基于失效模式与效应分析我院PIVAS开展非整支用量药品调配全流程风险管理实践

doi:10.5246/jcps.2024.07.044

中国药学(英文版) ›› 2024, Vol. 33 ›› Issue (7): 597-608.DOI: 10.5246/jcps.2024.07.044

基于失效模式与效应分析我院PIVAS开展非整支用量药品调配全流程风险管理实践

耿魁魁, 何娟, 戎生, 贾兆虎, 张香香, 史天陆^*()

中国科学技术大学附属第一医院 (安徽省立医院) 药学部; 精准药物制剂与临床药学安徽省重点实验室, 安徽合肥 230001

收稿日期:2023-09-11 修回日期:2023-10-23 接受日期:2023-11-29 出版日期:2024-07-30 发布日期:2024-07-30
通讯作者: 史天陆

Failure mode and effect analysis of the risk management of non-integral-dosage drug dispensing in PIVAS

Kuikui Geng, Juan He, Sheng Rong, Zhaohu Jia, Xiangxiang Zhang, Tianlu Shi^*()

Department of Pharmacy, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China; Anhui Provincial Key Laboratory of Precision Pharmaceutical Preparations and Clinical Pharmacy, Hefei 230001, Anhui, China

Received:2023-09-11 Revised:2023-10-23 Accepted:2023-11-29 Online:2024-07-30 Published:2024-07-30
Contact: Tianlu Shi
Supported by:
Anhui Provincial Health Research Project Fund (Grant No. AHWJ2023-BAc20143); Pharmaceutical Research Exploration Fund of the First Affiliated Hospital of University of Science and Technology of China (Grant No. YJKJJ04); 14th Five Year Plan Anhui Province Medical and Health Clinical Key Specialty Construction Project Support.

摘要/Abstract

摘要：

本研究旨在排除非整支用量药品在PIVAS审方、打签、摆药、核对、调配、成品复核等工作环节全流程存在的风险。通过组建项目团队, 使用失效模式与效应分析的管理方法, 梳理非整支用量药品在PIVAS药品管理、审方打签、混合调配、核对4个工作环节存在的风险点, 针对每个风险点进行严重度(S)、发生率(O)和可侦测度(D)评分, 并计算风险点的风险优先值(RPN), 对RPN排名前列的风险点制订相应的改进措施, 对比措施实施前后RPN值变化情况, 观察研究效果。研究梳理了非整支药品全流程存在的风险共31个, 主要风险存在于混合调配环节; 针对RPN值较高的8项风险制订相应的改进措施, 经过3个月的优化改进, RPN值和内差发生率明显降低, 改进措施起到了很好的风险控制效果。研究建立了全方位的非整支药品调配剂量换算系统, 可按照医嘱剂量直接换算成调配人员抽吸液体体积量, 避免人工二次计算, 使PIVAS非整支药品调配同质化、标准化、剂量计算的自动化; 同时开展了23种非整支用量药品的溶解试验, 发现11种药品品种溶解后溶剂体积增加, 非整支药品的剂量换算应按照终溶液的体积进行计算才能保证剂量的准确。研究基于失效模式与效应分析在PIVAS开展非整支用量药品全流程风险管理实践, 解决了非整支药品调配过程中存在的安全风险, 最大限度避免非整支用量药品调配中发生差错, 保证患者安全、精准用药。

关键词: 静脉用药调配中心, 失效模式与效应分析, 非整支使用药品, 风险管理, 调配

Abstract:

