Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, USA.
Division of Hospital Medicine, Children's Healthcare of Atlanta, Atlanta, Georgia, USA.
J Am Med Inform Assoc. 2021 Nov 25;28(12):2654-2660. doi: 10.1093/jamia/ocab179.
Excessive electronic health record (EHR) alerts reduce the salience of actionable alerts. Little is known about the frequency of interruptive alerts across health systems and how the choice of metric affects which users appear to have the highest alert burden.
(1) Analyze alert burden by alert type, care setting, provider type, and individual provider across 6 pediatric health systems. (2) Compare alert burden using different metrics.
We analyzed interruptive alert firings logged in EHR databases at 6 pediatric health systems from 2016-2019 using 4 metrics: (1) alerts per patient encounter, (2) alerts per inpatient-day, (3) alerts per 100 orders, and (4) alerts per unique clinician days (calendar days with at least 1 EHR log in the system). We assessed intra- and interinstitutional variation and how alert burden rankings differed based on the chosen metric.
Alert burden varied widely across institutions, ranging from 0.06 to 0.76 firings per encounter, 0.22 to 1.06 firings per inpatient-day, 0.98 to 17.42 per 100 orders, and 0.08 to 3.34 firings per clinician day logged in the EHR. Custom alerts accounted for the greatest burden at all 6 sites. The rank order of institutions by alert burden was similar regardless of which alert burden metric was chosen. Within institutions, the alert burden metric choice substantially affected which provider types and care settings appeared to experience the highest alert burden.
Estimates of the clinical areas with highest alert burden varied substantially by institution and based on the metric used.
过多的电子健康记录 (EHR) 警报会降低可操作警报的显著性。对于整个医疗系统中中断性警报的频率以及选择的衡量标准如何影响哪些用户似乎具有最高的警报负担，知之甚少。
(1) 分析 6 个儿科医疗系统中按警报类型、护理环境、提供者类型和个体提供者分类的警报负担。(2) 使用不同的指标比较警报负担。
我们使用 4 种指标分析了 6 个儿科医疗系统在 2016-2019 年间 EHR 数据库中记录的中断性警报触发情况：(1) 每个患者就诊的警报数，(2) 每个住院日的警报数，(3) 每 100 个医嘱的警报数，以及 (4) 每个独特临床医生日（至少有 1 个 EHR 登录系统的日历日）的警报数。我们评估了机构内和机构间的变化以及基于所选指标的警报负担排名差异。
各机构之间的警报负担差异很大，范围从每次就诊 0.06 到 0.76 次触发，每次住院日 0.22 到 1.06 次触发，每 100 个医嘱 0.98 到 17.42 次触发，每个临床医生日 0.08 到 3.34 次触发。在所有 6 个站点，自定义警报的负担最大。无论选择哪种警报负担衡量标准，机构的警报负担排名顺序都相似。在机构内部，警报负担衡量标准的选择极大地影响了哪些提供者类型和护理环境似乎经历了最高的警报负担。
根据机构和使用的衡量标准，估计具有最高警报负担的临床领域的结果差异很大。
