Center for Innovations in Quality, Effectiveness, and Safety (IQuESt), Michael E DeBakey Veterans Affairs Medical Center, Houston, Texas, USA.
Department of Medicine, Baylor College of Medicine, Houston, Texas, USA.
BMJ Qual Saf. 2018 Mar;27(3):241-246. doi: 10.1136/bmjqs-2017-006975. Epub 2017 Sep 21.
Methods to identify preventable adverse events typically have low yield and efficiency. We refined the methods of Institute of Healthcare Improvement's Global Trigger Tool (GTT) application and leveraged electronic health record (EHR) data to improve detection of preventable adverse events, including diagnostic errors.
We queried the EHR data repository of a large health system to identify an 'index hospitalization' associated with care escalation (defined as transfer to the intensive care unit (ICU) or initiation of rapid response team (RRT) within 15 days of admission) between March 2010 and August 2015. To enrich the record review sample with unexpected events, we used EHR clinical data to modify the GTT algorithm and limited eligible patients to those at risk for care escalation based on younger age and presence of minimal comorbid conditions. We modified the GTT review methodology; two physicians independently reviewed eligible 'e-trigger' positive records to identify preventable diagnostic and care management events.
Of 88 428 hospitalisations, 887 were associated with care escalation (712 ICU transfers and 175 RRTs), of which 92 were flagged as trigger-positive and reviewed. Preventable adverse events were detected in 41 cases, yielding a trigger positive predictive value of 44.6% (reviewer agreement 79.35%; Cohen's kappa 0.573). We identified 7 (7.6%) diagnostic errors and 34 (37.0%) care management-related events: 24 (26.1%) adverse drug events, 4 (4.3%) patient falls, 4 (4.3%) procedure-related complications and 2 (2.2%) hospital-associated infections. In most events (73.1%), there was potential for temporary harm.
We developed an approach using an EHR data-based trigger and modified review process to efficiently identify hospitalised patients with preventable adverse events, including diagnostic errors. Such e-triggers can help overcome limitations of currently available methods to detect preventable harm in hospitalised patients.
识别可预防的不良事件的方法通常产量和效率都较低。我们改进了医疗改善研究所全球触发工具(GTT)应用方法,并利用电子健康记录(EHR)数据来提高可预防的不良事件(包括诊断错误)的检测效率。
我们查询了一个大型医疗系统的电子健康记录数据库,以确定 2010 年 3 月至 2015 年 8 月期间与护理升级相关的“索引住院”(定义为入院后 15 天内转入重症监护病房(ICU)或启动快速反应小组(RRT))。为了通过 EHR 临床数据丰富记录审查样本,我们使用 EHR 临床数据修改了 GTT 算法,并根据年龄较小和存在最小合并症的情况,将符合条件的患者限定为有护理升级风险的患者。我们修改了 GTT 审查方法;两名医生独立审查合格的“电子触发”阳性记录,以识别可预防的诊断和护理管理事件。
在 88428 例住院患者中,有 887 例与护理升级相关(712 例 ICU 转科和 175 例 RRT),其中 92 例被标记为触发阳性并进行了审查。在 41 例中发现了可预防的不良事件,触发阳性预测值为 44.6%(审查员一致性为 79.35%;Cohen's kappa 0.573)。我们发现了 7 例(7.6%)诊断错误和 34 例(37.0%)与护理管理相关的事件:24 例(26.1%)药物不良反应、4 例(4.3%)患者跌倒、4 例(4.3%)与程序相关的并发症和 2 例(2.2%)医院相关感染。在大多数事件(73.1%)中,存在暂时伤害的可能性。
我们开发了一种使用基于 EHR 数据的触发和修改后的审查流程的方法,以有效地识别可预防不良事件(包括诊断错误)的住院患者。这种电子触发可以帮助克服当前可用方法在检测住院患者可预防伤害方面的局限性。