To mitigate risks associated with the prescription examination, marking, dispensing, checking, and review of non-integral-dosage drugs in Pharmacy Intravenous Admixture Service (PIVAS), we formed a project team. Employing the failure mode and effect analysis (FMEA) management method, we identified potential risks in four critical steps of the non-integral-dosage drug dispensing process within PIVAS drug management: prescription verification, mixed allocation, and verification. For each step, we assigned scores for severity, incidence, and detectability, subsequently calculating the Risk Priority Number (RPN) to prioritize identified risks. Targeted measures for improvement were developed for steps with the highest RPN values. A total of 31 risk factors were documented in the management of non-integral-dosage drugs, with the dispensing process being particularly vulnerable. Specific measures were devised for eight high RPN risks. Following a 3-month optimization and improvement period, RPN values and incidences of internal differences were significantly reduced. The implemented measures demonstrated effective risk control. Notably, we established a comprehensive conversion system for partial-dose drug dispensing, directly translating into a volume of suction fluid for dispensing personnel based on doctor orders. This eliminated the need for manual secondary calculations, thereby standardizing and automating the dispensing of non-integral-dosage drugs in PIVAS. Simultaneously, our project team conducted a dissolution test on 23 types of drugs with non-integral dosage, revealing that the solvent volume increased for 11 types after dissolution. The dosage conversion for partial dosage was recalibrated based on the volume of the final solution to ensure dosage accuracy. Through the application of failure mode and effect analysis, we systematically managed the risks associated with non-integral-dosage drugs in PIVAS. This approach addressed safety concerns in the dispensing process, reduced errors, and ensured the safe and precise administration of medication to patients.

Key words: PIVAS, FMEA, The non-integral-dosage drugs, Risk management, Dispensing

Supporting:

耿魁魁, 何娟, 戎生, 贾兆虎, 张香香, 史天陆. 基于失效模式与效应分析我院PIVAS开展非整支用量药品调配全流程风险管理实践[J]. 中国药学(英文版), 2024, 33(7): 597-608.

Kuikui Geng, Juan He, Sheng Rong, Zhaohu Jia, Xiangxiang Zhang, Tianlu Shi. Failure mode and effect analysis of the risk management of non-integral-dosage drug dispensing in PIVAS[J]. Journal of Chinese Pharmaceutical Sciences, 2024, 33(7): 597-608.

图/表 8

Table 1. Statistical table of project team members.

Figure 1. Distribution of risk points in PIVAS with partial dosage.

Table 2. Scoring standards of risk points S, O, and D of non-whole dose drug allocation.

Figure 2. Distribution of S, O, D, and RPN values at 31 risk points of non-integral drugs.

Table 3. Statistical table of S, O, D, and RPN values of non-integral drug dispensing risk points before and after the implementation of measures.

Figure 3. Conversion results of non-whole dose drugs (Water injection form on the left, powder injection form on the right).

Table 4. Statistical table of solvent dissolution test results of 23 kinds of powder varieties for injection used in non-whole doses.

Figure 4. Schematic diagram of the drug dissolution test.

