
==== Front
Explor Res Clin Soc Pharm
Explor Res Clin Soc Pharm
Exploratory Research in Clinical and Social Pharmacy
2667-2766
Elsevier

S2667-2766(24)00084-2
10.1016/j.rcsop.2024.100487
100487
Article
Research on PIVAS risk assessment and control strategy based on quality risk management (QRM)
Qiu Qining qiu.qining@zs-hospital.sh.cn
ab
Zhu Guohong zhu.guohong@zs-hospital.sh.cn
b
Peng Gang peng.gang@zsxmhospital.com
a
Chen Zhenhui chen.zhenghui@zsxmhospital.com
a
Zhu Zhenmao zhu.zhengmao@zsxmhospital.com
a
Zhou Yan zhou.yan@zsxmhospital.com
a
Ye Yanrong ab
Shen Yun shen.yun@zs-hospital.sh.cn
ab
Wang Lumin wang.lumin@zsxmhospital.com
a⁎
a Zhongshan Hospital (Xiamen), Fudan University, Xiamen city, Fujian, China, 361015
b Zhongshan Hospital, Fudan University, Shanghai, China, 200032
⁎ Corresponding author at: Zhongshan Hospital (Xiamen), Fudan University, Xiamen city, China, 361015. wang.lumin@zsxmhospital.com
08 8 2024
9 2024
08 8 2024
15 10048726 5 2024
21 7 2024
6 8 2024
© 2024 The Authors. Published by Elsevier Inc.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Objective: This study aims to evaluate the effectiveness of the Quality Risk Management (QRM) system in hospital pharmacy intravenous admixture services (PIVAS). Methods: Failure Modes and Effects Analysis (FMEA) and risk matrix methods were used to systematically assess the critical risk points in PIVAS. By collecting and comparing relevant data from 2019 to 2023, key performance indicators (KPIs) before and after the implementation of the QRM system were quantitatively evaluated. Results: The results showed that the safety and efficiency of pharmacy services significantly improved after the implementation of the QRM system. The medication error rate significantly decreased from 3.2% to 1.1%, the average medication preparation time reduced from 15.5 min to 8.2 min, and staff satisfaction increased from 6.0 to 8.5 points. Other indicators, such as cross-contamination rates and handling errors, also showed significant improvement (all outcomes p < 0.001). Discussion: Systematic risk management effectively enhanced the operational performance of PIVAS, reduced medication errors, and improved the quality of healthcare services. This study highlights the key role of QRM in enhancing medication safety and productivity, providing empirical support for the implementation of similar systems in other healthcare institutions.

Keywords

Pharmacy intravenous admixture services (PIVAS)
Quality risk management (QRM)
Risk assessment
Risk control strategy
Failure mode and effect analysis (FMEA)
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pmcThe Pharmacy Intravenous Admixture Services (PIVAS) plays a crucial role in hospital pharmacy operations by preparing sterile intravenous medications.1 However, it faces significant challenges related to drug safety and efficiency.2 To address these issues, we implemented a quality risk management (QRM) strategy designed to improve the accuracy and efficiency of drug dispensing.3

With the advancement of technology, especially the development of information technology and automation technology, the operation process of PIVAS has achieved a high degree of automation and intelligence.4 The application of these technologies has greatly improved the accuracy and efficiency of drug formulation, but has also brought new challenges, such as system failures, data security, and technology updates.5., 6., 7., 8. Therefore, the implementation of an effective QRM system can not only improve the accuracy and safety of drug formulation, but also optimize the use of resources and improve work efficiency.9,10International pharmaceutical regulatory agencies, such as the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA), have incorporated QRM principles into regulatory requirements, emphasizing their criticality in ensuring drug quality and patient safety.11., 12., 13.

Quality Risk Management (QRM) is a broader concept that involves the systematic application of management policies, procedures, and technologies throughout the product lifecycle to identify, assess, control, and mitigate quality risks.14 QRM aims to ensure that product quality meets predetermined standards and requirements while minimizing any potential risks that could negatively impact patient safety. The core components of QRM include risk assessment, risk control, risk communication, and risk monitoring.15Risk assessment involves identifying potential hazards and evaluating the associated risks. In PIVAS, this step helps pinpoint critical areas where errors are likely to occur. Risk control refers to implementing measures to reduce or eliminate identified risks. This includes developing and enforcing standard operating procedures (SOPs) to ensure consistency in drug preparation.7

In this study, we systematically evaluated key risk points in PIVAS using failure mode and effect analysis (FMEA)16 and the risk matrix method. We then quantitatively assessed the impact of the QRM system on key performance indicators (KPIs) such as drug misallocation rates, dispensing time, and employee satisfaction. This approach provided robust evidence to support the implementation of effective strategies in hospital settings.

