Introduction

Central sterile supply departments (CSSDs) are responsible for the standardized reprocessing of reusable surgical instruments and sterile packages, and their performance directly affects perioperative safety, operating room efficiency, and hospital infection prevention.1 Instrument reprocessing is a multi-step workflow, and any deviation at upstream steps may propagate downstream and increase the likelihood of defects, rework, and potential patient harm.2 Evidence-based guidance consistently emphasizes that disinfection and sterilization can only be reliable when reprocessing is performed correctly and consistently, with cleaning as the indispensable prerequisite.3,4 Among these steps, cleaning quality is foundational because residual soil can compromise subsequent disinfection and sterilization performance.5,6 The Centers for Disease Control and Prevention guideline on disinfection and sterilization explicitly notes that sterilization is compromised if not preceded by meticulous cleaning.6 Empirical evidence also suggests that residual contamination may persist after routine processing in sterile service settings, underscoring the need for systematic quality assurance rather than reliance on informal checks alone.7 Therefore, monitoring cleaning and disinfection performance using operational quality indicators is necessary to identify deviations early and prevent the accumulation of risk within high-throughput CSSD workflows.

Packaging management represents another critical control point because packaging integrity and correctness determine whether sterilized items remain protected until point of use and whether clinical teams receive complete and accurate instrument sets.8 Observational evidence based on surgical instrument tracking systems has shown that packaging errors occur in routine practice, including errors directly relevant to CSSD safety management such as wrong packaging tags, incomplete packages, instrument missing, and mismatches in instrument specifications.9 Such defects can translate into reprocessing demands, disrupted perioperative workflows, and avoidable safety hazards, highlighting the importance of strengthening packaging-related governance in parallel with upstream cleaning and disinfection control.10

However, in conventional CSSD operations, quality control is often implemented through periodic inspections or audits.10 Currently, there is a lack of sufficiently standardized mechanisms to ensure that identified defects are continuously analyzed and resolved. A pragmatic, CSSD-centered defect management improvement model could enhance both the immediate process quality and the long-term employee capabilities through transforming “defect discovery” into a repeatable improvement cycle with targeted training and supervision.11 In this context, the “defect management improvement model” has advantages as it operates quality improvement as a convention: standardized defect recording, structured analysis of defect patterns and causes, implementation of corrective measures, feedback and closure, as well as training and supervision for continuous behavioral and procedural changes.12 The closed-loop improvement approach is increasingly being applied to CSSD-related issues. Medical Failure Mode and Effect Analysis (HFMEA) has been used to identify high-risk steps and reduce the defect rate of surgical instrument packaging in the CSSD environment by 7%.13 Root Cause Analysis (RCA) has also been applied to packaging-related defects, and intervention measures based on cause analysis and employee training have shown measurable improvements.14 Additionally, a cycle-based quality improvement method including problem identification, cause analysis, implementation and re-evaluation has been reported to reduce the defect rate of sterile packaging processed in the CSSD.15 The action research model emphasizing the “plan-do-observe-reflect” cycle and frontline participation also shows that after standardization and checklist-based verification, the number of instrument packaging defects has decreased.16 These studies collectively support the feasibility and potential effectiveness of structured, feedback-driven defect management in improving the quality of CSSD processes.

In China, the WS 310–2016 national standards provide the regulatory framework for CSSD operations.17 The National Institute of Hospital Administration has established nine CSSD practice training bases across the country, offering certification courses in sterilization theory, equipment management, and quality monitoring.18 Despite these national guidelines and training resources, routine quality audits at our institution have persistently revealed reprocessing defects, with instrument set recalls from operating rooms and clinical wards occasionally required for re-sterilization. This gap motivated the present study. The five-member defect management team received training in root cause analysis (RCA) and Healthcare Failure Mode and Effect Analysis (HFMEA) based on established CSSD training protocols.14 This study therefore evaluated whether a closed-loop defect management improvement model could reduce reprocessing defects. This study investigated whether a closed-loop defect management improvement program was associated with changes in surgical instrument reprocessing quality in a central sterile supply department. It aimed to examine changes in key cleaning and packaging quality indicators before and after implementation, thereby evaluating the program’s impact on process reliability. The findings are expected to provide practical evidence to support quality improvement strategies that enhance perioperative safety and standardize CSSD management.