参考文献 25

[1]	Liu, H.; Zou, L.; Song, Y.J.; Yan, J.F. Cost analysis of implementing a vial-sharing strategy for chemotherapy drugs using intelligent dispensing robots in a tertiary Chinese hospital in Sichuan. Front Public Health. 2022, 10, 936686.
[2]	Yang, C.S.; Kang, B.Y.; Zhang, L.L.; Yu, D. Construction situation, costs and charges associated with pharmacy intravenous admixture services: multi-center cross-sectional survey based on 137 medical institutions in mainland China. BMC Health Serv. Res. 2020, 20, 577.
[3]	Wang, X.; Gu, M.; Gao, X.Q.; Xiong, X.; Wang, N.X.; Li, Q.Q.; Ge, M.M.; Luo, M.; Zhang, Y.; Hua, X.L.; Shi, C. Application of information-intelligence technologies in pharmacy intravenous admixture services in a Chinese third-class a hospital. BMC Health Serv. Res. 2022, 22, 1238.
[4]	Sisodiya, S.M. Precision medicine and therapies of the future. Epilepsia. 2020, 02, S90–S105.
[5]	Xiao, Y.Y.; Shen, J.A.; Zou, X.F. Mathematical modeling and dynamical analysis of anti-tumor drug dose-response. Math. Biosci. Eng. 2022, 19, 4120–4144.
[6]	Tu, Q.; Cotta, M.; Raman, S.; Graham, N.; Schlapbach, L.; Roberts, J.A. Individualized precision dosing approaches to optimize antimicrobial therapy in pediatric populations. Expert Rev. Clin. Pharmacol. 2021, 14, 1383–1399.
[7]	Tang, J.; Wang, K.; Yang, K.; Jiang, D.C.; Fang, X.H.; Su, S.; Lin, Y.; Chen, S.C.; Gu, H.Y.; Li, P.M.; Yan, S.Y. A combination of Beers and STOPP criteria better detects potentially inappropriate medications use among older hospitalized patients with chronic diseases and polypharmacy: a multicenter cross-sectional study. BMC Geriatr. 2023, 23, 44.
[8]	Wang, S.Y.; Hung, Y.L.; Hsu, C.C.; Hu, C.H.; Huang, R.Y.; Sung, C.M.; Li, Y.R.; Kou, H.W.; Chen, M.Y.; Chang, S.C.; Lee, C.W.; Tsai, C.Y.; Liu, K.H.; Hsu, J.T.; Yeh, C.N.; Yeh, T.S.; Hwang, T.L.; Jan, Y.Y.; Chen, M.F. Optimal perioperative nutrition therapy for patients undergoing pancreaticoduodenectomy: a systematic review with a component network meta-analysis. Nutrients. 2021, 13, 4049.
[9]	Zhang, J.; Zhang, Y.K.; Song, Y.Q. Analysis of the influence of non-whole medication administration through intravenous infusion on patient costs. Chin. Hosp. Pharm. J. 2017, 37, 2085–2088.
[10]	Anjalee, J.A.L.; Rutter, V.; Samaranayake, N.R. Application of Failure Mode and Effect Analysis (FMEA) to improve medication safety: a systematic review. Postgrad. Med. J. 2021, 97, 168–174.
[11]	Chilakamarri, P.; Finn, E.B.; Sather, J.; Sheth, K.N.; Matouk, C.; Parwani, V.; Ulrich, A.; Davis, M.; Pham, L.; Chaudhry, S.I.; Venkatesh, A.K. Failure mode and effect analysis: engineering safer neurocritical care transitions. Neurocrit. Care. 2021, 35, 232–240.
[12]	Liu, H.C.; Zhang, L.J.; Ping, Y.J.; Wang, L. Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review. J. Eval. Clin. Pract. 2020, 26, 1320–1337.
[13]	Rusu, I.; Thomas, T.O.; Roeske, J.C.; Mescioglu, I.; Melian, E.; Surucu, M. Failure mode and effects analysis of linac-based liver stereotactic body radiotherapy. Med. Phys. 2020, 47, 937–947.
[14]	Tiao, C.H.; Tsai, L.C.; Chen, L.C.; Liao, Y.M.; Sun, L.C. Healthcare Failure Mode and Effect Analysis (HFMEA) as an Effective Mechanism in Preventing Infection Caused by Accompanying Caregivers During COVID-19-Experience of a City Medical Center in Taiwan. Qual. Manag. Health Care. 2021, 30, 61–68.