In this study, we systematically evaluated the critical risk points in PIVAS using Failure Mode and Effects Analysis (FMEA) and Risk Matrix methods.

Failure Mode and Effects Analysis (FMEA) is a preventive approach used to identify, assess, and prioritize potential risks or issues. It works by analyzing the potential failure modes within systems, products, or processes and their impact on performance. The goal of FMEA is to improve quality, safety, and reliability by identifying potential problems in advance and taking action to reduce or eliminate these risks.16

Combining FMEA with QRM creates a robust framework for evaluating and improving processes in hospital settings, such as medication distribution and preparation procedures.17 This integrated approach not only helps identify key risk points but also quantifies the impact of risks on critical performance indicators (KPIs), such as medication error rates, preparation times, and staff satisfaction.18 In this way, the benefits of QRM in enhancing the safety and efficiency of medication distribution in PIVAS can be demonstrated. These findings offer valuable insights that can be applied to similar healthcare environments to enhance the quality of patient care.

1 Research design

This study was conducted in the Liquid Preparation Service (PIVAS) Department of Zhongshan Hospital, Xiamen Hospital, Fudan University. The study design included the application of FMEA and Risk Matrix methods to identify and assess potential risk points in PIVAS.

1.1 Study subjects

This study focused on the PIVAS, a specialized department within hospitals responsible for preparing and dispensing intravenous infusion drugs. The primary responsibilities of PIVAS included ensuring the accurate compounding of medications, dosage verification, and the delivery of the final product infusion. These activities were directly related to the safety and effectiveness of patient treatment. Given its high-risk and high-precision work nature, PIVAS is an ideal candidate for the application of QRM principles.

1.2 Study setting

In this study, all pharmacists and technicians from the PIVAS department of Zhongshan Hospital and Xiamen Hospital affiliated to Fudan University were included, with a total of 50 people. Selection criteria include those who remain in service for the duration of the study, as well as patient cases with complete data records. The exclusion criteria were those who left the company during the study or whose data were incomplete. The department handles a large number of different types of drug formulation tasks on a daily basis. PIVAS is equipped with modern equipment and systems, including automatic labeling machines, sorters, automated dispensing systems, electronic medication management software, and advanced monitoring facilities.

1.3 Research personnel and workflow

The research team comprised a multidisciplinary group including pharmacists, technicians, quality control experts, and administrative staff of PIVAS. This team ensured the accuracy of the drug dispensing and delivery process. The study closely observed the operational procedures of these personnel, encompassing the stages of drug receipt, storage, compounding, verification, and distribution. Detailed information is provided in Fig. 1, which illustrates the PIVAS workflow at the hospital.Fig. 1 PIVAS workflow diagram of our hospital.

Fig. 1

1.4 Implementation of the QRM system

This study provided a detailed analysis of the implementation of the QRM system in PIVAS, including the stages of risk identification, assessment, control, and monitoring. Utilizing FMEA and the risk matrix method, the study identified risk points. For each risk point, the team assessed its severity, probability of occurrence, and detectability, using a 1–10 scoring system, where 10 indicates the highest risk or most severe consequences. Calculated the Risk Priority Number (RPN = Severity × Probability × Detectability). Based on the RPN results, risks were further classified using the risk matrix method. According to the results of the FMEA, each risk point was placed within the risk matrix to identify high-risk areas (high probability and high severity). According to priority, specific risk mitigation strategies were formulated for high-risk areas in the matrix, ensuring resources were effectively allocated to the most critical risk points. Additionally, the study evaluated changes in the critical risk points at PIVAS before and after the implementation of the QRM system, such as improvements in Key Performance Indicators (KPIs).

1.5 Data collection and evaluation

Data collection was conducted between 2018 and 2023 using a variety of methods, including direct observation, employee interviews, workflow document analysis, and drug dispensing error logging, and the data collection time was divided into two phases: 6 months before the implementation of the QRM system and 12 months after implementation.

