Department of Radiology, William S. Middleton Veterans Hospital, Madison, Wisconsin, United States.
Department of Medicine, William S. Middleton Memorial Veterans Hospital and University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin, United States.
Appl Clin Inform. 2020 Jan;11(1):79-87. doi: 10.1055/s-0039-3402730. Epub 2020 Jan 29.
Despite progress in patient safety, misidentification errors in radiology such as ordering imaging on the wrong anatomic side persist. If undetected, these errors can cause patient harm for multiple reasons, in addition to producing erroneous electronic health records (EHR) data.
We describe the pilot testing of a quality improvement methodology using electronic trigger tools and preimaging checklists to detect "wrong-side" misidentification errors in radiology examination ordering, and to measure staff adherence to departmental policy in error remediation.
We retrospectively applied and compared two methods for the detection of "wrong-side" misidentification errors among a cohort of all imaging studies ordered during a 1-year period (June 1, 2015-May 31, 2016) at our tertiary care hospital. Our methods included: (1) manual review of internal quality improvement spreadsheet records arising from the prospective performance of preimaging safety checklists, and (2) automated error detection via the development and validation of an electronic trigger tool which identified discrepant side indications within EHR imaging orders.
Our combined methods detected misidentification errors in 6.5/1,000 of study cohort imaging orders. Our trigger tool retrospectively identified substantially more misidentification errors than were detected prospectively during preimaging checklist performance, with a high positive predictive value (PPV: 88.4%, 95% confidence interval: 85.4-91.4). However, two third of errors detected during checklist performance were not detected by the trigger tool, and checklist-detected errors were more often appropriately resolved ( < 0.00001, 95% confidence interval: 2.0-6.9; odds ratio: 3.6).
Our trigger tool enabled the detection of substantially more imaging ordering misidentification errors than preimaging safety checklists alone, with a high PPV. Many errors were only detected by the preimaging checklist; however, suggesting that additional trigger tools may need to be developed and used in conjunction with checklist-based methods to ensure patient safety.
尽管在患者安全方面取得了进展，但放射科的身份识别错误（如对错误解剖侧的影像检查申请）仍然存在。如果这些错误未被发现，除了产生错误的电子健康记录 (EHR) 数据外，还会因为多种原因给患者带来伤害。
我们描述了一种使用电子触发工具和影像检查前核对清单来检测放射科检查申请中“错误侧”身份识别错误的质量改进方法的初步测试，并衡量工作人员在错误纠正方面对部门政策的遵守情况。
我们回顾性地应用并比较了两种方法，以检测我们三级保健医院在一年期间（2015 年 6 月 1 日至 2016 年 5 月 31 日）所有影像检查申请中的“错误侧”身份识别错误。我们的方法包括：（1）通过前瞻性执行影像检查前安全核对清单，对内部质量改进电子表格记录进行手动审查，以及（2）通过开发和验证电子触发工具来检测电子病历影像申请中不一致的侧位指示，从而实现自动错误检测。
我们的综合方法在研究队列的 6.5/1000 影像检查申请中检测到身份识别错误。我们的触发工具回溯性地检测到的身份识别错误明显多于在执行影像检查前核对清单时前瞻性检测到的错误，具有较高的阳性预测值（PPV：88.4%，95%置信区间：85.4-91.4）。然而，核对清单检测到的错误中有三分之二未被触发工具检测到，并且核对清单检测到的错误更常被正确解决（ < 0.00001，95%置信区间：2.0-6.9；比值比：3.6）。
我们的触发工具能够检测到比单独使用影像检查前核对清单更多的影像检查申请身份识别错误，且具有较高的 PPV。许多错误仅通过影像检查前核对清单被检测到；然而，这表明可能需要开发和使用其他触发工具，并与基于核对清单的方法结合使用，以确保患者安全。
