Thomas F Byrd, Molly Mattson, Mary Polt, Katie Pint, Kiril Dimitrov, Angelica Willis, Julia Lister, Evan Beacom, Chris Tignanelli
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DTI scores, alert icons, and vital signs appeared on a custom Patient List interface. In the event of an alert, charge nurses were expected to conduct a bedside assessment and escalate care as necessary. We compared the 111-day pre- and postimplementation periods, with alert-to-action time as the primary outcome. Secondary outcomes included mortality, length of stay, ICU transfer, documentation rate, and provider acceptance.Among 301 patients with an elevated-risk score (156 pre- and 145 postimplementation), we found no significant differences in alert-to-action time (469 vs. 359 minutes before alert; <i>p</i> = 0.96), with provider actions typically occurring several hours before the alert in both periods. There were no significant differences in mortality (10.3% vs. 13.1%; <i>p</i> = 0.56), length of stay (15.7 vs. 12.8 days; <i>p</i> = 0.23), or ICU transfer (8.3% vs. 6.2%; <i>p</i> = 0.63). Charge nurses documented acknowledgment of the alert in 18.6% of cases, and acceptance was poor. Most nurses expressed a preference for interruptive alerts and more prominent DTI display locations.In this single-unit pilot, passive DTI-enabled EWS alerts did not improve time to intervention or clinical outcomes. 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引用次数: 0
摘要
传统的早期预警系统(EWS)在现实环境中显示出不确定的有效性。最近,像史诗退化指数(DTI)这样的机器学习模型已经开发出来,承诺更高的准确性。认识到DTI的潜力,以及中断(即弹出式)EWS警报普遍存在的警报疲劳问题,我们的机构实现了一个支持DTI的EWS,带有被动警报(在电子健康记录的预先指定位置可见彩色图标)。我们假设我们的干预可以缩短病情恶化患者的治疗时间。我们在一家大型学术医疗中心的30张床位的中级护理单元中试用了支持dti的EWS。DTI评分、警报图标和生命体征出现在自定义患者列表界面上。在警报的情况下,主管护士应进行床边评估,并在必要时升级护理。我们比较了111天的实施前和实施后的时间,以预警到行动的时间作为主要结果。次要结局包括死亡率、住院时间、ICU转移、记录率和提供者接受度。在301名风险评分较高的患者中(156名实施前和145名实施后),我们发现在警报到行动的时间上没有显著差异(警报前469分钟对359分钟;p = 0.96),在这两个时期,提供者的行动通常发生在警报前几个小时。两组患者的死亡率(10.3% vs. 13.1%, p = 0.56)、住院时间(15.7 vs. 12.8天,p = 0.23)和转ICU时间(8.3% vs. 6.2%, p = 0.63)均无显著差异。在18.6%的病例中,主管护士记录了对警报的承认,接受程度较差。大多数护士表达了对中断警报和更突出的DTI显示位置的偏好。在这个单单元试验中,被动dti激活的EWS警报并没有改善干预时间或临床结果。高风险DTI评分通常发生在已经识别出临床恶化的情况下。
Implementation of Passive Deterioration Index Alerts in an Intermediate Care Unit: A Failed Early Warning System Strategy.
Traditional early warning systems (EWS) have shown uncertain efficacy in real-world settings. More recently, machine learning models like the Epic Deterioration Index (DTI) have been developed, promising greater accuracy. Recognizing the potential of DTI, but also the pervasive issue of alert fatigue with interruptive (i.e., pop-up) EWS alerts, our institution implemented a DTI-enabled EWS with passive alerts (colored icons visible in prespecified locations within the electronic health record). We hypothesized that our intervention would reduce the time to treatment for deteriorating patients.We piloted a DTI-enabled EWS in a 30-bed intermediate care unit at a large academic medical center. DTI scores, alert icons, and vital signs appeared on a custom Patient List interface. In the event of an alert, charge nurses were expected to conduct a bedside assessment and escalate care as necessary. We compared the 111-day pre- and postimplementation periods, with alert-to-action time as the primary outcome. Secondary outcomes included mortality, length of stay, ICU transfer, documentation rate, and provider acceptance.Among 301 patients with an elevated-risk score (156 pre- and 145 postimplementation), we found no significant differences in alert-to-action time (469 vs. 359 minutes before alert; p = 0.96), with provider actions typically occurring several hours before the alert in both periods. There were no significant differences in mortality (10.3% vs. 13.1%; p = 0.56), length of stay (15.7 vs. 12.8 days; p = 0.23), or ICU transfer (8.3% vs. 6.2%; p = 0.63). Charge nurses documented acknowledgment of the alert in 18.6% of cases, and acceptance was poor. Most nurses expressed a preference for interruptive alerts and more prominent DTI display locations.In this single-unit pilot, passive DTI-enabled EWS alerts did not improve time to intervention or clinical outcomes. High-risk DTI scores often occurred after clinical deterioration had already been recognized.
期刊介绍:
ACI is the third Schattauer journal dealing with biomedical and health informatics. It perfectly complements our other journals Öffnet internen Link im aktuellen FensterMethods of Information in Medicine and the Öffnet internen Link im aktuellen FensterYearbook of Medical Informatics. The Yearbook of Medical Informatics being the “Milestone” or state-of-the-art journal and Methods of Information in Medicine being the “Science and Research” journal of IMIA, ACI intends to be the “Practical” journal of IMIA.