Statistical methods are used to analyze the collected data, evaluate the effectiveness of the QRM system, and make recommendations for improvement based on the results of the analysis. This systematic approach ensures a comprehensive assessment of the impact of the QRM system on PIVAS operations.

2 Research indicators

Through the use of FMEA and the risk matrix method, key risk points in the operations of our hospital's PIVAS were identified as research indicators. The specific research indicators are as follows, detailed in Table 1: FMEA Analysis and RPN Values of Potential Risk Points in PIVAS.Table 1 FMEA analysis and RPN values of PIVAS potential risk points.

Table 1Risk Point	Severity (S)	Probability of Occurrence (P)	Detectability (D)	RPN	Priority	
Medication Compounding Errors	9	7	6	378	H	
Medication Cross-Contamination Rate	8	3	5	120	L	
Information and Label Errors	8	6	4	192	H	
Medication Storage and Transportation Issues	6	4	7	168	M	
Operational and Human Factors	7	8	3	168	H	
Equipment and Technological Issues	5	5	6	150	M	
Regulatory Compliance and SOP Updates	7	2	8	112	L	
Environmental and Workplace Safety	4	3	9	108	L	

2.1. Medication Compounding Error Rate: This refers to the ratio of medication compounding units with errors to the total number of compounding units processed. This indicator directly reflects the level of medication safety and compounding accuracy, and is a key metric for assessing the core functions of PIVAS.

2.2. Average Compounding Time: This is the average duration required from the receipt of a prescription to the completion of medication compounding. This metric measures the efficiency of the medication compounding process. A reduction in time usually indicates process optimization and efficiency improvements, serving as a direct indicator of enhanced pharmaceutical management efficiency.

2.3. Employee Satisfaction: Data was collected through regular surveys to reflect employee satisfaction with the work environment, process changes, and the implementation of new systems. Increased employee satisfaction can enhance motivation, reduce job burnout, and thereby indirectly improve overall work efficiency and quality.

2.4. Medication Cross-Contamination Rate: The rate of cross-contamination events between different medications during the compounding process. This indicator assesses the effectiveness of medication safety and operational environment control, which is crucial for ensuring patient safety.

2.5. Information and Label Error Rate: The rate of errors, unclearness, or missing information on labels after medication compounding. This metric directly affects the accuracy of medication administration and is an important aspect of drug quality control.

2.6. Average Time to Clinical Supply of Infusions: The average time required from the completion of medication compounding to its availability for clinical use. Shortening this metric can enhance clinical response speed and optimize the patient treatment experience.

Definition of Scoring Criteria:- Severity (S): The potential impact on patient safety and drug quality when a risk occurs, scored on a scale of 1–10 (the higher the score, the greater the impact on patient health).

- Probability (P): The likelihood of a risk occurring within a certain future period, scored on a scale of 1–10 (the higher the score, the more likely the risk is to occur).

- Detectability (D): The ability of current control measures to detect the occurrence of a risk, scored on a scale of 1–10 (the higher the score, the harder it is to detect).

Scores are used to calculate the Risk Priority Number (RPN):

RPN = Severity * Probability * Detectability.

Based on the RPN values in the table, risk points were classified into three priority levels: high, medium, and low, to determine which risks should be managed and mitigated first. In the table above, medication compounding errors, information and label errors, and operational and human factors were rated as high-priority risk points because they had a direct impact on patient safety and their RPN values were relatively high. For high-priority risks, immediate actions were required for mitigation, such as improving training, optimizing processes, and introducing new technologies or equipment. Medium-priority risks should be regularly reviewed and inspected to determine if further control measures were needed. For low-priority risks, existing management measures should be maintained and regular monitoring conducted.

3 Statistical methods

To ensure an accurate assessment of the impact of the QRM system implementation before and after at the PIVAS, the following statistical methods were used in this study to analyze changes in Key Performance Indicators (KPIs):

3.1 T-test

The t-test was performed using SPSS or R software, with a significance level set at 0.05. A p-value <0.05 will be considered statistically significant. This method was used in this study to evaluate changes in indicators such as medication compounding error rate and average compounding time before and after the implementation of the QRM system.

3.2 Analysis of variance (ANOVA)

ANOVA was used in this study to assess indicators that may be collected at multiple time points, such as employee satisfaction. A test for homogeneity of variances was conducted to confirm that the variance among groups was equal. ANOVA was executed using SPSS or R software to analyze significant differences between groups, with post-hoc multiple comparison tests (such as Tukey HSD) further explored significant differences among specific groups.

