Division of Emergency Medicine, Washington University School of Medicine, Saint Louis, MO.
Division of Emergency Medicine, Washington University School of Medicine, Saint Louis, MO.
Ann Emerg Med. 2020 Aug;76(2):230-240. doi: 10.1016/j.annemergmed.2019.07.032. Epub 2019 Oct 14.
Trigger tools improve surveillance for harm by focusing reviews on records with "triggers" whose presence increases the likelihood of an adverse event. We refine and automate a previously developed emergency department (ED) trigger tool and present record selection strategies to further optimize yield.
We specified 97 triggers for extraction from our electronic medical record, identifying 76,894 ED visits with greater than or equal to 1 trigger. We reviewed 1,726 records with greater than or equal to 1 trigger, following a standard trigger tool review process. We validated query performance against manual review and evaluated individual triggers, retaining only those associated with adverse events in the ED. We explored 2 approaches to enhance record selection: on number of triggers present and using trigger weights derived with least absolute shrinkage and selection operator logistic regression.
The automated query performed well compared with manual review (sensitivity >70% for 80 triggers; specificity >92% for all). Review yielded 374 adverse events (21.6 adverse events per 100 records). Thirty triggers were associated with risk of harm in the ED. An estimated 10.3% of records with greater than 1 of these triggers would include an adverse event in the ED. Selecting only records with greater than or equal to 4 or greater than or equal to 9 triggers improves yield to 17% and 34.8%, respectively, whereas use of least absolute shrinkage and selection operator trigger weighting enhances the yield to as high as 52%.
The ED trigger tool is a promising approach to improve yield, scope, and efficiency of review for all-cause harm in emergency medicine. Beginning with a broad set of candidate triggers, we validated a computerized query that eliminates the need for manual screening for triggers and identified a refined set of triggers associated with adverse events in the ED. Review efficiency can be further enhanced with enhanced record selection.
触发工具通过将审查重点放在有“触发因素”的记录上，从而提高对伤害的监测，这些触发因素的存在增加了不良事件发生的可能性。我们改进并自动化了之前开发的急诊（ED）触发工具，并提出了记录选择策略，以进一步优化效果。
我们从电子病历中指定了 97 个提取触发器，确定了 76894 次 ED 就诊，其中至少有 1 个触发因素。我们按照标准的触发工具审查流程，对至少有 1 个触发因素的 1726 份记录进行了审查。我们通过手动审查验证了查询性能，并评估了各个触发器，只保留了与 ED 中不良事件相关的触发器。我们探索了两种增强记录选择的方法：基于存在的触发器数量和使用最小绝对收缩和选择算子逻辑回归得出的触发器权重。
自动化查询与手动审查相比表现良好（对于 80 个触发器，敏感性> 70%；对于所有触发器，特异性> 92%）。审查共发现 374 起不良事件（每 100 份记录中有 21.6 起不良事件）。30 个触发器与 ED 中的伤害风险相关。估计有 10.3%的记录，这些记录中存在大于 1 个此类触发器，则会在 ED 中发生不良事件。仅选择大于或等于 4 个或大于或等于 9 个触发器的记录可以将效果提高到 17%和 34.8%，而使用最小绝对收缩和选择算子触发加权则可以将效果提高到高达 52%。
ED 触发工具是一种很有前途的方法，可以提高急诊医学中所有原因伤害审查的效果、范围和效率。从一组广泛的候选触发因素开始，我们验证了一种计算机化查询，该查询不需要手动筛选触发因素，并确定了一组与 ED 中不良事件相关的改进触发因素。通过增强记录选择，可以进一步提高审查效率。