Methods Study Design and Setting

We conducted a retrospective quasi-experimental before–after study in the Central Sterile Supply Department (CSSD) of West China Hospital. The pre-intervention period was January to June, 2025 (conventional management), and the post-intervention period was July to December, 2025 (the closed-loop defect management improvement model). The study was conducted at West China Hospital, a 4,300-bed tertiary teaching hospital performing approximately 180,000 surgical procedures annually. The CSSD processes approximately 700,000 instrument packages per year with 42 full-time staff (12 registered nurses and 30 technicians/workers).

Participants

The study unit was the reusable surgical instrument package (instrument set/package) reprocessed in the CSSD. Inclusion criteria were: (1) instruments originated from our hospital and met relevant national and industry standards; (2) instruments were managed by the same CSSD staff group throughout the study. Exclusion criteria were: (1) damaged instruments or instruments with unclear/illegible identification; and (2) external (non-hospital) instruments.

Conventional Management (Pre-Intervention Phase)

During the pre-intervention phase, the CSSD followed the hospital’s routine workflow for instrument reprocessing (receiving, cleaning, rinsing, disinfection, drying, inspection, assembly, packaging, and documentation) under an existing quality management framework.19

Closed-Loop Defect Management Improvement Model (Intervention)

In the post-intervention phase, conventional management was maintained and strengthened by a closed-loop defect management improvement model that operationalized a “record–analyze–correct–feedback–train/supervise” cycle. The intervention was implemented as follows, with responsibilities and procedures standardized to support reproducibility (Figure 1).

Figure 1 Operational Framework of the Closed-loop Defect Management Improvement Model.

Multidisciplinary Defect Management Team and Governance

The team members include a supervisor nurse (with 10 years of experience in CSSD) and four department supervisors (each with 5–10 years of experience). The training is jointly responsible by the supervisor nurse and department supervisors. Before implementation, the team received a 4-hour systematic training, covering defect management concepts, root cause analysis methods (fishbone diagram, 4M1E framework: personnel, machine, material, method, environment), and the application of Pareto analysis in defect priority ranking. Subsequently, all 42 front-line employees received 2-hour practical training, including the new standardized defect record procedure and feedback mechanism. The training attendance rate was 100%, and the post-training assessment results showed a pass rate of 100%. In addition, review training sessions of 15 to 20 minutes were conducted monthly during the implementation period. During the two phases, routine sampling audits were carried out daily (approximately 175 devices per working day, approximately 21 packages, 6 days per week, for a total of 26 weeks); defects were classified into three categories: severe (affecting sterilization effectiveness or patient safety, requiring immediate handling), major (requiring rework within 24 hours), and minor (correctable on-site). For each defect, corrective measures were recorded and verified, through continuous three consecutive defect-free working days’ enhanced sampling, and signed by both department supervisors and nurses. Feedback was provided through immediate oral notification, daily morning meetings, weekly written summaries, and monthly case discussions. Monthly random observations were conducted to monitor employee compliance, and the compliance rate increased compared to before the intervention.

Standardization of Operational Requirements

Check procedures were refined based on WS 310.3–2016 and the hospital’s existing protocols, covering 12 procedure items (visual inspection, ATP testing, protein residue testing, functionality, dryness, assembly, packaging integrity, sealing quality, rigid container inspection, labeling, chemical indicator, and weight verification). Key operational requirements were reiterated and reinforced, including (as applicable in routine practice) use of softened water and multi-enzyme detergent for cleaning, and complete disassembly/segmentation cleaning for detachable instruments to avoid retained soil in joints, lumens, and complex structures.

Defect Recording, Tracking, and Root-Cause-Oriented Improvement

A nonconformity recording and tracking mechanism was implemented to document defects, analyze causes, and propose corrective actions. Defect recording, tracking, and root-cause-oriented improvement were operationalized through: (a) a standardized electronic log documenting each defect; (b) daily spot checks by shift supervisors, weekly summaries, and monthly team reviews; (c) a five-member management team (section leads) responsible for analysis; and (d) root cause analysis using fishbone diagrams (4M1E framework: manpower, machine, material, method, environment) and Pareto analysis for defect prioritization.

