analysis mechanism for audited prescriptions; (4) suboptimal processes for adjusting review rules; (5) ambiguous approval procedures for rule revisions; and (6) inadequate intelligence of the supporting information system. 2.1.2 Countermeasure Formulation Building upon the identified root causes, the team employed the 5W1H (Why, What, Where, When, Who, How) methodology to generate a range of potential improvement measures. Each proposed countermeasure was subsequently evaluated by a panel of ten evaluators across three criteria: feasibility, effectiveness, and economic viability, using a 5-3-1 scoring scale (Excellent=5, Acceptable=3, Poor=1). The maximum attainable total score was 150 points. Through structured group discussion, countermeasures achieving a score of 140 or higher were deemed viable and selected for further consideration, yielding an initial set of six high-priority options (Table 1 ). From this set, logical integrations were made to consolidate overlapping strategies. Specifically, countermeasures A1 and D1, both targeting the enhancement of reviewing pharmacists’ competency, were merged. Similarly, items B1 and F1, which pertained to the regulation revision and approval workflow, were integrated into a unified strategy termed "Strengthening the Management of the Rule Database." Furthermore, measures C4 and E1, each emphasizing workflow optimization for reviewing pharmacists, were combined. Consequently, four core implementation strategies were finalized: a) Strengthening the Management of the Rule Database; b) Establishing a Core Competency Framework for Prescription Review Pharmacists; c) Implementing Precise Documentation of Irrational Prescriptions at the Dispensing Window; d) Optimizing the Workflow for Prescription Review Pharmacists. Table 1 Strategies for Improving Audit Accuracy Root Cause Description Countermeasures Total Score Adopted Proposer person in charge A.Delayed off-label use drug filing Clinical drug use misaligns with system rules, causing false-positive alerts. A1.Strengthen communication with the secretary of the clinical filing section and fulfill the main responsibility 142 ☆ Tian** Tian** A2. Provide diversified resource query portals 126 Xu** A3.Streamline the off-label use filing process 124 Gao** B. Inadequate rule base adjustment process Leads to incomplete/untimely rule updates. B1. Strengthen rule base management, clarify adjustment process. 144 ☆ Zhang** Zhang** B2. Establish a sound rule adjustment management system. 136 Cao** B3. Increase regulatory efforts. 108 Chen** C. Insufficient system intelligence May cause rule errors, leading to false positives/negatives. C1. Accurate registration of unreasonable prescriptions at the dispensing window to achieve information sharing 148 ☆ Xu** Xu** C2. strengthening the daily review of rules by reviewing pharmacists 134 Zhang** C3. Improve information system 134 Zhang** C4.Information pharmacists complete timely matching of drugs 122 Tian** C5.Review rules for manufacturer/specification changes or new drugs. 140 Zhang** Zhang** D.Inadequate competence of reviewing pharmacist Pharmacist competency is foundational for review advancement, off-label filing, and rule adjustment. D1. Build core competencies of prescription review pharmacists. 144 ☆ Zhang* Zhang* D2. Strengthen the collaboration and communication between prescribing pharmacists and information pharmacists, clinical pharmacists and engineers. 134 Zhang** D3. Strengthen pharmacist training 122 Xu** E.Failure to sort out unreasonable prescriptions in a timely manner Hinders comprehensive understanding of rule problems. E1. Strengthening the sorting and analysis of system audit results 146 ☆ Xu** Xu**、Zhang** E2. Improve the responsibility of reviewing pharmacists 136 Cao** F. Unclear rule revision approval process Impacts rule revision efficiency. F1. optimize the rule approval process 142 ☆ Cao* Tian** 2.2 Execution Phase (DO) 2.2.1 Management of Rule Library Adjustment 2.2.1.1 Principles for Rule Library Adjustment Rule adjustment refers to the dynamic updating and optimization of drug review rules within the prescription review system, based on current evidence-based medical evidence and prescription evaluation standards. The goal is to establish a systematic, comprehensive, standardized, and clinically aligned prescription review rule repository [ 20 ]. Adjustments should adhere to principles of scientific rigor, regulatory compliance, and precision, ensuring alignment with relevant laws, regulations, and clinical practice requirements. 2.2.1.2 Approval and Adjustment Process for Rule Revision Rule revision is regarded as a routine task by prescription review pharmacists. Identified issues are categorized and addressed through a tiered approach: simple issues are resolved via internal discussion within the prescription review center; complex issues are escalated to the prescription review management team for review; and contentious issues necessitate resolution through interdepartmental collaboration. The rule revision process itself is stratified into three levels based on the nature and impact of the issue: internal rule revisions, hospital-wide rule revisions, and off-label use filings. For routine updates driven by database improvements, system updates, or legal review, the prescription review center completes the internal “Application Form for Prescription Review System Rule Revision” and the “Prescription Review Rule Revision Filing Form.” These are then submitted to designated personnel for implementation and documentation. When prescriptions deviate from approved drug labeling or exceed defined scopes, the responsible clinical department must submit the “Application Form for Prescription Review System Rule Revision” through the hospital’s collaborative office (OA) platform, supported by relevant evidence-based medical literature. The subsequent pathway is determined by the assessed level of safety risk: low-risk revisions may be approved by the management team, medical department, and pharmacy department; high-risk revisions require additional approval from the hospital ethics committee and the pharmacy council. 2.2.1.3 Testing and Optimization of Prescription Review Rules After the prescription review rules are established, a system test is necessary to verify their compliance with the intended operational logic. The primary objectives of this testing phase include: confirming the rules' ability to accurately identify and manage various prescription scenarios, thereby ensuring their effectiveness in clinical practice; promptly identifying potential issues that may affect the user experience for physicians (e.g., redundant alerts), and making appropriate optimizations and adjustments; and uncovering unforeseen risks that may arise during rule implementation, such as rule conflicts, misjudgments, or omissions. Resolving these issues early can significantly enhance the accuracy and reliability of the prescription review system and strengthen physicians’ confidence in its performance. For example, consider the rule that restricts outpatient prescriptions for phenobarbital tablets to a duration not exceeding 14 days: if this rule is simply configured in the excessive duration module without proper testing, the system may erroneously approve prescriptions due to the non-splitting configuration of the drug. This occurs because the calculation of prescription duration is based on drug dosage, and phenobarbital is defined as a non-split drug in the algorithm. Therefore, any prescription for no more than one bottle is considered acceptable. This example illustrates the importance of comprehensively considering factors such as dosage form and algorithm logic during rule configuration to ensure scientific validity and practical feasibility. Additionally, when defining custom rules, it is important to note that basic rules and self-defined rules function independently. To prevent system-generated false alerts, basic rules should be temporarily disabled when creating custom rules. For instance, when the clinical diagnosis includes both anxiety and depression, the use of sodium valproate does not constitute off-label prescribing. Therefore, to ensure the accuracy and effectiveness of the custom rules, the indications "anxiety" and "depression" must be explicitly added to the off-label use module for sodium valproate . Following their establishment, prescription review rules must undergo systematic testing to verify operational logic and clinical utility. The primary objectives of this phase are threefold: (1) to confirm the rules’ accuracy in identifying and managing diverse prescription scenarios, thereby ensuring clinical effectiveness; (2) to promptly identify and optimize potential issues affecting physician user experience, such as excessive or redundant alerts; and (3) to uncover and resolve unforeseen implementation risks, including rule conflicts, misjudgments, or omissions. Early resolution of such issues significantly enhances system accuracy, reliability, and end-user confidence. For instance, consider a rule designed to restrict outpatient prescriptions for phenobarbital tablets to a maximum duration of 14 days. If the rule is configured solely within the excessive-duration module without adequate testing, its intended function may be compromised when the medication is defined as "non-splittable" in the system. Since the system calculates treatment duration based on the total quantity prescribed, and if phenobarbital is set as non-splittable in the algorithm, any prescription for one full bottle or less would be incorrectly judged as appropriate by the system. This example underscores the necessity of comprehensively considering dosage form, algorithm logic, and other contextual factors during rule configuration to ensure scientific validity and practical feasibility. Additionally, when defining custom rules, it is critical to note that basic (default) rules and user-defined rules operate independently. To prevent conflicting alerts, basic rules should be temporarily disabled during the creation and testing of corresponding custom rules. For example, if sodium valproate is prescribed for a diagnosis encompassing both anxiety and depression, its use may not constitute off-label prescribing. To ensure custom rule accuracy, the indications “anxiety” and “depression” must be explicitly added to the off-label use module for sodium valproate, while any conflicting basic rules are suspended. 