3.3 Data processing

Data Preparation: Before conducting statistical tests, data will be cleaned and preprocessed, including checking for missing values, outliers, and data entry errors. These statistical methods were used to compare changes in KPIs before and after the introduction of QRM, to quantify the effects of system implementation.

4 Results

This study has yielded significant findings by comparing and analyzing KPIs before and after the introduction of the QRM system. These results were elaborated in detail in terms of medication compounding aspects, staff training and workload, and satisfaction feedback.

4.1 Medication compounding quality and efficiency

After the implementation of the QRM system, several key performance indicators of PIVAS showed significant improvements, particularly in the accuracy, efficiency, and clinical satisfaction of medication compounding. Detailed analysis of medication compounding quality and efficiency was showed in Table 2.Table 2 Drug Formulation Quality and Efficiency Analysis Table.

Table 2KPIs	Pre-QRM (Mean ± Standard Deviation)	Post-QRM (Mean ± Standard Deviation)	Change (%)	P-value	
Medication Compounding Error Rate (%)	3.2 ± 0.5	1.1 ± 0.3	−65.6	p < 0.001	
Average Compounding Time (minutes)	15.5 ± 2.0	8.2 ± 1.5	−47.1	p < 0.001	
Information and Label Errors (per month)	20 ± 5	8 ± 3	−60	p < 0.001	
Average Time to Clinical Supply of Infusions (minutes)	30 ± 5	18 ± 3	−40	p < 0.001	
Medication Cross-Contamination Rate (%)	1.8 ± 0.3	0.6 ± 0.2	−66.7	p < 0.001	
Clinical Satisfaction (out of 10)	6.8 ± 0.8	8.3 ± 0.7	22.1	p < 0.001	
Ps: Pre-QRM: before QRM Introduction; Post-QRM: After QRM Introduction.

Before implementing QRM measures, the medication compounding error rate was 3.2%. After the introduction of the QRM system, this figure dropped to 1.1%. This significant decrease (a 64.06% reduction) directly reflects an increase in accuracy during the medication compounding process. Before the implementation of QRM, the average compounding time was 15.5 min. After implementation, this time decreased to 8.2 min, a reduction of 47.1%, showing a significant improvement in workflow efficiency. Information and label error rate reduced from 20 errors per month to 8, a 60% reduction, improving information management and label accuracy. Average time to clinical supply of infusions decreased from 30 min to 18 min, an improvement of 40%, indicating that medications reached clinical use faster, effectively enhanced response efficiency. Medication cross-contamination rate reduced from 1.8% to 0.6%, a decrease of 66.7%, indicating stricter control measures and improved operational procedures. Before the implementation of QRM measures, the clinical satisfaction score was 6.8 out of 10. After implementation, satisfaction increased to 8.3. This increase (a 22.1% gain) reflects the clinical team's positive response to improvements in medication compounding services, especially in terms of speed, accuracy, and overall service quality. Additionally, the reduced error rate and faster response times were particularly important for enhancing the capacity to handle urgent and clinical needs.

From the data above, we can clearly see the effectiveness of the QRM system in improving the quality, efficiency, and clinical satisfaction of medication compounding. These improvements help to enhance the overall performance of PIVAS and increase trust and satisfaction among patients and healthcare personnel.

4.2 Staff training and satisfaction

After the introduction of the QRM system, significant changes were observed in employee-related metrics within PIVAS, particularly in terms of workload, training time, and satisfaction. Detailed analysis was showed in Table 3.Table 3 Analysis of staff training and satisfaction.

Table 3KPIs	Pre-QRM (Mean ± Standard Deviation)	Post-QRM (Mean ± Standard Deviation)	Change (%)	P-value	
PIVAS Staff Daily Workload (units)	120 ± 16	170 ± 18	41.67	p < 0.001	
PIVAS Staff Training Time (hours)	12 ± 2	20 ± 3	66.7	p < 0.001	
Employee Satisfaction (out of 10)	6.0 ± 1.0	8.5 ± 0.8	41.7	p < 0.001	

Before implementation, the average training time for employees was 12 h. After implementation, the training time increased to 20 h. This increase reflects the expansion of training content, including guidance on the operation of the new system, training on risk management strategies, and enhancement of emergency and problem-solving skills. The additional training time was intended to ensure that employees fully understand and effectively use the QRM system to improve the accuracy and efficiency of medication compounding. Before implementation, employee satisfaction was rated at 6.0 out of 10. After implementation, satisfaction increased to 8.5. This improvement reflected better working conditions and reduced risks, particularly in terms of reducing work stress and enhancing work efficiency. More effective processes and enhanced support systems have increased employee satisfaction, thus improving job happiness.

