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预测社区药店配药错误:系统人为失误减少和预测方法(SHERPA)的应用。

Predicting dispensing errors in community pharmacies: An application of the Systematic Human Error Reduction and Prediction Approach (SHERPA).

机构信息

Division of Pharmacy and Optometry, Centre for Pharmacoepidemiology and Drug Safety, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, United Kingdom.

NIHR Greater Manchester Patient Safety Translational Research Centre, Manchester Academic Health Science Centre, University of Manchester, Manchester, United Kingdom.

出版信息

PLoS One. 2022 Jan 4;17(1):e0261672. doi: 10.1371/journal.pone.0261672. eCollection 2022.

Abstract

INTRODUCTION

The objective of this study was to use a prospective error analysis method to examine the process of dispensing medication in community pharmacy settings and identify remedial solutions to avoid potential errors, categorising them as strong, intermediate, or weak based on an established patient safety action hierarchy tool.

METHOD

Focus group discussions and non-participant observations were undertaken to develop a Hierarchical Task Analysis (HTA), and subsequent focus group discussions applied the Systematic Human Error Reduction and Prediction Approach (SHERPA) focusing on the task of dispensing medication in community pharmacies. Remedial measures identified through the SHERPA analysis were then categorised as strong, intermediate, or weak based on the Veteran Affairs National Centre for Patient Safety action hierarchy. Non-participant observations were conducted at 3 pharmacies, totalling 12 hours, based in England. Additionally, 7 community pharmacists, with experience ranging from 8 to 38 years, participated in a total of 4 focus groups, each lasting between 57 to 85 minutes, with one focus group discussing the HTA and three applying SHERPA. A HTA was produced consisting of 10 sub-tasks, with further levels of sub-tasks within each of them.

RESULTS

Overall, 88 potential errors were identified, with a total of 35 remedial solutions proposed to avoid these errors in practice. Sixteen (46%) of these remedial measures were categorised as weak, 14 (40%) as intermediate and 5 (14%) as strong according to the Veteran Affairs National Centre for Patient Safety action hierarchy. Sub-tasks with the most potential errors were identified, which included 'producing medication labels' and 'final checking of medicines'. The most common type of error determined from the SHERPA analysis related to omitting a check during the dispensing process which accounted for 19 potential errors.

DISCUSSION

This work applies both HTA and SHERPA for the first time to the task of dispensing medication in community pharmacies, detailing the complexity of the task and highlighting potential errors and remedial measures specific to this task. Future research should examine the effectiveness of the proposed remedial solutions to improve patient safety.

摘要

简介

本研究旨在采用前瞻性错误分析方法,考察社区药房配药过程,并根据既定的患者安全行动层级工具,识别避免潜在错误的补救措施,将其分为强、中、弱三类。

方法

采用焦点小组讨论和非参与式观察的方法,开发了一项层次任务分析(HTA),并随后通过关注社区药房配药任务的系统性人为失误减少和预测方法(SHERPA)进行了焦点小组讨论。通过 SHERPA 分析确定的补救措施,然后根据退伍军人事务部国家患者安全中心的行动层级,分为强、中、弱三类。在英格兰的 3 家药店进行了非参与式观察,总计 12 小时。此外,7 名有 8 至 38 年经验的社区药剂师参加了总共 4 次焦点小组讨论,每次讨论持续 57 至 85 分钟,其中一次讨论 HTA,3 次讨论 SHERPA。生成了一个包含 10 个子任务的 HTA,并在每个子任务中进一步细化了子任务。

结果

总体而言,共确定了 88 个潜在错误,并提出了 35 个补救措施来避免这些错误在实践中发生。根据退伍军人事务部国家患者安全中心的行动层级,其中 16 项(46%)补救措施被归类为弱,14 项(40%)为中,5 项(14%)为强。潜在错误最多的子任务被确定,包括“制作药品标签”和“药品最终核对”。从 SHERPA 分析中确定的最常见的错误类型与在配药过程中遗漏检查有关,共涉及 19 个潜在错误。

讨论

本研究首次将 HTA 和 SHERPA 应用于社区药房的配药任务,详细说明了任务的复杂性,并突出了与该任务相关的潜在错误和补救措施。未来的研究应检验拟议补救措施的有效性,以提高患者安全性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e537/8726472/b8e72cbb0f0e/pone.0261672.g001.jpg

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