2.2.2 Development of Core Competencies for Prescription Review Pharmacists The professional competency of prescription review pharmacists critically influences not only the efficiency of the review process but also the accuracy, depth, and sustainable evolution of the rule library. Consequently, establishing a scientific, systematic, and comprehensive core competency indicator system for these pharmacists is essential to enhance the professionalism and standardization of prescription reviews and to foster the continuous improvement of pharmaceutical care quality. The construction of this system integrated literature analysis and the Delphi expert consultation method to identify relevant competency indicators and establish an evaluation framework. The Analytic Hierarchy Process was subsequently employed to calculate the weight of each indicator, resulting in a scientifically robust and rational evaluation system. A comprehensive core competency indicator system for prescription review pharmacists should encompass multiple levels: primary indicators (e.g., Professional Competency, Technical Competency, Professional Development Competency, Pharmaceutical Service Competency), secondary indicators (e.g., Professional Ethos, Clinical Pharmacotherapy Involvement, Foundational Prescription Review Skills, Communication and Collaboration, Custom Rule-Formulation Ability, Research Capability, Learning and Development Capacity), and tertiary indicators (e.g., Drug Knowledge Proficiency, Identification of Pharmacological Issues in Prescriptions, Accurate Interpretation and Communication of Drug Label Information to Physicians, Key Review Aspects for Common Disease Medications). Weight analysis revealed that among the primary indicators, Professional Competency and Pharmaceutical Service Competency form the foundational basis for a pharmacist's competency in their role. Notably, among the secondary indicators, Clinical Pharmacy Service Capacity, Custom Rule-Formulation Ability, and Pharmaceutical Information Service Capacity are particularly significant for enhancing prescription review quality. Specifically, the formulation of custom rules requires pharmacists to master evidence-based pharmacy methods, synthesize the latest clinical guidelines, drug labelling information, and medical literature to scientifically develop and adjust review rules. Concurrently, they must be proficient with the prescription review system's operational procedures, possessing the technical ability to create, modify, and delete rules within the platform, as well as the analytical skills to monitor and interpret system data to promptly identify and resolve issues. Throughout the rule adjustment process, effective communication and collaboration with clinicians, information system engineers, and other relevant stakeholders are imperative to ensure the developed rules are both scientifically sound and practically feasible. Therefore, strong communication skills and a collaborative ethos are indispensable attributes for a competent prescription review pharmacist. To facilitate effective communication during the rule-adjustment process, a comprehensive set of measures has been instituted. These include the clear definition of communication objectives and content, regular meetings of the pre-prescription review panel, implementation of a mechanism for sharing physician contact information within the review interface, and explicit assignment of communication responsibilities to reviewing pharmacists. Furthermore, efforts have been made to cultivate an open and candid communication environment, complemented by established feedback mechanisms and structured training protocols. In their role as key liaisons for interdepartmental communication, reviewing pharmacists are tasked with identifying issues and facilitating the exchange of information. False-negative findings primarily arise from absent system data or incomplete data transmission between the Hospital Information System (HIS) and the Prescription Automatic Screening System (PASS). Addressing these issues necessitates robust communication channels among reviewing pharmacists, dispensing pharmacists, informatics pharmacists, and systems engineers. Conversely, false-positive alerts frequently originate from off-label drug use. Given that updates to drug labelling often lag behind rapidly evolving clinical evidence, off-label medication use remains an ongoing challenge within pre-prescription review [ 21 ]. Depending on the severity of the scenario, required responses may involve formal documentation for off-label use or the submission of an online request for rule modification. This process, which includes the implementation of necessary precautions and citation of relevant evidence-based medical literature, requires reviewing pharmacists to maintain proactive and consistent communication with departmental secretaries to ensure uninterrupted workflow progression. 2.2.3. Accurate Documentation of Inappropriate Prescriptions by Dispensing Pharmacists Prescriptions deemed appropriate during the pre-review stage may still contain errors that fall outside the detection scope of the automated system or exhibit logical inconsistencies. Such cases are classified as false-negative prescriptions. Without thorough analysis by the prescription review pharmacist, these issues are likely to remain undetected. The verification performed by the dispensing pharmacist at the service window thus constitutes a primary method for identifying false-negative outcomes from the pre-review process. Intervention records and feedback provided by the dispensing pharmacist also offer crucial evidence for prescription review pharmacists to optimize review rules. To enhance this workflow, we refined the prescription intervention registration form and implemented a shared Excel spreadsheet accessible via the local area network (LAN) at both outpatient dispensing windows and prescription review pharmacists’ computer terminals. This modification facilitates the standardized management of prescription interventions and improves coordination between prescription review and dispensing pharmacists, thereby increasing team efficiency. Through this registration form, prescription review pharmacists can promptly identify false-negative cases missed during pre-review and update the rule database accordingly. Each documented case includes the date, patient information, prescription details, system review status, identified causes of the problem, and a description of the intervention ( Fig. 3). 2.2.4 Improv ing the Workflow of Prescription Review Pharmacists The workflow for pharmacists conducting prescription review should be comprehensive, integrating not only the core tasks of receiving and evaluating prescriptions, performing prescription audits, managing dispensing-window interventions, and maintaining communication and feedback channels, but also the critical review of rules associated with alterations in drug manufacturers or specifications. Furthermore, systematic analysis of inappropriate prescriptions is essential to generate robust data that informs and supports subsequent rule modifications. 2.2.4.1 Review of Management Protocols for Drug Manufacturer/Specification Changes and Newly Introduced Medications Given the uniqueness of drug identification codes, any modification in drug specifications or manufacturing sources necessitates re-verification of the alignment between the hospital information system and the drug knowledge base of the prescription review platform. This includes confirming data matching, adjusting unit conversions, updating drug attributes, and maintaining package inserts, with thorough documentation of all related rule modifications. In parallel, critical medication instructions should be annotated to support the timely updating of clinical guidance notes in the drug utilization checklist. In parallel with the nationwide deployment of electronic prescription circulation, the hospital's medical insurance formulary for externally dispensed medications is undergoing continuous expansion. For instance, the current dual-channel reimbursement drug list contains 413 distinct agents. For such externally prescribed drugs, the standardization and clinical appropriateness of prescriptions can only be guaranteed through the implementation of scientifically sound, comprehensive, and rationally designed system rules. To ensure the sustained development of this initiative, the hospital has integrated associated responsibilities into its performance evaluation framework. Specifically, for newly introduced drugs, externally dispensed medications, and products with altered specifications or manufacturers, information pharmacists are responsible for maintaining core drug data. Following this, the updated formulary is disseminated to the medication review team, where dedicated clinical review pharmacists subsequently verify the relevant rules and maintain corresponding audit records. 2.2.4.2 Review and Analysis of Inappropriate Prescriptions The outpatient query function within the prescription pre-review system enables the selection and screening of prescriptions categorized as "approved by pharmacist override," "rejected for modification by the prescriber," "dispensed with dual verification," or "passed after automatic system intervention." This functionality facilitates a comprehensive analysis of the types and distribution of prescription-related issues, allowing for the timely implementation of targeted interventions and serving as a vital mechanism for identifying false-positive alerts. Prescriptions "approved by pharmacist override" refer to those initially flagged by the system as potentially inappropriate but subsequently approved by a reviewing pharmacist. This scenario may indicate either inaccuracies in the system's alert logic or an overly restrictive rule set. In contrast, cases resulting in "rejection for modification by the prescriber" or "dispensing with dual verification" typically reflect a clinician's judgment regarding therapeutic necessity or adherence to alternative clinical guidelines. For such instances, the reviewer must consult relevant evidence-based medical literature. If the prescribed regimen is supported by established evidence, it should be documented and archived according to standard clinical communication protocols. If not supported, direct communication with the prescriber is warranted to discuss potential adjustments to the treatment plan. The category of prescriptions "passed after automatic system intervention" represents transactions that occur based on predefined system logic, typically outside of a pharmacist's immediate review window. Proactive monitoring of these prescriptions is essential, as it allows for a more complete identification of issues requiring documentation or reveals other medication-related problems, such as incorrect drug or dose selection. In these cases, timely communication with either the prescriber or the patient is necessary to facilitate correction, followed by appropriate documentation. For prescribers who repeatedly disregard system alerts, raising awareness through institution-wide dissemination of review findings can be an effective strategy. Furthermore, rigorous prescription evaluation by clinical pharmacists serves a dual purpose: it not only constitutes a key metric for evaluating pharmacist performance but also aids in identifying latent deficiencies within the clinical rule base. This process fosters a closed-loop quality management system that integrates pre-prescription review, point-of-dispensation pharmacist intervention, and retrospective prescription analysis. This continuous cycle is fundamental to the ongoing optimization of the rule base and the sustained improvement of overall prescription quality. 2.3 Check Phase 2.3.1 Comparative Analysis of Prescription Intervention Outcomes Following the implementation of PDCA cycle management, a comparative analysis of key performance indicators revealed significant improvements in the prescription review process. The proportion of irrational prescriptions intervened by pharmacists at the point of dispensing decreased markedly from 0.11% to 0.008% (χ² = 249.019, P < 0.005), indicating a substantial reduction in false-negative alerts from the pre-review system. Concurrently, the rate of prescription tasks flagged as irrational post-system-review declined from 19.30% to 10.61% (χ² = 5668.134, P < 0.005). Simultaneously, the physician modification rate increased from 60.36% to 70.52% (χ²=616.086, P <0.005), reflecting an improved willingness to adjust prescriptions in response to system alerts. Collectively, these improvements contributed to a rise in overall prescription compliance from 93.28% to 97.94% (χ² = 5653.660, P < 0.005) (Table 2 ). Table 2 System Audit Intervention Status Time Total Tasks Window Interventions (Rate%) System-Reviewed Irrational Tasks (Rate%) Physician Modifications Post-Review (Rate%) Task pass rate (%) Nov 2022 - Jan 2023 (Pre-PDCA) 118,587 132 (0.11) 22,886 (19.30) 13,815 (60.36) 93.28 Aug 2023 - Oct 2023 (Post PDCA Round 1) 248,223 82 (0.03) 32,633 (13.15) 21,797 (66.79) 96.45 May 2024 - Jul 2024 (Post PDCA Round 2) 279,458 35 (0.01) 30,766 (11.01) 21,757 (70.72) 97.25 Feb 2025 - Apr 2025 (Post PDCA Round 3) 302,514 23 (0.008) 32,092 (10.61) 22,630 (70.52) 97.94 Note: due to the impact of the COVID-19 pandemic between November 2022 and January 2023, the total number of prescriptions was relatively low. 2.3.2 Improvement in Physician Satisfaction Physician satisfaction, which reflects their approval of the pre-prescription review system, was assessed in this study. Using a five-point Likert-scale questionnaire, data were collected from 52 outpatient clinics to evaluate physicians' perceptions of the system[ 22 ] . The survey items addressed the prescribing pharmacists' professional