Enhanced Supervision and High-Frequency Audits

Supervision intensity was increased through high-frequency spot checks and real-time feedback. Where available and consistent with institutional policy, work-area monitoring supported compliance supervision and targeted coaching. Defect summaries were communicated to staff in a timely manner, and solutions were disseminated through corrective-action notices and on-site guidance to ensure that defects were not only detected but also corrected and prevented from recurring.

Feedback Closure, Periodic Evaluation

A monthly comprehensive evaluation was led by the CSSD head nurse, and section leaders submitted electronic reports summarizing defect patterns and closure status. Regular case discussions and skills-competition activities were used to encourage frontline reporting, facilitate shared learning, and strengthen problem-solving capacity, thereby consolidating procedural adherence and sustaining improvement over time. The CSSD operates a single daytime shift (8:00–17:00) on weekdays, with an on-call team available for emergency cases after hours. A total of 42 staff members (12 registered nurses and 30 technicians/workers) are employed, with 8–10 staff assigned daily to decontamination, 6–8 to inspection and assembly, 4 to packaging, and 4 to sterilization support. During the post-intervention phase, verification steps were assigned as follows: visual inspection and ATP testing were performed by frontline staff at each workstation, with supervisory verification conducted by the five-member team rotating across workstations throughout each shift. Although the five team members were not all present simultaneously in all areas, at least one team member was present in each major work area (decontamination, assembly, packaging, sterilization) during routine hours, with shift supervisors assuming verification responsibilities after hours. This structure ensured continuous oversight without requiring all five members to be physically present across all shifts.

Data Collection

Data were collected from routine CSSD operational records, including: (1) routine cleaning quality inspection and sampling logs; (2) packaging inspection logs prior to sterilization; and (3) nonconformity logs generated through the tracking mechanism in the post-intervention phase. (4) Quality control inspection and sampling records: cleaning sampling audits, packaging inspection results, labeling verification logs, and assembly checklist verification logs. Two trained quality-control staff extracted and verified data using a standardized data abstraction sheet. Discrepancies were resolved by discussion with a senior supervisor and cross-checking original records.

Measurements

The cleaning and packaging inspection was carried out using a fixed sampling plan that was implemented in two stages: Cleaning quality inspection: approximately 175 devices per working day, 6 days per week, 26 weeks per stage (27,206 devices per stage); Packaging inspection: approximately 21 packages per working day, 6 days per week, 26 weeks per stage (3,244 packages per stage). In contrast, the error event data reported by the operating room came from a complete census of all A-Class processing packages (107,943 before the intervention, 107,984 after the intervention). Due to the daily variation in the total processing volume, it is impossible to precisely calculate the sampling ratio relative to the total processing volume; however, the fixed-rate sampling remained consistent throughout each stage. The comparability between the categories was confirmed through baseline characteristics (Table 1). As this is a routine operational audit, individual projects or personnel may be sampled multiple times within six months. However, when a defect is identified, the item is recorded as non-conforming at the first detection; subsequent occurrences of the same defect for that item are no longer counted.

Table 1 Comparison of Baseline Data for the Two Groups of Surgical Instruments

Cleaning Quality Indicators Qualified Rate of Regular Sampling Inspection for Cleaning Quality

A sampled instrument/pack was classified as passing if it met the department’s predefined acceptance criteria for cleaning verification,20 including: a) Visual cleanliness: the instrument surface (including joints/serrations) was visually clean and shiny, with no residual blood, soil, scale, or rust, assessed by direct visual inspection and/or a lighted magnifier in line with the routine monitoring requirements in WS 310.3–2016. b) The results of pollutant residue detection are within the qualified threshold for cleaning quality. For ATP testing, according to the manufacturer’s specifications, the threshold for a qualified result is ≤200 relative light units (RLU); for protein residue testing, when using the OPA colorimetric method, the threshold is ≤1 μg/cm2. The sampling sites follow the manufacturer’s instructions and are incorporated into the facility cleaning verification process. The testing focuses on high-risk areas, such as cavities (inner diameter ≥2 mm), hinge connection joints, and serrated surfaces. Items failing either visual inspection or residue testing were classified as nonconforming. Qualified rate of regular sampling inspection for cleaning quality increased=number of qualified items in sampling inspection/total number of sampled instruments after cleaning × 100%.