[15]	da Costa Furtado Abi, A.X.; de Almeida Cruz, E.D.; Pontes, L.; dos Santos, T.; Felix, J.V.C. The Healthcare Failure Mode and Effect Analysis as a tool to evaluate care protocols. Rev. Bras. Enferm. 2022, 75, e20210153.
[16]	Haroun, A.; AL- Ruzzieh, M.; Hussien, N.; Masa’ad, A.; Hassoneh, R.; Abu Alrub, G.; Ayaad, O. Using failure mode and effects analysis in improving nursing blood sampling at an international specialized cancer center. Asian Pac. J. Cancer Prev. 2021, 22, 1247–1254.
[17]	Caballero-Romero, Á.; Fernández, S.; Morillo, A.B.; Zaragoza-Rascón, M.; Jaramillo-Pérez, C.; Del Pozo-Rubio, R. Healthcare failure mode and effects analysis and cost‑minimization analysis of three pharmaceutical services. Análisis modal de fallos y efectos y análisis de minimización de costes de tres programas de entrega de medicamentos. Farm Hosp. 2021, 45, 66–72.
[18]	Jafarzadeh Ghoushchi, S.; Dorosti, S.; Ab Rahman, M.N.; Khakifirooz, M.; Fathi, M. Theory-based failure modes and effect analysis for medication errors. J. Healthc. Eng. 2021, 2021, 1–14.
[19]	Sova, P.M.; Holmström, A.R.; Airaksinen, M.; Sneck, S. Using Healthcare Failure Mode and Effect Analysis in prospective medication safety risk management in secondary care inpatient wards. Eur. J. Hosp. Pharm. 2024, 31, 227–233.
[20]	Pueyo-López, C.; Sánchez-Cuervo, M.; Vélez-Díaz-Pallarés, M.; Ortega-Hernández-Agero, T.; de Salazar-López de Silanes, E.G. Healthcare failure mode and effect analysis in the chemotherapy preparation process. J. Oncol. Pharm. Pract. 2020, 27, 1588–1595.
[21]	Abrami, M.; Grassi, M.; Masiello, D.; Pontrelli, G. Dissolution of irregularly-shaped drug particles: mathematical modelling. Eur. J. Pharm. Biopharm. 2022, 177, 199–210.
[22]	Jessurun, J.G.; Hunfeld, N.G.M.; van Rosmalen, J.; van Dijk, M.; van den Bemt, P.M.L.A. Effect of a pharmacy-based centralized intravenous admixture service on the prevalence of medication errors: a before-and-after study. J. Patient Saf. 2022, 18, e1181–e1188.
[23]	Ding, Z.H.; Liu, X.Y.; Zhang, J.; Zhang, Y.K. Application of non⁃whole drug conversion method in pediatric drug dispensing in intravenous drug dispensing center. Anhui Med. Pharm. J. 2020, 24, 607–609.
[24]	Li, H.Y.; Wang, H.; Bian, S.Q.; Lu, S.; Zhang, J.Z. Medical Failure Mode and Effect Analysis in Pediatric Intravenous Drug Admixture. J. Nursing (China). 2020, 27, 12–15.
[25]	Sharifi-Rad, J.; Quispe, C.; Patra, J.K.; Singh, Y.D.; Panda, M.K.; Das, G.; Adetunji, C.O.; Michael, O.S.; Sytar, O.; Polito, L.; Živković, J.; Cruz-Martins, N.; Klimek-Szczykutowicz, M.; Ekiert, H.; Choudhary, M.I.; Ayatollahi, S.A.; Tynybekov, B.; Kobarfard, F.; Muntean, A.C.; Grozea, I.; Daştan, S.D.; Butnariu, M.; Szopa, A.; Calina, D. Paclitaxel: application in modern oncology and nanomedicine-based cancer therapy. Oxid. Med. Cell Longev. 2021, 2021, 3687700.

基于失效模式与效应分析我院PIVAS开展非整支用量药品调配全流程风险管理实践

Failure mode and effect analysis of the risk management of non-integral-dosage drug dispensing in PIVAS

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 25

相关文章 2

编辑推荐

Metrics

本文评价

[1]	吴钰珊, 阳丽梅, 黄旭慧, 黄烽, 韩涛. CYP2C9*3和CYP4F2 rs2108622基因多态性对中国华法林治疗患者过度抗凝和出血并发症的影响: 一项队列研究[J]. 中国药学(英文版), 2021, 30(6): 484-494.
[2]	王宝敏, 周林光, 李斌, 杨松, 江滨. 基于风险管理的FDA药品安全监管发展历程及启示[J]. 中国药学(英文版), 2015, 24(6): 405-411.