knowledge and communication skills, as well as the consistency of the system’s warning messages with clinical medication use. Satisfaction specifically regarding this consistency is presented in Table 3. Overall satisfaction (combining 'very satisfied' and 'satisfied' responses) was higher following the implementation of PDCA management (40.4%) compared to the pre-implementation period (9.6%). A rank-sum test indicated that this difference in overall physician satisfaction with the warning messages before and after PDCA management was statistically significant (Z = -2.180, P < 0.05). Table 3 Comparison of Satisfaction Survey Results for Warning Messages in the Prescription Pre-Audit System [n(%)] Time Very satisfied Satisfied Satisfied Fairly Dissatisfied Very Dissatisfied Pre-PDCA 0（0.0） 5(9.6) 30(57.7) 15(28.8) 2(3.9) Post- PDCA 0（0.0） 21(40.4) 24(46.1） 7（13.5） 0（0.0） 3 Discussion 3.1 Summary of Experiences and Lessons Learned Through practical exploration, this study has delineated core strategies for enhancing the performance of a prescription pre-audit system. Preliminary research serves to anchor the adjustment of system rules in clinical needs, while continuous monitoring and data analysis provide dynamic evidence for performance optimization. Rule adjustments are grounded in evidence-based medicine grading to ensure scientific rigor. Furthermore, iterative rule updates, coupled with multidisciplinary communication, are indispensable for maintaining the system’s adaptability to evolving clinical demands. However, the study also identifies areas requiring improvement in current practice: the professional capacity of reviewing pharmacists in optimizing rules remains inadequate, and feedback mechanisms within departments often lack closure, potentially compromising the system’s responsiveness to complex clinical scenarios. Two critical practices warrant particular emphasis: (a) Standardization of Intervention Documentation at Dispensing Windows. Optimizing the intervention process has significantly increased the documentation rate by dispensing pharmacists. This practice not only furnishes real-world data for rule optimization by reviewing pharmacists but also establishes a traceability mechanism for prescriptions flagged by the system’s automatic intervention. Crucially, detailed documentation of "unreasonable or erroneous prescriptions enforced by doctors when a reviewing pharmacist was unavailable" enables reviewing pharmacists to fully comprehend interventions made at the dispensing stage. If no window-level intervention was recorded, the reviewing pharmacist can proactively communicate with the physician and patient using this documented information, thereby forming a vital "secondary line of defense" for medication safety. (b) Closed-loop Management of the Rule Base. As the system's core, the rule base requires dynamic optimization throughout a "pre-event, during-event, post-event" cycle. Pre-event management necessitates strengthening the baseline rule repository based on enhanced competency building for prescribing pharmacists. During an event, it is critical to accurately document system failures to identify irrational prescriptions, thereby clarifying rule gaps. Post-event, rule iteration should be driven by prescription review and data analysis. This closed-loop model essentially implements "full life-cycle management" of the rule base, representing a sustainable logic for ensuring the long-term accuracy of the system audit. 3.2 Role Reconstruction under Dynamic Rule Base Updates: Opportunities and Challenges Amidst ongoing healthcare reforms and the evolution of pharmacy service models [ 23-27 ], the Computerized Prescription Pre-review System has emerged as a pivotal platform for enhancing the quality of pharmaceutical care [ 28, 29 ]. This study, aligning with a growing body of domestic research, employs the PDCA cycle to establish a systematic framework for managing the rule base. The core of this approach involves exploring integrative pathways that synchronize system development with pharmacy services, achieved by reinforcing both "the centrality of the rule base" and "the leading role and responsibility of the reviewing pharmacist." This dynamic context presents distinct opportunities for pharmacist role evolution. Reviewing pharmacists are positioned to actively engage in the standardization and refinement of the rule base, embedding clinical pharmacy expertise into its operational logic—for instance, by optimizing alert thresholds based on actual prescribing patterns [ 30 ]. Furthermore, they can enhance interdisciplinary communication with systems engineers to facilitate the effective translation of pharmaceutical needs into technical specifications. The creative utilization of system-generated data, such as conducting targeted educational interventions based on patterns of frequent prescription irregularities, enables pharmacists to improve prescriber acceptance rates and advance rational medication use. Concurrently, the imperative for dynamic rule base updating poses a multifaceted challenge to pharmacist competencies. The rapid iteration of medical knowledge necessitates that both reviewing and informatics pharmacists establish robust, rapid-response mechanisms. The entire workflow—from distilling evidence from current guidelines and justifying rule modifications, to building clinical consensus and validating post-implementation outcomes—must become routine, standardized practice. This process represents a comprehensive assessment of pharmacists' capabilities in evidence-based practice, interdisciplinary collaboration, and systems thinking. Successfully navigating this challenge is essential for transforming pharmacy services from a primarily reactive "prescription review" function toward a more proactive model of "clinical empowerment." 4 Conclusion This study implemented a pharmacist competency-based PDCA cycle system, which was structured around dynamic management of a rule database and driven by multi-source feedback. The intervention yielded significant outcomes: a pronounced reduction in the proportion of irrational prescriptions intercepted at the dispensing window, alongside substantial improvements in both physician prescription modification rates and satisfaction scores. The system proved effective in enhancing the accuracy and efficiency of prescription review and in optimizing the clinical prescription management workflow. Nevertheless, a specific challenge persists, primarily concerning prescriptions for medications (excluding antimicrobials) with dosages below those recommended in the official drug labeling. Future work will concentrate on this issue by continuously refining the rule library and review logic through iterative PDCA cycles, thereby improving the comprehensiveness of the review system. It is critical to emphasize that the PDCA cycle is not a finite process concluding after a set number of iterations, but rather a continuous, ongoing quality improvement mechanism. While each cycle resolves a subset of false-positive or false-negative alerts, the dynamic nature of clinical practice—including the introduction of new medications, updates to drug monographs, and revisions to clinical guidelines—inevitably generates new alerts of a similar nature. Therefore, sustaining these continuous improvement cycles is essential. Through this iterative and enduring process, the intelligent prescription review system achieves dynamic adaptation and long-term optimization, ultimately providing stronger support for promoting rational clinical medication use. Declarations Acknowledgments The team would like to thank the staff of the Pharmacy Department and all the physicians who supported this study. Authors' contributions J.H. contributed to conceptualization, H.L. drafted the manuscript, L. supervised the study, C.L.performed formal analysis, and Z.Z.prepared the figures. All authors have read and approved the final manuscript. Funding This study was supported by the Key Research, Development and Promotion Special Project of Xuchang City Science and Technology Bureau (No. 2025039). Data availability All data generated or analysed during this study are included in this published article. Ethics approval and consent to participate Although this study did not directly involve human participants, it was conducted in accordance with the ethical principles of the Declaration of Helsinki, ensuring patient safety and the integrity of healthcare practices. This study was approved by the Ethics Committee of Xuchang Central Hospital (2025-05-014).The need for written informed consent was waived by the Ethics Committee of Xuchang Central Hospital due to the retrospective nature of the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Xie SS, Liu S, Wan JJ, et al. Practice and effect of pharmacists conducting prospective prescription review in outpatient and emergency settings with the aid of rational drug use software. China Pharm. 2021;32(7):876–80. Fan X, Chen D, Bao S, Dong X, Fang F, Bai R, Zhang Y, Zhang X, Tang W, Ma Y, et al. Prospective prescription review system correlated with more rational PPI medication use, better clinical outcomes and reduced PPI costs: experience from a retrospective cohort study. BMC Health Serv Res. 2023;23(1):1014. Zhou L, Gu X, Tan FL, et al. Establishment, maintenance and application effect analysis of the prescription pre-review system in a tertiary hospital in China. BMC Health Serv Res. 2025;25:853. 10.1186/s12913-025-12901-8 . Postema PG. Editorial commentary: choosing wisely: implications of drug prescription, drug safety assessment and tools for improvement. Trends Cardiovasc Med. 2022;32(1):50–1. Zhang QH, Jin R, Wang K, et al. Practice and discussion of an evidence-based pharmacy-based prospective prescription review system in outpatient pharmacy. Chin J Hosp Pharm. 2020;40(4):443–7. Wu GL, Lu XL, Wang H. Application and effectiveness of SWOT analysis in the construction of a prospective prescription review rule database in a children's specialty hospital outpatient department. Cent South Pharm. 2023;21(12):3345–9. Spigelmyer A, Howard C, Rybakov I, Burwell S, Slain D. Impact of clinical phar macist discharge prescription review on the appropriateness of antibiotic therapy: a retrospective comparison. Int J Clin Pharm. 2023;45(3):769–73. Li J, Song HZ, Zhou X, et al. Implementing refined management of β-lactamase inhibitor combinations using a prospective prescription review system. Herald Med. 2020;39(9):1230–3. Wu MF, Shi WZ, Zhao ZG. Comprehensive evaluation of commonly used rational drug use software in China. Chin J Hosp Pharm. 2019;39(10):991–5. Wang L, Ma Y, Xu P, et al. Competency cultivation and case analysis for prescription review pharmacists in prospective prescription review. China Pharm. 2022;33(15):1893–7. Li WR, Li D, Zhao CJ, et al. Analysis of the current status of prospective prescription review in Chinese medical institutions. China Pharm. 2021;32(5):524–9. Zhang HL, Tian JH, Cao K, et al. Application practice of an information-intelligentized prospective prescription review system in the outpatient department of a hospital.Chin. J Ration Drug Use Explor. 2023;20(10):107–12. Xie LB, Que FC, He Y. Improvement and practice of prescription review competence of review pharmacists in our hospital. J Clin Ration Drug Use. 2022;15(20):160–3. Zhang JJ, Wang FL, Meng XY, et al. Analysis of the effect of PDCA cycle on the perioperative prophylactic use of antibiotics in laparoscopic cholecystectomy. China Pharm. 2023;34(13):1632–6. Zhou CK, Ding SM, Zhang R, et al. Construction of a new rational drug use management model based on PDCA cycle and drug therapy pathways. Chin J Hosp Pharm. 2021;41(1):98–102. Wang DD, Xie XH, Huang SW, et al. PDCA cycle assists in the establishment of refined rules for dosage and administration in prospective prescription review. Clin Ration Drug Use. 2024;17(4):159–63. Liu L, Tao S, Xu J, et al. Using PDCA cycle to reduce patient waiting time in outpatient pharmacy. Chin J Mod Appl Pharm. 