Packaging Management Indicators

Packaging management indicators were assessed through routine audits of packages awaiting sterilization and through documented defect records. The calculation formulas were adapted from previously published CSSD quality-indicator studies.21 Packaging pass rate22=Number of qualified packages in sampling inspection of items awaiting sterilization/total number of inspected packages×100%.

Operating Room–Reported Instrument Set Error Rate

Operating room–reported instrument set error rate (complaint-based) refers to issues that affect instrument use and are identified by the user department during clinical use, involving reusable medical devices/instruments and instrument sets/packs.23 (a) Cleaning quality nonconformity rate: number of sets/packs reported by the operating room in the current month as having unacceptable cleaning quality for items within reusable devices/instruments/sets/total number of instrument sets/packs processed in the same month. The detection standards are the same as those mentioned above. 1). (b) Assembly error rate: number of sterilized sets/packs reported by the operating room in the current month as not matching the assembly checklist / total number of instrument sets/packs processed in the same month. (c) Label information error rate: number of reusable device/instrument sets/packs reported by the operating room in the current month as having incorrect label information / total number of instrument sets/packs processed in the same month. (d) Package integrity breach rate: number of reusable device/instrument sets/packs reported by the operating room in the current month as having compromised package integrity / total number of instrument sets/packs processed in the same month. (e) Instrument damage incidence rate: number of sets/packs reported by the operating room in the current month as involving damaged medical instruments/devices / total number of instrument sets/packs processed in the same month.

Bias Control and Comparability Measures

To reduce biases and enhance the effectiveness of comparisons, we employed two design strategies. Firstly, we maintained consistency in core equipment, packaging types, surgical frequencies, personnel allocation, and sampling schemes during both the pre-intervention and post-intervention phases (Table 1 and Supplementary Table 1), thereby confirming baseline comparability. Secondly, we designated July 2025 as the initial implementation month and included the post-intervention phase (from July to December 2025).

Statistical Analyses

All results were analyzed in the form of ratios, with clear numerical values for both the numerator and the denominator. Count data were presented as frequencies and percentages. For between-group comparisons of categorical outcomes, the chi-square (χ2) test was used when expected cell counts were ≥5. When expected cell counts were <5, Fisher’s exact test was applied instead. To quantify the magnitude of changes, effect sizes were calculated as absolute risk differences (RD) for all outcomes, and as relative risks (RR) for cleaning and packaging indicators or rate ratios for operating room–reported error events, each accompanied by 95% confidence intervals (CI). RD was expressed as the post-intervention value minus the pre-intervention value, in percentage points. For rare outcomes with very low event counts, exact Poisson methods were used for rate ratio confidence interval estimation. A two-sided P<0.05 was considered statistically significant. All statistical analyses were performed using SPSS (17.0, IBM Corp., Armonk, NY, USA). The reporting of effect estimates with 95% CIs followed contemporary recommendations for before–after studies to move beyond P-value-only interpretations and provide clinically interpretable measures of improvement.

Ethics

The study protocol was reviewed and approved by the West China Hospital ethics committee (approval number: 2026 Audit (186) Number). As the study was based on instrument/package process data and departmental quality management records, and does not involve any additional interventions or risks. Informed consent has been waived. Reporting of this quasi-experimental before–after study was guided by STROBE,24 and the intervention description followed TIDieR;25 quality-improvement reporting considerations were informed by SQUIRE.26

Results Reprocessing Volume and Data Completeness

In 2025, a total of 54,412 cleaned medical devices and 6,488 packages of items to be sterilized were randomly inspected throughout the year. As per the pre-defined schedule, July 2025 was designated as the initial implementation and adaptation month, during which the new procedures were introduced. We included this period (from July to December 2025) in the post-intervention stage of the main analysis; for packaging data, the tracking system contains complete records from January to December 2025 (215,927 packages), of which there were 107,943 packages from January to June. The post-intervention packaging analysis included data from July to December 2025 (107,984 packages).

Comparison of Baseline Data for the Two Groups of Surgical Instruments

In the post-intervention phase, all 42 frontline staff and the five-member management team completed the training program (attendance rate: 100%; post-training assessment pass rate: 100%). In the pre-defined quality control samples, the instrument and packaging characteristics before and after the intervention were basically similar (Table 1). Regarding the instruments, there was no significant difference in the composition of the source departments and the complexity of the instruments at different stages (P>0.05). Regarding the packaging, the distribution of source departments, packaging material types, and packaging complexity was also comparable at different stages (all P>0.05). Overall, these findings indicate that the operational case combinations during different periods remained stable, thereby supporting the reliability of the before-and-after comparison.