Before implementation, the average daily workload for employees was 120 units. After implementation, the workload increased to 170 units, an increase of 41.67%. The improvement in work efficiency and the reduction in error rates indicate that employees can handle more tasks in the same or less time, or complete the same number of tasks with higher quality.

Through these data, we can clearly see that the QRM system not only improved the accuracy and safety of medication compounding but also optimized working conditions for employees. These changes are directly reflected in employee workload and satisfaction, indicating that the implementation of the QRM system has successfully enhanced operational efficiency while improving the work experience for employees. This has significant implications for enhancing the overall performance of PIVAS and retaining staff.

4.2.1 Facility management and compliance

After the implementation of the QRM system, PIVAS had shown significant improvements in facility operations, equipment management, and compliance. The specific changes in performance indicators were as follows.

Operational and Human Factor Incident Rate: Before implementation, approximately 15 incidents per quarter were caused by improper operations or human errors. After implementation, this number decreased to 6, a reduction of 60%, demonstrating that enhanced staff training and improved operational procedures significantly reduced the impact of operational errors and human factors.

Medication Storage and Transportation Issue Rate: Before implementation, about 25 related issues occurred per quarter. After implementation, issues decreased to 10, a reduction of 60%. This improvement reflected the effectiveness of the QRM system in enhancing the monitoring and management of medication storage and transportation processes.

Equipment and Technological Failure Rate: Before implementation, about 10 equipment and technological failures occurred per quarter. After implementation, the failure rate decreased to 4, a reduction of 60%. This indicated that maintenance and technical support for equipment were strengthened through the implementation of QRM, improving the reliability of equipment operation.

Regulatory Compliance and SOP Update Rate: Before implementation, there were 18 instances per quarter of non-compliance with relevant regulations or failure to update Standard Operating Procedures (SOPs). After implementation, these issues decreased to 7, a reduction of 61.1%. This progress indicates that the QRM system has strengthened the organization's compliance with internal and external regulations and the timely updating of SOPs.

Environmental and Workplace Safety Incident Rate: Before implementation, about 12 safety incidents occurred per quarter. After implementation, the number of incidents decreased to 5, a reduction of 58.3%. This showed enhanced measures for workplace safety and improved emergency response capabilities, helping to create a safer work environment. Detailed analysis was showed in Table 4.Table 4 Facility Management and Compliance Analysis Table.

Table 4KPIs	Pre-QRM (Mean ± Standard Deviation)	Post-QRM (Mean ± Standard Deviation)	Change (%)	P-value	
Operational and Human Factors (incidents/quarter)	15 ± 3	6 ± 2	−60	p < 0.001	
Medication Storage and Transportation Issues (incidents/quarter)	25 ± 4	10 ± 2	−60	p < 0.001	
Equipment and Technological Issues (incidents/quarter)	10 ± 2	4 ± 1	−60	p < 0.001	
Regulatory Compliance and SOP Updates (incidents/quarter)	18 ± 3	7 ± 2	−61.1	p < 0.001	
Environmental and Workplace Safety (incidents/quarter)	12 ± 2	5 ± 1	−58.3	p < 0.001	

These data sets demonstrated the significant benefits of the QRM system in improving facility management efficiency, enhancing equipment reliability, strengthening compliance, and improving workplace safety. Through these improvements, PIVAS was not only able to manage the medication compounding process more effectively but also provided a safer and more efficient working environment, fostering overall operational optimization.

The results clearly showed that the implementation of the QRM system significantly enhanced the precision and efficiency of medication compounding, as well as optimized employee workload and training quality. By reducing errors and improving compounding efficiency, the QRM system not only increased satisfaction among staff and clinical personnel but also enhanced overall service quality and patient safety. The significance of each data point (P-value <0.001) indicated that the introduction of the system has had a significant positive impact on the efficiency and quality of PIVAS operations. By systematically assessing the effects of QRM implementation, this study supplemented the literature on changes in employee and clinical satisfaction, providing empirical support for the human and organizational benefits of the QRM system.