2020;37(22):2797–802. Professional Committee on Pharmacy Affairs of Chinese Hospital Association. Compiling Group of Specifications for Pharmaceutical Care in Medical Institutions. Specifications for pharmaceutical care in medical institutions. Herald Med. 2019;38(12):1535–56. Wei DJ, Wang FY, Li AZ, et al. Effectiveness of using PDCA to prevent and control multidrug-resistant organism infections in inpatients. Chin J Infect Control. 2023;22(4):478–83. Wu D, Xu Y, Zhang XH, et al. Construction of pediatric prescription and order knowledge base and operation practice of prospective prescription review system in our hospital. Chin Pharm J. 2020;55(12):1046–50. Huang W, Jin Y, Zhao YC, et al. Application practice of improving rational drug use intervention system based on PDCA cycle. J Clin Ration Drug Use. 2023;16(20):140–4. Zhang QH, Jin R, Wang K, et al. Investigation and Analysis of Outpatient Doctors Satisfaction Survey to Pre-prescription Review System in a Third Grade Class-A Hospital in Beijing. Herald Med. 2020;39(9):1206–10. Ministry of Health of the PRC. Notice of the Ministry of Health on print ing and distributing the Management Standard of Hospital Prescription Comment(Trial). 2010. [Accessed 20 Jan. 2026]. http://www.nhc.gov.cn/yzygj National Health Commission of the PRC. Notice on the Issuance of prescrip tion review Standards for Medical Institutions. 2018. [Accessed 20 Jan. 2026]. http://www.nhc.gov.cn/yzygj National Health Commission of the People’s Republic of China.Circular on the Action for Compre. hensive Improvement of Healthcare Quality(2023–2025), [Accessed 20 Jan. 2026]. http://www.nhc.gov.cn/yzygj/s3585/202305/ cfe6b26bce624b9f894cef021a363f3e.shtml. National Health Commission of the People’s Republic of China.Circular of the General Office of the National Health and Wellness Commission on Further Strengthening the Management of Medical Quality(Safety.) Adverse Events,2024, http://www.nhc.gov.cn/yzygj/s7657/202407/14ba157e62774b57876d0ac950bac988.shtml Ni J, Tang X, Chen LM. Overdose Data Analysis:A Review of Medication Error Reports in the FDA Adverse Event Reporting System(FAERS).BMC Pharmacol Toxicol. 2023;24(1):41. Xie H, Zhang H, Peng J, Li L, Geng Y, Ge WP. Prescription Review System Promotes Safe Use of Analgesics,Improves Clinical Outcomes,and Saves Medical Costs in Surgical Patients:In sights From Nanjing Drum Tower Hospital. Adv Ther. 2022;39(1):441–54. Zhou L, Zhao FY, Li YH, et al. Scientific Management for Healthcare Quality Improvement:The Practice of Six Sigma DMAIC in Prescription Pre-Review Work. J Eval Clin Pract. 2025;31:e70187. .https://doi.org/10.1111/jep.70187 . Zhang HL, Tian JH, Xu ZZ, et al. Application practice of refined setting for duplicate use of Chinese patent medicines in prospective outpatient prescription review. Chin J Hosp Pharm. 2024;44(11):113–8. Additional Declarations No competing interests reported. Supplementary Files questionnaire.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 17 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviewers invited by journal 15 Mar, 2026 Editor assigned by journal 15 Mar, 2026 Editor invited by journal 10 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 09 Mar, 2026 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8992981","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606538254,"identity":"19719909-68de-43de-a7ae-c18d61d3fb99","order_by":0,"name":"huili zhang","email":"","orcid":"","institution":"Xuchang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"huili","middleName":"","lastName":"zhang","suffix":""},{"id":606538255,"identity":"c00fbd60-8be4-49b0-97a5-7575ca97bb4e","order_by":1,"name":"Liang ZHAO","email":"","orcid":"","institution":"Xuchang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liang","middleName":"","lastName":"ZHAO","suffix":""},{"id":606538256,"identity":"039a36c0-cec6-4a7f-98fb-a00e08806246","order_by":2,"name":"Conglin LIU","email":"","orcid":"","institution":"Xuchang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Conglin","middleName":"","lastName":"LIU","suffix":""},{"id":606538257,"identity":"5370192d-bc4d-4841-b14c-44fe9a67fb4a","order_by":3,"name":"Zhenzhen XU","email":"","orcid":"","institution":"Xuchang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenzhen","middleName":"","lastName":"XU","suffix":""},{"id":606538258,"identity":"fcc64fb2-e2b9-4c43-807f-41fe4a78ab8b","order_by":4,"name":"Jinghui TIAN","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYDACZiCWMGBgMGBgbHz8o0JCTp6QDh4kLc3GDGcsjA0bCGmBMYC62IQZ2yoSGQ4Q0GLPzvzsgUXBHbvtEsltzIXzJBIYG5gfPrqB12Fs5gYSBs+Sd85IbHs8c5tEHjsDm7FxDn6/mElIGBxONriR2G7Au02imLGBh00avxb2bzAtbRK8cyQSGw4Q1MIDtsUOpEWat4EYLYd5ykBaEgzOPGw2nHFMwtiwmYBf2PuPb5OW+HPY3uB4+sMHH2rq5OTZmx8+xqcFBJglGBgSGxBcAspBgPEDMH6IUDcKRsEoGAUjFQAAv8BG3nsmz/sAAAAASUVORK5CYII=","orcid":"","institution":"Xuchang Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jinghui","middleName":"","lastName":"TIAN","suffix":""}],"badges":[],"createdAt":"2026-02-28 07:56:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8992981/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8992981/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104879034,"identity":"b8d8ad7a-807c-4712-9da5-b2d6cc078f6f","added_by":"auto","created_at":"2026-03-18 08:59:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":63860,"visible":true,"origin":"","legend":"\u003cp\u003eWorkflow of outpatient Pre-prescription review system\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8992981/v1/78cc3da9cbd47862cd79dc2a.png"},{"id":104879038,"identity":"e86a9590-d65b-4af6-be80-9a4699263bd2","added_by":"auto","created_at":"2026-03-18 08:59:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95492,"visible":true,"origin":"","legend":"\u003cp\u003eFishbone diagram of reasons affecting audit accuracy\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8992981/v1/dd2ee7e41842fadbe7f7f59a.png"},{"id":104879037,"identity":"a5a18435-d02f-4fcd-9efa-2a0504aea1d0","added_by":"auto","created_at":"2026-03-18 08:59:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":134265,"visible":true,"origin":"","legend":"\u003cp\u003eUnreasonable prescription registration form at the dispensing window\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8992981/v1/6c61ae8a29e5071abeee5e2e.png"},{"id":105033799,"identity":"900a8fb5-f24e-41db-af3c-3c3a0b90c207","added_by":"auto","created_at":"2026-03-20 07:21:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1125467,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8992981/v1/84e84a5b-e6df-4a72-bb72-a0c10a3d1146.pdf"},{"id":104879035,"identity":"831e1dd8-7101-4156-8d32-d9176bfd60c4","added_by":"auto","created_at":"2026-03-18 08:59:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":61617,"visible":true,"origin":"","legend":"","description":"","filename":"questionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8992981/v1/4b9fa1da726b129b86b2269f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"PDCA Cycle-Driven Whole-Chain Optimization of the Pre-Prescription Audit System: A Multidimensional Approach from Pharmacist Competency Development to Dynamic Rule Database Management","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWith the ongoing deepening of medical reforms and the implementation of the zero-markup drug policy, pre-prescription review has emerged as a critical strategy and research priority for healthcare institutions aiming to enhance the quality and safety of medical services, reduce medical disputes, and improve patient satisfaction [\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e-4\u003c/strong\u003e]. Currently, medical institutions at all levels nationwide are actively advancing related initiatives [\u003cstrong\u003e5\u003c/strong\u003e]. However, numerous challenges persist in practical implementation: limited pharmacist review capacity and efficiency may disrupt prescription workflows and elicit feedback from physicians and patients; system rules are frequently based solely on drug labeling or developed by technology vendors without incorporating essential pharmaceutical principles, resulting in poor alignment with clinical practice [\u003cstrong\u003e6,7\u003c/strong\u003e]; high false-positive rates (contributing to alert fatigue or overlooking critical alerts) and false-negative rates (posing direct threats to medication safety) remain prevalent [\u003cstrong\u003e8\u003c/strong\u003e\u003cstrong\u003e–\u003c/strong\u003e\u003cstrong\u003e13\u003c/strong\u003e]; inadequate system integration or technical flaws among systems such as hospital information systems (HIS) or laboratory information systems (LIS) may lead to rule execution failures; and inappropriate calibration of review rules may result in missed detections or erroneous approvals. Addressing these issues necessitates systemic solutions grounded in comprehensive strategic thinking.\u003c/p\u003e\n\u003cp\u003eThe PDCA cycle, also referred to as the quality loop, is a widely recognized model for continuous quality improvement in management. It consists of four sequential phases: Plan (defining objectives and methods), Do (implementing actions through training and execution), Check (conducting real-time evaluations), and Action (implementing corrective measures and refining processes). Through iterative application of these stages, continuous intervention and improvement of management goals can be achieved [\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e]. As an effective management methodology, the PDCA cycle has been extensively applied in the field of pharmaceutical service quality management [\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e–1\u003c/strong\u003e\u003cstrong\u003e7\u003c/strong\u003e]. Although prior studies have investigated related topics, key practical aspects—such as sharing the window prescription intervention registration form, verification of updates related to drug manufacturers or packaging alongside knowledge base revisions, and establishment of competency frameworks for dedicated prescription-review pharmacists—remain insufficiently addressed.\u003c/p\u003e\n\u003cp\u003eThis paper proposes a PDCA cycle–based management model designed to enhance prescription review accuracy by systematically identifying influencing factors and implementing targeted improvement strategies. A closed-loop improvement pathway encompassing “problem identification – intervention implementation – outcome validation” is thereby established. Practical implementation demonstrates that this model effectively addresses existing challenges in our institution’s prescription review process while offering replicable technical approaches and management frameworks that support the implementation of the Prescription Review Standards for Medical Institutions [\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e8\u003c/strong\u003e]. It also facilitates the transition from passive monitoring to proactive empowerment within pharmacy services, providing innovative insights for the dynamic development and management of pre-prescription rule libraries across the industry.\u003c/p\u003e"},{"header":"1 Materials and Methods ","content":"\u003cp\u003e\u003cstrong\u003e1.1 Outpatient Prescription Pre-review Model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur institution employs a commercial outpatient prescription pre-review system (Sichuan Meikang Pharmaceutical Software Research and Development Co., Ltd.). This system serves as a clinical pharmacy decision support platform, integrated with hospital prescription information systems and underpinned by extensive knowledge databases. It operates by automatically screening prescription content against a predefined rule library. These clinical rules are categorized into four distinct alert tiers based on severity: black, red, orange, and yellow.