Cleaning Quality Outcomes

The regular sampling results showed that the quality of cleaning had significantly higher after the implementation (Table 2). The cleaning pass rate in the regular sampling inspections increased from 97.99% (26,658/27,206) in the pre-intervention period to 99.76% (27,141/27,206) in the post-intervention period, representing an absolute increase of 1.770 percentage points (95% CI: 1.602 to 1.951; P<0.001). At the same time, the rate of unqualified cleaning decreased from 2.01% to 0.24%. Among them, the number of unqualified by visual inspection decreased from 499 to 59 cases (RD: −1.622, 95% CI: −1.791 to −1.453; P<0.001), and the number of unqualified by ATP testing decreased from 47 to 6 cases (RD: −0.154, 95% CI: −0.219 to −0.105; P<0.001). Protein residue failures were rare, occurring in 2 cases pre-intervention and 0 cases post-intervention, with no statistically significant difference (RD: −0.007, 95% CI: −0.023 to 0.011; Fisher’s exact P=0.500). The qualified rates of cleaning before and after implementation of management gradually increased (Figure 2).

Table 2 Comparison of Cleaning Quality Between the Two Groups

Figure 2 The pass rates of cleaning before and after the intervention.

Notes: Data represent routine sampling inspections (175 instruments/day, 6 days/week). Vertical dashed line marks the start of the intervention period (July 2025).

Packaging Management Outcomes

After implementation, the packaging performance was significantly better (Table 3). The packaging pass rate increased from 96.73% (3138/3244) to 98.98% (3211/3244), with an absolute improvement of 2.251 percentage points (95% CI: 1.520 to 2.982; P<0.001) (Figure 3). The number of non-conforming cases for closed packaging decreased from 71 to 17 (RD: −1.673 percentage points, 95% CI: −2.209 to −1.132; P<0.001), and the number of non-conforming Rigid container boxes decreased from 18 to 6 (RD: −0.377 percentage points, 95% CI: −0.684 to −0.063; P=0.014). The number of non-conforming sealed-type packaging decreased from 17 to 10, but this reduction did not reach statistical significance (RD: −0.221 percentage points, 95% CI: −0.551 to 0.126; P=0.177).

Table 3 Comparison of Packaging Quality Between the Two Groups

Figure 3 The pass rates of packaging before and after the intervention.

Notes: Data represent routine sampling inspections (21 packages/day, 6 days/week). Vertical dashed line marks the start of the intervention period (July 2025).

Comparison of the Two Groups of Error Events

After the implementation of management, the error rate of surgical instruments were significantly lower in the post-intervention period. The number of assembly errors dropped from 43 cases (3.98‱) to 15 cases (1.39‱), with an absolute reduction of −0.026 percentage points (95% CI: −0.041 to −0.011; P<0.001). Packaging integrity damage decreased markedly from 57 cases (5.28‱) to 6 cases (0.56‱), with an RD of −0.047 percentage points (95% CI: −0.062 to −0.033; P<0.001). Incorrect label information cases decreased from 15 cases (1.39‱) to 5 cases (0.46‱), with an RD of −0.009 percentage points (95% CI: −0.017 to −0.001; P=0.025). Insufficient cleaning and instrument damage also decreased, but these reductions did not reach statistical significance (Table 4).

Table 4 Comparison of the Two Groups of Error Events

Discussion

The Central Sterile Supply Department (CSSD) is responsible for reusable surgical instrument reprocessing, directly impacting perioperative safety and infection control.12,27–29 However, traditional quality control relies on periodic inspections without systematic defect analysis.6 This retrospective before-and-after comparative study aimed to evaluate whether the implementation of a closed-loop defect management improvement model in a high-throughput disinfection supply center was associated with better cleaning outcomes and fewer packaging defects. Among the six pre-defined indicators, we observed statistically significant improvements after the implementation, including higher sampling pass rates for cleaning and packaging, as well as lower rates of label information errors, packaging integrity damage, and assembly errors. This study, through the intervention of the closed-loop defect management improvement project, was associated with enhancing the quality of surgical instrument reprocessing in the CSSD, with the aim of providing practical evidence for improving perioperative safety and promoting the standardization of CSSD management.