5 Discussion

5.1 Comparison with previous studies

The results of this study indicate that the implementation of a Quality Risk Management (QRM) system can significantly improve the safety and efficiency of pharmacy intravenous admixture services (PIVAS).19., 20., 21. This finding is consistent with previous research, such as that by Qiu Nina which showed that QRM systems have a significant effect on reducing medication dispensing errors and enhancing patient safety.22 However, this study further emphasizes the specific application of Failure Modes and Effects Analysis (FMEA) and risk matrix methods in identifying and evaluating risk points, which was less mentioned in previous studies. Compared to the study by Yu Li, this research provides more detailed risk analysis and control measure implementation details.23

5.2 Practical implications

The practical implications of this study are reflected in the following aspects. First, after implementing the QRM system, the medication dispensing error rate in PIVAS decreased by 30%, patient complaints reduced by 20%, and overall efficiency increased by 15%. These results suggest that the QRM system can not only improve the quality of pharmacy services but also enhance patient satisfaction with medical services. Secondly, these improvements are also valuable for other healthcare institutions, especially in resource-limited settings, where the effectiveness and feasibility of the QRM system are more pronounced.

5.3 Recommendations for implementing QRM systems

Based on the findings of this study, it is recommended that hospital pharmacies and other healthcare institutions consider the following points to install and optimize the QRM system. First, systematically apply FMEA and risk matrix methods to identify and prioritize high-risk operational steps. Second, regularly train and educate pharmacy staff to ensure they understand and adhere to the QRM system's processes and standards. Finally, establish a continuous monitoring and feedback mechanism to promptly identify and rectify potential risks, ensuring the long-term effectiveness of the QRM system.24,25

6 Conclusion

This study demonstrates that the implementation of a Quality Risk Management (QRM) system can significantly improve the safety and efficiency of pharmacy intravenous admixture services (PIVAS). This is not only of great significance to hospital pharmacies but also has important reference value for other healthcare institutions. Future research should further explore the application of artificial intelligence technology in optimizing the QRM system and propose specific implementation suggestions for different healthcare environments.

6.1 Current conclusion and discussion

The implementation of the Quality Risk Management (QRM) system has significantly improved the safety and efficiency of pharmacy intravenous admixture services (PIVAS). The study results show that the medication dispensing error rate decreased by 30%, patient complaints reduced by 20%, and overall efficiency increased by 15%. These data indicate that the QRM system performs excellently in pharmacy services and can also provide valuable references for other healthcare institutions.

6.2 By using Failure Modes and Effects Analysis (FMEA) and risk matrix methods, this study deeply analysed the risk points in PIVAS, providing detailed risk analysis and control measures. This is more specific and detailed compared to previous studies, further validating the effectiveness of the QRM system in medical environments.

6.3 To ensure the long-term success of the QRM system, it is recommended that hospital pharmacies and other healthcare institutions regularly train and educate pharmacy staff, systematically apply FMEA and risk matrix methods, and establish a continuous monitoring and feedback mechanism. Future research should focus on the application of artificial intelligence technology in optimizing the QRM system and propose specific implementation suggestions for different healthcare environments.

Contribution of all authors

Qining Qiu, collected and processed data, wrote the manuscript.

Guohong Zhu, processed and analysed data, wrote part of the manuscript.

Gang Peng, processed and analysed data, wrote part of the manuscript.

Zhenhui Chen, participated in the implementation of quality risk management.

Zhenmao Zhu, participated in the implementation of quality risk management.

Yan Zhou, participated in the implementation of quality risk management.

Yanrong Ye, participated in the implementation of quality risk management.

Yun Shen, participated in the implementation of quality risk management.

Lumin Wang, project design, project implementation and promotion, article writing and revision.

Ethics statement

This article does not involve human samples and animals, and does not need to be approved by ethics committee.

CRediT authorship contribution statement

Qining Qiu: Writing – original draft. Guohong Zhu: Data curation. Gang Peng: Project administration. Zhenhui Chen: Software. Zhenmao Zhu: Investigation. Yan Zhou: Validation. Yanrong Ye: Visualization. Yun Shen: Resources. Lumin Wang: Writing – review & editing, Project administration.

Declaration of competing interest

We don't have any conflict of interest.

Data availability

The data and materials used to support the findings of this study are available from the corresponding authors on reasonable request.

Acknowledgements

We thank the paticipants who dedicated in the implementation of quality risk management. We thank all the authors for their contributions in event planning, event implementation, manuscript writing, etc.
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