\u003c/p\u003e\n\u003cp\u003eUpon generation by a physician, each prescription undergoes an initial automated review. Prescriptions that do not trigger any rule alerts, or contain elements outside the system’s assessment scope, proceed directly to the payment stage. Conversely, if the system identifies a discrepancy between the prescription and its rule set, an alert interface is generated. This interface highlights the potential issue, providing the prescribing physician with an opportunity to modify the prescription or, alternatively, to submit it for formal pharmacist review. Subsequently, a pharmacist conducts a manual review, synthesizing information from the system alert, the full prescription details, and any physician notes prior to determining the appropriate action (Fig. \u003cstrong\u003e1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Data Sources and Statistical Methods\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA dedicated prescription pre-review working group was formed, composed of the director of the pharmacy department, clinical pharmacists, the head of the prescription review center, reviewing pharmacists, dispensing pharmacists, and pharmacy informaticists, to identify factors influencing the accuracy of the pre-review process. For comparative analysis, two sets of prescriptions were included as study subjects: those issued between November 2022 and January 2023 (prior to the implementation of PDCA management) and those issued between February 2025 and April 2025 (following three complete cycles of PDCA management). All statistical analyses were conducted using SPSS software (version 22.0). Continuous variables were compared using independent samples t-tests, while categorical variables were analyzed with the χ² test. A two-sided p-value of less than 0.05 was considered statistically significant.\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"2 PDCA Cycle Management Model ","content":"\u003cp\u003e\u003cstrong\u003e2.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ePlanning Phase (Plan)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.1.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCause Analysis\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\u003cp\u003eAll members of the working group were trained in the PDCA cycle methodology and employed a fishbone diagram \u0026nbsp;to conduct a systematic root cause analysis. Potential factors affecting prescription pre-review accuracy were categorized according to the 5M framework: Man, Machine, Material, Method, and Environment (Fig. \u003cstrong\u003e2\u003c/strong\u003e). Each identified factor was then scored for its relative importance using a 5-3-1 scoring system. The factors were ranked based on their aggregate scores, and in accordance with the Pareto principle (80/20 rule), the top 20% constituting the key influencers were selected for further intervention [\u003cstrong\u003e19\u003c/strong\u003e]. This process culminated in the identification of six \u0026nbsp;primary factors:(1) delays in filing off-label use approval requests, primarily due to untimely submissions by prescribing physicians; (2) insufficient competency among reviewing pharmacists; (3) absence of a systematic retrospective analysis mechanism for audited prescriptions; (4) suboptimal processes for adjusting review rules; (5) ambiguous approval procedures for rule revisions; and (6) inadequate intelligence of the supporting information system.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.1.2 Countermeasure Formulation \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eBuilding upon the identified root causes, the team employed the 5W1H (Why, What, Where, When, Who, How) methodology to generate a range of potential improvement measures. Each proposed countermeasure was subsequently evaluated by a panel of ten evaluators across three criteria: feasibility, effectiveness, and economic viability, using a 5-3-1 scoring scale (Excellent=5, Acceptable=3, Poor=1). The maximum attainable total score was 150 points. Through structured group discussion, countermeasures achieving a score of 140 or higher were deemed viable and selected for further consideration, yielding an initial set of six high-priority options (Table\u003cstrong\u003e\u0026nbsp;1\u003c/strong\u003e). From this set, logical integrations were made to consolidate overlapping strategies. Specifically, countermeasures A1 and D1, both targeting the enhancement of reviewing pharmacists’ competency, were merged. Similarly, items B1 and F1, which pertained to the regulation revision and approval workflow, were integrated into a unified strategy termed \"Strengthening the Management of the Rule Database.\" Furthermore, measures C4 and E1, each emphasizing workflow optimization for reviewing pharmacists, were combined. Consequently, four core implementation strategies were finalized: a) Strengthening the Management of the Rule Database; b) Establishing a Core Competency Framework for Prescription Review Pharmacists; c) Implementing Precise Documentation of Irrational Prescriptions \u0026nbsp;at the Dispensing Window; d) Optimizing the Workflow for Prescription Review Pharmacists.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eStrategies for Improving Audit Accuracy\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eRoot Cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eCountermeasures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003eTotal Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eAdopted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eProposer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eperson in charge\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eA.Delayed off-label use drug filing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 120px;\"\u003e\n \u003cp\u003eClinical drug use misaligns with system rules, causing false-positive alerts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eA1.Strengthen communication with the secretary of the clinical filing section and fulfill the main responsibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e☆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eTian**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eTian**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eA2. Provide diversified resource query portals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eXu**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eA3.Streamline the off-label use filing process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eGao**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 107px;\"\u003e\n \u003cp\u003eB. Inadequate rule base adjustment process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 120px;\"\u003e\n \u003cp\u003eLeads to incomplete/untimely rule updates.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eB1.\u0026nbsp;Strengthen rule base management, clarify adjustment process.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e☆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eB2.\u0026nbsp;Establish a sound rule adjustment management system.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eCao**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eB3.\u0026nbsp;Increase regulatory efforts.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eChen**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 107px;\"\u003e\n \u003cp\u003eC. Insufficient system intelligence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMay cause rule errors, leading to false positives/negatives.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eC1. Accurate registration of unreasonable prescriptions at the dispensing window to achieve information sharing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e☆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eXu**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eXu**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eC2. strengthening the daily review of rules by reviewing pharmacists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eC3. Improve information system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eC4.Information pharmacists complete timely matching of drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eTian**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eC5.Review rules for manufacturer/specification changes or new drugs.\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eD.Inadequate competence of \u0026nbsp;reviewing pharmacist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003ePharmacist competency is foundational for review advancement, off-label filing, and rule adjustment.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eD1. Build core competencies of prescription review pharmacists.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e☆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eD2. Strengthen the collaboration and communication between prescribing pharmacists and information pharmacists, clinical pharmacists and engineers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eZhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eD3. Strengthen pharmacist training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eXu**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eE.Failure to sort out unreasonable prescriptions in a timely manner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eHinders comprehensive understanding of rule problems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eE1. Strengthening the sorting and analysis of system audit results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e☆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eXu**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eXu**、Zhang**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eE2. Improve the responsibility of reviewing pharmacists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eCao**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 107px;\"\u003e\n \u003cp\u003eF. Unclear rule revision approval process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003eImpacts rule revision efficiency.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 148px;\"\u003e\n \u003cp\u003eF1. optimize the rule approval process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 38px;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e☆\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eCao*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 59px;\"\u003e\n \u003cp\u003eTian**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003e\u003cstrong\u003e2.2 Execution Phase (DO) \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.1 Management of Rule Library Adjustment \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.1.1\u0026nbsp;\u003c/strong\u003ePrinciples for Rule Library Adjustment\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eRule adjustment refers to the dynamic updating and optimization of drug review rules within the prescription review system, based on current evidence-based medical evidence and prescription evaluation standards. The goal is to establish a systematic, comprehensive, standardized, and clinically aligned prescription review rule repository [\u003cstrong\u003e20\u003c/strong\u003e]. Adjustments should adhere to principles of scientific rigor, regulatory compliance, and precision, ensuring alignment with relevant laws, regulations, and clinical practice requirements.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.1.2 Approval and Adjustment Process for Rule Revision \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eRule revision is regarded as a routine task by prescription review pharmacists. Identified issues are categorized and addressed through a tiered approach: simple issues are resolved via internal discussion within the prescription review center; complex issues are escalated to the prescription review management team for review; and contentious issues necessitate resolution through interdepartmental collaboration. The rule revision process itself is stratified into three levels based on the nature and impact of the issue: internal rule revisions, hospital-wide rule revisions, and off-label use filings. For routine updates driven by database improvements, system updates, or legal review, the prescription review center completes the internal “Application Form for Prescription Review System Rule Revision” and the “Prescription Review Rule Revision Filing Form.” These are then submitted to designated personnel for implementation and documentation. When prescriptions deviate from approved drug labeling or exceed defined scopes, the responsible clinical department must submit the “Application Form for Prescription Review System Rule Revision” through the hospital’s collaborative office (OA) platform, supported by relevant evidence-based medical literature. The subsequent pathway is determined by the assessed level of safety risk: low-risk revisions may be approved by the management team, medical department, and pharmacy department; high-risk revisions require additional approval from the hospital ethics committee and the pharmacy council.