Cleaning is the indispensable prerequisite for effective disinfection and sterilization; therefore, strengthening upstream decontamination control is a rational target for defect management. A classic synthesis of disinfection and sterilization principles emphasizes that cleaning must precede high-level disinfection and sterilization, because residual soil can protect microorganisms and undermine subsequent steps.30 The cleaning pass rate in the regular sampling inspections increased from 97.99% (26,658/27,206) in the pre-intervention period to 99.76% (27,141/27,206) in the post-intervention period, representing an absolute increase of 1.770 percentage points (95% CI: 1.602 to 1.951; P<0.001). At the same time, the rate of unqualified cleaning decreased from 2.01% to 0.24%. Among them, the number of unqualified cases identified by visual inspection decreased from 499 to 59 cases (RD: −1.622 percentage points, 95% CI: −1.791 to −1.453; P<0.001), and the number of unqualified cases identified by ATP testing decreased from 47 to 6 cases (RD: −0.154 percentage points, 95% CI: −0.219 to −0.105; P<0.001). Protein residue failures were rare, occurring in 2 cases pre-intervention and 0 cases post-intervention, with no statistically significant difference (RD: −0.007 percentage points, 95% CI: −0.023 to 0.011; Fisher’s exact P=0.500). In our study, the qualified rate of regular sampling inspection for cleaning quality increased while the cleaning nonconformity rate decreased, indicating fewer loads requiring rework and a tighter process around acceptance criteria. This is consistent with the result of a study that indicates that implementing intervention measures can increase the pass rate of equipment.23 This might be because unified inspection standards, structured defect records, rapid feedback, and supervised targeted training can effectively inspect and improve quality. In the disinfection supply center, reducing the rate of non-conformity can have a significant operational impact, as each non-conforming load will cause subsequent delays, additional manpower requirements, and potential disruptions to the workflow.

Packaging is a key control link that ensures a sterile environment and ensures that the correct and complete products can be delivered to the usage location. The results of this study show that: After implementation, the packaging performance was significantly better (Table 3). The packaging pass rate increased from 96.73% (3,138/3,244) to 98.98% (3,211/3,244), with an absolute improvement of 2.251 percentage points (95% CI: 1.520 to 2.982; P<0.001). The number of non-conforming cases for closed-type packaging decreased from 71 to 17 (RD: −1.673 percentage points, 95% CI: −2.209 to −1.132; P<0.001), and the number of non-conforming rigid container boxes decreased from 18 to 6 (RD: −0.377 percentage points, 95% CI: −0.684 to −0.063; P=0.014). A study has shown that in large hospital environments, the combination of Lean Six Sigma with governance and personnel allocation improvements significantly increases the first-pass rate and reduces tray defect rates, further demonstrating the value of methods specifically targeting variation, employee capabilities, and feedback loops.31 Similarly, a recent quasi-experimental quality improvement study in a tertiary ICU demonstrated that a structured, layered intervention approach-combining a guiding framework, standardized checklists, targeted education, and direct observation with performance feedback-was associated with a significant reduction in CLABSI rates from 12.43% to 3.52% (P<0.001),32 further supporting the effectiveness of systematic, feedback-driven improvement strategies across different high-risk clinical settings. This is consistent with the results of this study. It again indicates that the packaging pass rate of the equipment significantly increased after the intervention was implemented.

Furthermore, observational evidence from the tracking system indicates that packaging errors occur during routine operations. These issues include incorrect labels, incomplete packaging, and mismatched instrument specifications, etc.33 Our research findings show: After the implementation of management, the error rate of surgical instruments significantly decreased. The number of assembly errors dropped from 43 cases (3.98‱) to 15 cases (1.39‱), with an absolute reduction of −0.026 percentage points (95% CI: −0.041 to −0.011; P<0.001). Packaging integrity damage decreased markedly from 57 cases (5.28‱) to 6 cases (0.56‱), with an RD of −0.047 percentage points (95% CI: −0.062 to −0.033; P<0.001). Incorrect label information cases decreased from 15 cases (1.39‱) to 5 cases (0.46‱), with an RD of −0.009 percentage points (95% CI: −0.017 to −0.001; P=0.025). Insufficient cleaning and instrument damage also decreased, but there was no difference. The results of this study are consistent with this. After implementation, the packaging pass rate increased, and label, integrity damage, and assembly errors were significantly reduced. Our results are consistent with the previously adopted structured quality improvement method in aseptic processing.34 Recent improvement projects implemented in the aseptic processing department have also proved the reduction in the number of defect reports through standardized reporting, root cause analysis, and repeated revisions.35

