Identifying and Reducing Insulin Errors in the Simulated Military Critical Care Air Transport Environment: A Human Factors Approach

Author:

Frasier Lane L1,Cheney Mark23,Burkhardt Joshua24,Alderman Mark2,Nelson Eric2,Proctor Melissa2,Brown Daniel25,Davis William T67,Smith Maia P1,Strilka Richard12

Affiliation:

1. Department of Surgery, University of Cincinnati , Cincinnati, OH 45267, USA

2. Center for Sustainment of Trauma and Readiness Skills, University of Cincinnati , Cincinnati, OH 45219, USA

3. Department of Anesthesiology, University of Cincinnati , Cincinnati, OH 45219, USA

4. Department of Emergency Medicine, University of Cincinnati , Cincinnati, OH 45219, USA

5. Department of Emergency Medicine, Wright State University , Dayton, OH 45324, USA

6. 59th Medical Wing Science and Technology, United States Air Fore En route Care Research Center , JBSA-Fort Sam Houston, TX 78234, USA

7. Department of Military and Emergency Medicine, Uniformed Services University , Bethesda, MD 20814, USA

Abstract

ABSTRACT Introduction During high-fidelity simulations in the Critical Care Air Transport (CCAT) Advanced course, we identified a high frequency of insulin medication errors and sought strategies to reduce them using a human factors approach. Materials and Methods Of 169 eligible CCAT simulations, 22 were randomly selected for retrospective audio–video review to establish a baseline frequency of insulin medication errors. Using the Human Factors Analysis Classification System, dosing errors, defined as a physician ordering an inappropriate dose, were categorized as decision-based; administration errors, defined as a clinician preparing and administering a dose different than ordered, were categorized as skill-based. Next, 3 a priori interventions were developed to decrease the frequency of insulin medication errors, and these were grouped into 2 study arms. Arm 1 included a didactic session reviewing a sliding-scale insulin (SSI) dosing protocol and a hands-on exercise requiring all CCAT teams to practice preparing 10 units of insulin including a 2-person check. Arm 2 contained arm 1 interventions and added an SSI cognitive aid available to students during simulation. Frequency and type of insulin medication errors were collected for both arms with 93 simulations for arm 1 (January–August 2021) and 139 for arm 2 (August 2021–July 2022). The frequency of decision-based and skill-based errors was compared across control and intervention arms. Results Baseline insulin medication error rates were as follows: decision-based error occurred in 6/22 (27.3%) simulations and skill-based error occurred in 6/22 (27.3%). Five of the 6 skill-based errors resulted in administration of a 10-fold higher dose than ordered. The post-intervention decision-based error rates were 9/93 (9.7%) and 23/139 (2.2%), respectively, for arms 1 and 2. Compared to baseline error rates, both arm 1 (P = .04) and arm 2 (P < .001) had a significantly lower rate of decision-based errors. Additionally, arm 2 had a significantly lower decision-based error rate compared to arm 1 (P = .015). For skill-based preparation errors, 1/93 (1.1%) occurred in arm 1 and 4/139 (2.9%) occurred in arm 2. Compared to baseline, this represents a significant decrease in skill-based error in both arm 1 (P < .001) and arm 2 (P < .001). There were no significant differences in skill-based error between arms 1 and 2. Conclusions This study demonstrates the value of descriptive error analysis during high-fidelity simulation using audio–video review and effective risk mitigation using training and cognitive aids to reduce medication errors in CCAT. As demonstrated by post-intervention observations, a human factors approach successfully reduced decision-based error by using didactic training and cognitive aids and reduced skill-based error using hands-on training. We recommend the development of a Clinical Practice Guideline including an SSI protocol, guidelines for a 2-person check, and a cognitive aid for implementation with deployed CCAT teams. Furthermore, hands-on training for insulin preparation and administration should be incorporated into home station sustainment training to reduced medication errors in the operational environment.

Funder

Defense Health Agency

Publisher

Oxford University Press (OUP)

Reference39 articles.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3