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.1.3 Testing and Optimization of Prescription Review Rules\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAfter the prescription review rules are established, a system test is necessary to verify their compliance with the intended operational logic. The primary objectives of this testing phase include: confirming the rules' ability to accurately identify and manage various prescription scenarios, thereby ensuring their effectiveness in clinical practice; promptly identifying potential issues that may affect the user experience for physicians (e.g., redundant alerts), and making appropriate optimizations and adjustments; and uncovering unforeseen risks that may arise during rule implementation, such as rule conflicts, misjudgments, or omissions. Resolving these issues early can significantly enhance the accuracy and reliability of the prescription review system and strengthen physicians’ confidence in its performance. For example, consider the rule that restricts outpatient prescriptions for phenobarbital tablets to a duration not exceeding 14 days: if this rule is simply configured in the excessive duration module without proper testing, the system may erroneously approve prescriptions due to the non-splitting configuration of the drug. This occurs because the calculation of prescription duration is based on drug dosage, and phenobarbital is defined as a non-split drug in the algorithm. Therefore, any prescription for no more than one bottle is considered acceptable. This example illustrates the importance of comprehensively considering factors such as dosage form and algorithm logic during rule configuration to ensure scientific validity and practical feasibility.\u003c/p\u003e\u003cp\u003eAdditionally, when defining custom rules, it is important to note that basic rules and self-defined rules function independently. To prevent system-generated false alerts, basic rules should be temporarily disabled when creating custom rules. For instance, when the clinical diagnosis includes both anxiety and depression, the use of sodium valproate does not constitute off-label prescribing. Therefore, to ensure the accuracy and effectiveness of the custom rules, the indications \"anxiety\" and \"depression\" must be explicitly added to the off-label use module for sodium valproate .\u003c/p\u003e\u003cp\u003eFollowing their establishment, prescription review rules must undergo systematic testing to verify operational logic and clinical utility. The primary objectives of this phase are threefold: (1) to confirm the rules’ accuracy in identifying and managing diverse prescription scenarios, thereby ensuring clinical effectiveness; (2) to promptly identify and optimize potential issues affecting physician user experience, such as excessive or redundant alerts; and (3) to uncover and resolve unforeseen implementation risks, including rule conflicts, misjudgments, or omissions. Early resolution of such issues significantly enhances system accuracy, reliability, and end-user confidence. For instance, consider a rule designed to restrict outpatient prescriptions for phenobarbital tablets to a maximum duration of 14 days. If the rule is configured solely within the excessive-duration module without adequate testing, its intended function may be compromised when the medication is defined as \"non-splittable\" in the system. Since the system calculates treatment duration based on the total quantity prescribed, and if phenobarbital is set as non-splittable in the algorithm, any prescription for one full bottle or less would be incorrectly judged as appropriate by the system. This example underscores the necessity of comprehensively considering dosage form, algorithm logic, and other contextual factors during rule configuration to ensure scientific validity and practical feasibility. Additionally, when defining custom rules, it is critical to note that basic (default) rules and user-defined rules operate independently. To prevent conflicting alerts, basic rules should be temporarily disabled during the creation and testing of corresponding custom rules. For example, if sodium valproate is prescribed for a diagnosis encompassing both anxiety and depression, its use may not constitute off-label prescribing. To ensure custom rule accuracy, the indications “anxiety” and “depression” must be explicitly added to the off-label use module for sodium valproate, while any conflicting basic rules are suspended.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.2\u0026nbsp;\u003c/strong\u003eDevelopment of Core Competencies for Prescription Review Pharmacists\u003c/p\u003e\u003cp\u003eThe professional competency of prescription review pharmacists critically influences not only the efficiency of the review process but also the accuracy, depth, and sustainable evolution of the rule library. Consequently, establishing a scientific, systematic, and comprehensive core competency indicator system for these pharmacists is essential to enhance the professionalism and standardization of prescription reviews and to foster the continuous improvement of pharmaceutical care quality. The construction of this system integrated literature analysis and the Delphi expert consultation method to identify relevant competency indicators and establish an evaluation framework. The Analytic Hierarchy Process was subsequently employed to calculate the weight of each indicator, resulting in a scientifically robust and rational evaluation system. A comprehensive core competency indicator system for prescription review pharmacists should encompass multiple levels: primary indicators (e.g., Professional Competency, Technical Competency, Professional Development Competency, Pharmaceutical Service Competency), secondary indicators (e.g., Professional Ethos, Clinical Pharmacotherapy Involvement, Foundational Prescription Review Skills, Communication and Collaboration, Custom Rule-Formulation Ability, Research Capability, Learning and Development Capacity), and tertiary indicators (e.g., Drug Knowledge Proficiency, Identification of Pharmacological Issues in Prescriptions, Accurate Interpretation and Communication of Drug Label Information to Physicians, Key Review Aspects for Common Disease Medications). Weight analysis revealed that among the primary indicators, Professional Competency and Pharmaceutical Service Competency form the foundational basis for a pharmacist's competency in their role. Notably, among the secondary indicators, Clinical Pharmacy Service Capacity, Custom Rule-Formulation Ability, and Pharmaceutical Information Service Capacity are particularly significant for enhancing prescription review quality. Specifically, the formulation of custom rules requires pharmacists to master evidence-based pharmacy methods, synthesize the latest clinical guidelines, drug labelling information, and medical literature to scientifically develop and adjust review rules. Concurrently, they must be proficient with the prescription review system's operational procedures, possessing the technical ability to create, modify, and delete rules within the platform, as well as the analytical skills to monitor and interpret system data to promptly identify and resolve issues. Throughout the rule adjustment process, effective communication and collaboration with clinicians, information system engineers, and other relevant stakeholders are imperative to ensure the developed rules are both scientifically sound and practically feasible. Therefore, strong communication skills and a collaborative ethos are indispensable attributes for a competent prescription review pharmacist.\u003c/p\u003e\u003cp\u003eTo facilitate effective communication during the rule-adjustment process, a comprehensive set of measures has been instituted. These include the clear definition of communication objectives and content, regular meetings of the pre-prescription review panel, implementation of a mechanism for sharing physician contact information within the review interface, and explicit assignment of communication responsibilities to reviewing pharmacists. Furthermore, efforts have been made to cultivate an open and candid communication environment, complemented by established feedback mechanisms and structured training protocols. In their role as key liaisons for interdepartmental communication, reviewing pharmacists are tasked with identifying issues and facilitating the exchange of information. False-negative findings primarily arise from absent system data or incomplete data transmission between the Hospital Information System (HIS) and the Prescription Automatic Screening System (PASS). Addressing these issues necessitates robust communication channels among reviewing pharmacists, dispensing pharmacists, informatics pharmacists, and systems engineers. Conversely, false-positive alerts frequently originate from off-label drug use. Given that updates to drug labelling often lag behind rapidly evolving clinical evidence, off-label medication use remains an ongoing challenge within pre-prescription review [\u003cstrong\u003e21\u003c/strong\u003e].\u0026nbsp;Depending on the severity of the scenario, required responses may involve formal documentation for off-label use or the submission of an online request for rule modification. This process, which includes the implementation of necessary precautions and citation of relevant evidence-based medical literature, requires reviewing pharmacists to maintain proactive and consistent communication with departmental secretaries to ensure uninterrupted workflow progression.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.3. Accurate Documentation of Inappropriate Prescriptions by Dispensing Pharmacists\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePrescriptions deemed appropriate during the pre-review stage may still contain errors that fall outside the detection scope of the automated system or exhibit logical inconsistencies. Such cases are classified as false-negative prescriptions. Without thorough analysis by the prescription review pharmacist, these issues are likely to remain undetected. The verification performed by the dispensing pharmacist at the service window thus constitutes a primary method for identifying false-negative outcomes from the pre-review process. Intervention records and feedback provided by the dispensing pharmacist also offer crucial evidence for prescription review pharmacists to optimize review rules. To enhance this workflow, we refined the prescription intervention registration form and implemented a shared Excel spreadsheet accessible via the local area network (LAN) at both outpatient dispensing windows and prescription review pharmacists’ computer terminals. This modification facilitates the standardized management of prescription interventions and improves coordination between prescription review and dispensing pharmacists, thereby increasing team efficiency. Through this registration form, prescription review pharmacists can promptly identify false-negative cases missed during pre-review and update the rule database accordingly. Each documented case includes the date, patient information, prescription details, system review status, identified causes of the problem, and a description of the intervention (\u003cstrong\u003eFig.\u003c/strong\u003e 3).