Regarding CSSD packaging, the intervention measures based on HFMEA have been reported to reduce packaging defects by systematically identifying high-risk failure modes and implementing targeted controls.36 Compared with a single method, our closed-loop model was associated with a broader “record-analyze-correct-feedback-training/supervision” cycle throughout the processing chain, which perhaps explains why improvements were observed in multiple defect types rather than just in a single area. Importantly, contemporary observational work suggests that many sterile processing errors arise from visualization-dependent tasks, highlighting why consistent criteria, staff training, and verification processes are critical. This framing also supports the plausibility of our improvements in assembly errors and labeling accuracy, which are highly dependent on attention, cognitive load, and standard work. Beyond traditional quality metrics, emerging digital tools such as automated surveillance systems and artificial intelligence may offer new opportunities to enhance infection prevention and control in CSSD settings, though the evidence base remains fragmented and implementation gaps persist.37

In addition, the intervention included enhanced supervision and high-frequency spot checks, which may have introduced surveillance bias and Hawthorne effects.38 To mitigate this, we maintained a fixed sampling protocol for cleaning and packaging inspections across both periods (approximately 175 devices per working day, approximately 21 packages, 6 days per week, for a total of 26 weeks), ensuring that detection intensity for these primary outcomes was unchanged. However, for operating room–reported error events, which relied on passive reporting rather than active surveillance, increased staff awareness and reporting culture during the post-intervention period could theoretically have led to higher reporting rates, which would bias results toward the null (ie, underestimating true improvements) rather than overestimating them. Thus, while we cannot fully exclude these effects, their net impact would likely attenuate rather than amplify the observed improvements. Nevertheless, future studies should incorporate run-in periods, fidelity measures, and sustained follow-up to better distinguish intervention effects from surveillance-related artifacts. While PDCA-based quality cycles are commonly applied in CSSD settings, our contribution lies in the systematic operationalization of a comprehensive “record–analyze–correct–feedback–train/supervise” model with defined team roles, standardized defect categorization, and quantifiable outcome tracking across multiple defect types. The novelty is not the closed-loop concept per se, but the evaluation of this integrated, reproducible model with empirical evidence of its effectiveness.

Several limitations should be considered. First, the single-center before–after design precludes causal inference, as temporal trends, seasonal variations, or unmeasured concurrent initiatives may have contributed to the observed improvements. Second, although we maintained consistent staffing and equipment across both periods, the intensified supervision and feedback inherent to the intervention may have introduced Hawthorne effects, potentially inflating performance during the post-intervention phase. Third, outcomes were derived from routine operational records rather than research-dedicated data collection, which may affect data completeness and precision. Fourth, the fixed sampling protocol, though consistent across periods, was based on routine audits rather than random sampling, and individual items may have been sampled multiple times, potentially introducing clustering effects. Finally, the findings are from a single tertiary hospital and may not be generalizable to smaller or resource-limited settings. Future multicenter studies with controlled designs and longer follow-up are needed to confirm the durability and generalizability of these improvements.

Conclusion

In this single-center quasi-experimental before-after study, implementation of a closed-loop defect management improvement model in a tertiary-hospital CSSD was associated with significant improvements in surgical instrument reprocessing quality. After a prespecified run-in period, cleaning performance improved, reflected by a higher qualified rate of regular sampling inspection for cleaning quality and a lower cleaning nonconformity rate. Packaging management also showed broad gains, with increased packaging pass rates and reduced labeling information errors, package integrity damage, and assembly errors. Collectively, these results suggest that a standardized “record-analyze-correct-feedback-train/supervise” closed-loop approach may be a pragmatic and scalable strategy to strengthen process reliability and reduce recurrent defects in CSSD workflows.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research received no external funding.

Disclosure

The authors report no conflicts of interest in this work.

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