Shah Sonam N, Seger Diane L, Fiskio Julie M, Horn John R, Bates David W
Department of General Internal Medicine, Brigham and Women's Hospital, 41 Avenue Louis Pasteur, Office 103, Boston, MA, 02115, USA.
Department of Pharmacy Practice, MCPHS University, Boston, MA, USA.
Drug Saf. 2021 Jun;44(6):661-668. doi: 10.1007/s40264-021-01048-0. Epub 2021 Feb 22.
Medication organizations across the USA have adopted electronic health records, and one of the most anticipated benefits of these was improved medication safety, but alert fatigue has been a major issue.
We compared the appropriateness of medication-related clinical decision support alerts triggered by two commercial applications: EPIC and Seegnal's platform.
This was a retrospective comparison of two commercial applications. We provided Seegnal with deidentified inpatient, outpatient, and inpatient genetic electronic medical record (EMR)-extracted datasets for 657, 2731, and 413 patients, respectively. Seegnal then provided the alerts that would have triggered, which we compared with those triggered by EPIC in clinical care. A random sample of the alerts triggered were reviewed for appropriateness, and the positive predictive value (PPV) and negative predictive value (NPV) were calculated. We also reviewed all the inpatient and outpatient charts for patients within our cohort who were receiving ten or more concomitant medications with alerts we found to be appropriate to assess whether any adverse events had occurred and whether Seegnal's platform could have prevented them.
Results from EPIC and the Seegnal platform were compared based on alert load, PPV, NPV, and potential adverse events. Overall, compared with EPIC, the Seegnal platform triggered fewer alerts in the inpatient (1697 vs. 27,540), outpatient (2341 vs. 35,134), and inpatient genetic (1493 vs. 20,975) cohorts. The Seegnal platform had higher specificity in the inpatient (99 vs. 0.3%; p < 0.0001), outpatient (99 vs. 0.3%; p < 0.0001), and inpatient genetic (97.9 vs. 1.2%; p < 0.0001) groups and higher sensitivity in the inpatient (100 vs. 68.8%; p < 0.0001) and outpatient (88.6 vs.78.3%; p < 0.0001) groups but not in the inpatient genetic cohort (81 vs. 78.5%; p = 0.11). We identified 16 adverse events that occurred in the inpatient setting, 11 (69%) of which potentially could have been prevented with the Seegnal platform.
Overall, the Seegnal platform triggered 94% fewer alerts than EPIC in the inpatient setting and 93% fewer in the outpatient setting, with much higher sensitivity and specificity. This application could substantially reduce alert fatigue and improve medication safety at the same time.
美国各地的医疗机构都采用了电子健康记录，人们最期待的好处之一是提高用药安全性，但警报疲劳一直是个大问题。
我们比较了两款商业应用程序触发的与用药相关的临床决策支持警报的恰当性：EPIC和Seegnal平台。
这是对两款商业应用程序的回顾性比较。我们分别向Seegnal提供了去识别化的住院、门诊和住院基因电子病历（EMR）提取数据集，涉及657名、2731名和413名患者。然后Seegnal提供了可能触发的警报，我们将其与临床护理中EPIC触发的警报进行比较。对触发的警报随机抽样检查其恰当性，并计算阳性预测值（PPV）和阴性预测值（NPV）。我们还查阅了队列中正在接受十种或更多联合用药且警报被认为恰当的患者的所有住院和门诊病历，以评估是否发生了任何不良事件以及Seegnal平台是否可以预防这些事件。
基于警报负荷、PPV、NPV和潜在不良事件对EPIC和Seegnal平台的结果进行了比较。总体而言，与EPIC相比，Seegnal平台在住院患者（1697条对27540条）、门诊患者（2341条对35134条）和住院基因患者（1493条对20975条）队列中触发的警报较少。Seegnal平台在住院患者组（99%对0.3%；p<0.0001）、门诊患者组（99%对0.3%；p<0.0001）和住院基因患者组（97.9%对1.2%；p<0.0001）中具有更高的特异性，在住院患者组（100%对68.8%；p<0.0001）和门诊患者组（88.6%对78.3%；p<0.0001）中具有更高的敏感性，但在住院基因患者队列中并非如此（81%对78.5%；p=0.11）。我们确定了16起发生在住院环境中的不良事件，其中11起（69%）可能通过Seegnal平台预防。
总体而言，Seegnal平台在住院环境中触发的警报比EPIC少94%，在门诊环境中少93%，同时具有更高的敏感性和特异性。该应用程序可以大幅减少警报疲劳并同时提高用药安全性。