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.4\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eImprov\u003c/strong\u003e\u003cstrong\u003eing the Workflow of Prescription Review Pharmacists\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe workflow for pharmacists conducting prescription review should be comprehensive, integrating not only the core tasks of receiving and evaluating prescriptions, performing prescription audits, managing dispensing-window interventions, and maintaining communication and feedback channels, but also the critical review of rules associated with alterations in drug manufacturers or specifications. Furthermore, systematic analysis of inappropriate prescriptions is essential to generate robust data that informs and supports subsequent rule modifications.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.4.1\u0026nbsp;\u003c/strong\u003eReview of Management Protocols for Drug Manufacturer/Specification Changes and Newly Introduced Medications\u003c/p\u003e\u003cp\u003eGiven the uniqueness of drug identification codes, any modification in drug specifications or manufacturing sources necessitates re-verification of the alignment between the hospital information system and the drug knowledge base of the prescription review platform. This includes confirming data matching, adjusting unit conversions, updating drug attributes, and maintaining package inserts, with thorough documentation of all related rule modifications. In parallel, critical medication instructions should be annotated to support the timely updating of clinical guidance notes in the drug utilization checklist.\u003c/p\u003e\u003cp\u003eIn parallel with the nationwide deployment of electronic prescription circulation, the hospital's medical insurance formulary for externally dispensed medications is undergoing continuous expansion. For instance, the current dual-channel reimbursement drug list contains 413 distinct agents. For such externally prescribed drugs, the standardization and clinical appropriateness of prescriptions can only be guaranteed through the implementation of scientifically sound, comprehensive, and rationally designed system rules. To ensure the sustained development of this initiative, the hospital has integrated associated responsibilities into its performance evaluation framework. Specifically, for newly introduced drugs, externally dispensed medications, and products with altered specifications or manufacturers, information pharmacists are responsible for maintaining core drug data. Following this, the updated formulary is disseminated to the medication review team, where dedicated clinical review pharmacists subsequently verify the relevant rules and maintain corresponding audit records.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.2.4.2 Review and Analysis of Inappropriate Prescriptions\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe outpatient query function within the prescription pre-review system enables the selection and screening of prescriptions categorized as \"approved by pharmacist override,\" \"rejected for modification by the prescriber,\" \"dispensed with dual verification,\" or \"passed after automatic system intervention.\" This functionality facilitates a comprehensive analysis of the types and distribution of prescription-related issues, allowing for the timely implementation of targeted interventions and serving as a vital mechanism for identifying false-positive alerts. Prescriptions \"approved by pharmacist override\" refer to those initially flagged by the system as potentially inappropriate but subsequently approved by a reviewing pharmacist. This scenario may indicate either inaccuracies in the system's alert logic or an overly restrictive rule set. In contrast, cases resulting in \"rejection for modification by the prescriber\" or \"dispensing with dual verification\" typically reflect a clinician's judgment regarding therapeutic necessity or adherence to alternative clinical guidelines. For such instances, the reviewer must consult relevant evidence-based medical literature. If the prescribed regimen is supported by established evidence, it should be documented and archived according to standard clinical communication protocols. If not supported, direct communication with the prescriber is warranted to discuss potential adjustments to the treatment plan. The category of prescriptions \"passed after automatic system intervention\" represents transactions that occur based on predefined system logic, typically outside of a pharmacist's immediate review window. Proactive monitoring of these prescriptions is essential, as it allows for a more complete identification of issues requiring documentation or reveals other medication-related problems, such as incorrect drug or dose selection. In these cases, timely communication with either the prescriber or the patient is necessary to facilitate correction, followed by appropriate documentation. For prescribers who repeatedly disregard system alerts, raising awareness through institution-wide dissemination of review findings can be an effective strategy. Furthermore, rigorous prescription evaluation by clinical pharmacists serves a dual purpose: it not only constitutes a key metric for evaluating pharmacist performance but also aids in identifying latent deficiencies within the clinical rule base. This process fosters a closed-loop quality management system that integrates pre-prescription review, point-of-dispensation pharmacist intervention, and retrospective prescription analysis. This continuous cycle is fundamental to the ongoing optimization of the rule base and the sustained improvement of overall prescription quality.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.3 Check Phase \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.3.1\u0026nbsp;\u003c/strong\u003eComparative Analysis of Prescription Intervention Outcomes\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFollowing the implementation of PDCA cycle management, a comparative analysis of key performance indicators revealed significant improvements in the prescription review process. The proportion of irrational prescriptions intervened by pharmacists at the point of dispensing decreased markedly from 0.11% to 0.008% (χ² = 249.019, P \u0026lt; 0.005), indicating a substantial reduction in false-negative alerts from the pre-review system. Concurrently, the rate of prescription tasks flagged as irrational post-system-review declined from 19.30% to 10.61% (χ² = 5668.134, P \u0026lt; 0.005).\u0026nbsp;Simultaneously, the physician modification rate increased from 60.36% to 70.52% (χ²=616.086, \u003cem\u003eP\u003c/em\u003e\u0026lt;0.005), reflecting an improved willingness to adjust prescriptions in response to system alerts. Collectively, these improvements contributed to a rise in overall prescription compliance from 93.28% to 97.94% (χ² = 5653.660, P \u0026lt; 0.005) (Table \u003cstrong\u003e2\u003c/strong\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e System Audit Intervention Status\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003eTotal Tasks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eWindow Interventions (Rate%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eSystem-Reviewed Irrational Tasks (Rate%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePhysician Modifications Post-Review (Rate%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003eTask pass rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eNov 2022 - Jan 2023 (Pre-PDCA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e118,587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e132 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22,886 (19.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e13,815 (60.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e93.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eAug 2023 - Oct 2023 (Post PDCA Round 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e248,223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e82 (0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e32,633 (13.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21,797 (66.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e96.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eMay 2024 - Jul 2024 (Post PDCA Round 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e279,458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e35 (0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e30,766 (11.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e21,757 (70.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e97.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003eFeb 2025 - Apr 2025 (Post PDCA Round 3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e302,514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e23 (0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e32,092 (10.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e22,630 (70.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e97.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003eNote: \u0026nbsp; due to the impact of the COVID-19 pandemic between November 2022 and January 2023, the total number of prescriptions was relatively low.\u0026nbsp;\u003c/p\u003e\u003cp\u003e\u003cstrong\u003e2.3.2 Improvement in Physician Satisfaction \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\u003cp\u003ePhysician satisfaction, which reflects their approval of the pre-prescription review system, was assessed in this study. Using a five-point Likert-scale questionnaire, data were collected from 52 outpatient clinics to evaluate physicians' perceptions of the system[\u003cstrong\u003e22\u003c/strong\u003e\u003cstrong\u003e]\u003c/strong\u003e.\u0026nbsp;The survey items addressed the prescribing pharmacists' professional knowledge and communication skills, as well as the consistency of the system’s warning messages with clinical medication use. Satisfaction specifically regarding this consistency is presented in Table 3. Overall satisfaction (combining 'very satisfied' and 'satisfied' responses) was higher following the implementation of PDCA management (40.4%) compared to the pre-implementation period (9.6%). A rank-sum test indicated that this difference in overall physician satisfaction with the warning messages before and after PDCA management was statistically significant (Z = -2.180, P \u0026lt; 0.05).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eComparison of Satisfaction Survey Results for Warning Messages in the Prescription Pre-Audit System [n(%)]\u0026nbsp;\u003c/p\u003e\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"555\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eVery satisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eSatisfied Fairly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eDissatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eVery Dissatisfied\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003ePre-PDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0（0.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e5(9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e30(57.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e15(28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e2(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003ePost- PDCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0（0.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e21(40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e24(46.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e7（13.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0（0.