Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Gertner Institute for Epidemiology and Health Policy Research, Tel HaShomer, Ramat Gan, Israel.
J Am Med Inform Assoc. 2021 Jun 12;28(6):1074-1080. doi: 10.1093/jamia/ocaa219.
We aimed to assess associations of physician's work overload, successive work shifts, and work experience with physicians' risk to err.
This large-scale study included physicians who prescribed at least 100 systemic medications at Sheba Medical Center during 2012-2017 in all acute care departments, excluding intensive care units. Presumed medication errors were flagged by a high-accuracy computerized decision support system that uses machine-learning algorithms to detect potential medication prescription errors. Physicians' successive work shifts (first or only shift, second, and third shifts), workload (assessed by the number of prescriptions during a shift) and work-experience, as well as a novel measurement of physicians' prescribing experience with a specific drug, were assessed per prescription. The risk to err was determined for various work conditions.
1 652 896 medical orders were prescribed by 1066 physicians; The system flagged 3738 (0.23%) prescriptions as erroneous. Physicians were 8.2 times more likely to err during high than normal-low workload shifts (5.19% vs 0.63%, P < .0001). Physicians on their third or second successive shift (compared to a first or single shift) were more likely to err (2.1%, 1.8%, and 0.88%, respectively, P < .001). Lack of experience in prescribing a specific medication was associated with higher error rate (0.37% for the first 5 prescriptions vs 0.13% after over 40, P < .001).
Longer hours and less experience in prescribing a specific medication increase risk of erroneous prescribing.
Restricting successive shifts, reducing workload, increasing training and supervision, and implementing smart clinical decision support systems may help reduce prescription errors.
评估医生的工作负荷、连续轮班和工作经验与医生犯错风险之间的关联。
这项大规模研究包括了 2012 年至 2017 年期间在舍巴医疗中心所有急症护理科室(不包括重症监护室)开具至少 100 种全身药物的医生。通过一个高精度的计算机决策支持系统标记出假定的药物错误,该系统使用机器学习算法来检测潜在的药物处方错误。根据每个处方评估医生的连续轮班(第一或唯一轮班、第二班和第三班)、工作量(通过轮班期间的处方数量评估)和工作经验,以及医生对特定药物的处方经验的新测量方法。确定了各种工作条件下的犯错风险。
1652896 份医疗医嘱由 1066 名医生开具;系统标记出 3738 份(0.23%)处方错误。在高工作量和正常低工作量轮班之间,医生犯错误的可能性高出 8.2 倍(5.19% 与 0.63%,P<.0001)。与第一或唯一轮班相比,第三或第二连续轮班的医生犯错误的可能性更高(分别为 2.1%、1.8%和 0.88%,P<.001)。在开具特定药物方面缺乏经验与更高的错误率相关(前 5 次处方的错误率为 0.37%,40 次以上的处方的错误率为 0.13%,P<.001)。
工作时间延长和在开具特定药物方面经验不足会增加处方错误的风险。
限制连续轮班、减少工作量、增加培训和监督以及实施智能临床决策支持系统可能有助于减少处方错误。