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"3 Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Summary of Experiences and Lessons Learned\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThrough practical exploration, this study has delineated core strategies for enhancing the performance of a prescription pre-audit system. Preliminary research serves to anchor the adjustment of system rules in clinical needs, while continuous monitoring and data analysis provide dynamic evidence for performance optimization. Rule adjustments are grounded in evidence-based medicine grading to ensure scientific rigor. Furthermore, iterative rule updates, coupled with multidisciplinary communication, are indispensable for maintaining the system’s adaptability to evolving clinical demands. However, the study also identifies areas requiring improvement in current practice: the professional capacity of reviewing pharmacists in optimizing rules remains inadequate, and feedback mechanisms within departments often lack closure, potentially compromising the system’s responsiveness to complex clinical scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTwo critical practices warrant particular emphasis:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(a) Standardization of Intervention Documentation at Dispensing Windows. Optimizing the intervention process has significantly increased the documentation rate by dispensing pharmacists. This practice not only furnishes real-world data for rule optimization by reviewing pharmacists but also establishes a traceability mechanism for prescriptions flagged by the system’s automatic intervention. Crucially, detailed documentation of \"unreasonable or erroneous prescriptions enforced by doctors when a reviewing pharmacist was unavailable\" enables reviewing pharmacists to fully comprehend interventions made at the dispensing stage. If no window-level intervention was recorded, the reviewing pharmacist can proactively communicate with the physician and patient using this documented information, thereby forming a vital \"secondary line of defense\" for medication safety.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e(b) Closed-loop Management of the Rule Base. As the system's core, the rule base requires dynamic optimization throughout a \"pre-event, during-event, post-event\" cycle. Pre-event management necessitates strengthening the baseline rule repository based on enhanced competency building for prescribing pharmacists. During an event, it is critical to accurately document system failures to identify irrational prescriptions, thereby clarifying rule gaps. Post-event, rule iteration should be driven by prescription review and data analysis. This closed-loop model essentially implements \"full life-cycle management\" of the rule base, representing a sustainable logic for ensuring the long-term accuracy of the system audit.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Role Reconstruction under Dynamic Rule Base Updates: Opportunities and Challenges\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmidst ongoing healthcare reforms and the evolution of pharmacy service models [\u003cstrong\u003e23-27\u003c/strong\u003e], the Computerized Prescription Pre-review System has emerged as a pivotal platform for enhancing the quality of pharmaceutical care [\u003cstrong\u003e28, 29\u003c/strong\u003e]. This study, aligning with a growing body of domestic research, employs the PDCA cycle to establish a systematic framework for managing the rule base. The core of this approach involves exploring integrative pathways that synchronize system development with pharmacy services, achieved by reinforcing both \"the centrality of the rule base\" and\u0026nbsp;\"the leading role and responsibility of the reviewing pharmacist.\"\u0026nbsp;This dynamic context presents distinct opportunities for pharmacist role evolution. Reviewing pharmacists are positioned to actively engage in the standardization and refinement of the rule base, embedding clinical pharmacy expertise into its operational logic—for instance, by optimizing alert thresholds based on actual prescribing patterns [\u003cstrong\u003e30\u003c/strong\u003e]. Furthermore, they can enhance interdisciplinary communication with systems engineers to facilitate the effective translation of pharmaceutical needs into technical specifications. The creative utilization of system-generated data, such as conducting targeted educational interventions based on patterns of frequent prescription irregularities, enables pharmacists to improve prescriber acceptance rates and advance rational medication use.\u003c/p\u003e\n\u003cp\u003eConcurrently, the imperative for dynamic rule base updating poses a multifaceted challenge to pharmacist competencies. The rapid iteration of medical knowledge necessitates that both reviewing and informatics pharmacists establish robust, rapid-response mechanisms. The entire workflow—from distilling evidence from current guidelines and justifying rule modifications, to building clinical consensus and validating post-implementation outcomes—must become routine, standardized practice. This process represents a comprehensive assessment of pharmacists' capabilities in evidence-based practice, interdisciplinary collaboration, and systems thinking. Successfully navigating this challenge is essential for transforming pharmacy services from a primarily reactive \"prescription review\" function toward a more proactive model of \"clinical empowerment.\"\u003c/p\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study implemented a pharmacist competency-based PDCA cycle system, which was structured around dynamic management of a rule database and driven by multi-source feedback. The intervention yielded significant outcomes: a pronounced reduction in the proportion of irrational prescriptions intercepted at the dispensing window, alongside substantial improvements in both physician prescription modification rates and satisfaction scores. The system proved effective in enhancing the accuracy and efficiency of prescription review and in optimizing the clinical prescription management workflow. Nevertheless, a specific challenge persists, primarily concerning prescriptions for medications (excluding antimicrobials) with dosages below those recommended in the official drug labeling. Future work will concentrate on this issue by continuously refining the rule library and review logic through iterative PDCA cycles, thereby improving the comprehensiveness of the review system. It is critical to emphasize that the PDCA cycle is not a finite process concluding after a set number of iterations, but rather a continuous, ongoing quality improvement mechanism. While each cycle resolves a subset of false-positive or false-negative alerts, the dynamic nature of clinical practice\u0026mdash;including the introduction of new medications, updates to drug monographs, and revisions to clinical guidelines\u0026mdash;inevitably generates new alerts of a similar nature. Therefore, sustaining these continuous improvement cycles is essential. Through this iterative and enduring process, the intelligent prescription review system achieves dynamic adaptation and long-term optimization, ultimately providing stronger support for promoting rational clinical medication use.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe team would like to thank the staff of the Pharmacy Department and all the physicians \u0026nbsp;who supported this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.H. contributed to conceptualization, H.L. drafted the manuscript, L. supervised the study, C.L.performed formal analysis, and Z.Z.prepared the figures. All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the \u0026nbsp;Key Research, Development and Promotion Special Project of Xuchang City Science and Technology Bureau (No. 2025039).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough this study did not directly involve human participants, it was conducted in accordance with the ethical principles of the Declaration of Helsinki, ensuring patient safety and the integrity of healthcare practices. This study was approved by the Ethics Committee of Xuchang Central Hospital (2025-05-014).The need for written informed consent was waived by the Ethics Committee of Xuchang Central Hospital due to the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXie SS, Liu S, Wan JJ, et al. Practice and effect of pharmacists conducting prospective prescription review in outpatient and emergency settings with the aid of rational drug use software. China Pharm. 2021;32(7):876\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan X, Chen D, Bao S, Dong X, Fang F, Bai R, Zhang Y, Zhang X, Tang W, Ma Y, et al. 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Chin J Hosp Pharm. 2024;44(11):113\u0026ndash;8.\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PDCA cycle, Pre-prescription review system, Pharmacist capacity building, Rule database management","lastPublishedDoi":"10.21203/rs.3.rs-8992981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8992981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aimed to enhance the operational efficacy of the pre-prescription review system by applying the PDCA (Plan-Do-Check-Act) cycle management model, with the goal of improving prescription review accuracy, strengthening rational drug use management, and bolstering patient medication safety.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the PDCA cycle framework, the fishbone diagram was used to systematically identify potential factors affecting pre-prescription review accuracy across seven dimensions: physicians, pharmacists, competent authorities, systems, drugs, regulations, and interdepartmental collaboration. The 80/20 rule was applied to prioritize key factors, and targeted improvement strategies were formulated using the 5W1H method. Outcomes were quantitatively evaluated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix critical factors influencing review accuracy were pinpointed: delays in registering off-label drug use, insufficient pharmacist competency, untimely analysis of irrational prescriptions, inadequate rule adjustment procedures, unclear rule revision/approval processes, and limited system intelligence. Key interventions included centralized rule database management, core competency development for prescription review pharmacists, accurate documentation of irrational prescriptions at dispensing windows, and optimization of pharmacist workflows. Post-implementation, the proportion of irrational prescriptions identified by window pharmacists dropped from 0.11% to 0.008% (χ² = 249.019, P \u0026lt; 0.005). The system's post-review irrational task rate decreased from 19.30% to 10.61% (χ² = 5668.134, P \u0026lt; 0.005). Physician prescription modification rates rose from 60.36% to 70.52% (χ² = 616.086, P \u0026lt; 0.005), and overall prescription compliance improved from 93.28% to 97.94% (χ² = 5653.660, P \u0026lt; 0.005). Physician satisfaction with the system increased significantly from 9.6% to 40.4% (Z = -2.180, P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PDCA cycle management model effectively optimizes the pre-prescription review system. Its core value lies in enhancing alert accuracy and system adaptability through multidimensional analysis and targeted interventions, while promoting system intelligence and informatization. This model offers a practical, replicable framework for improving pharmaceutical service quality and for the continuous, refined optimization of pre-prescription review processes.\u003c/p\u003e","manuscriptTitle":"PDCA Cycle-Driven Whole-Chain Optimization of the Pre-Prescription Audit System: A Multidimensional Approach from Pharmacist Competency Development to Dynamic Rule Database Management","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 08:59:14","doi":"10.21203/rs.3.rs-8992981/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-17T22:36:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96294959124317851883571287373658641430","date":"2026-05-13T23:17:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T02:48:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T02:40:01+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T05:31:59+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-10T02:53:24+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2026-03-10T01:19:56+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b3048dc1-93c3-4145-b0b5-695624da5cac","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-17T22:36:36+00:00","index":97,"fulltext":""},{"type":"reviewerAgreed","content":"96294959124317851883571287373658641430","date":"2026-05-13T23:17:40+00:00","index":92,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-18T08:59:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 08:59:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8992981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8992981","identity":"rs-8992981","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}