Freeman Nikki L B, McGinigle Katharine L, Leese Peter J
University of North Carolina at Chapel Hill, US.
University of North Carolina School of Medicine, US.
EGEMS (Wash DC). 2019 Jul 26;7(1):34. doi: 10.5334/egems.304.
Enhanced recovery after surgery (ERAS) aims to improve surgical outcomes by integrating evidence-based practices across preoperative, intraoperative, and postoperative care. Data in electronic medical records (EMRs) provide insight on how ERAS is implemented and its impact on surgical outcomes. Because ERAS is a multimodal pathway provided by multiple physicians and health care providers over time, identifying ERAS cases in EMRs is not a trivial task. To better understand how EMRs can be used to study ERAS, we describe our experience with using current methodologies and the development and rationale of a new method for retrospectively identifying ERAS cases in EMRs.
Using EMR data from surgical departments at the University of North Carolina at Chapel Hill, we first identified ERAS cases using a protocol-based method, using basic information including the date of ERAS implementation, surgical procedure and date, and primary surgeon. We further examined two operational flags in the EMRs, a nursing order and a case request for OR order. Wide variation between the methods compelled us to consult with ERAS surgical staff and explore the EMRs to develop a more refined method for identifying ERAS cases.
We developed a two-step method, with the first step based on the protocol definition and the second step based on an ERAS-specific medication definition. To test our method, we randomly sampled 150 general, gynecological, and urologic surgeries performed between January 1, 2016 and March 30, 2017. Surgical cases were classified as ERAS or not using the protocol definition, nursing order, case request for OR order, and our two-step method. To assess the accuracy of each method, two independent reviewers assessed the charts to determine whether cases were ERAS.
Of the 150 charts reviewed, 74 were ERAS cases. The protocol only method and nursing order flag performed similarly, correctly identifying 74 percent and 73 percent of true ERAS cases, respectively. The case request for OR order flag performed less well, correctly identifying only 44 percent of the true ERAS cases. Our two-step method performed well, correctly identifying 98 percent of true ERAS cases.
ERAS pathways are complex, making study of them from EMRs difficult. Current strategies for doing so are relatively easy to implement, but unreliable. We have developed a reproducible and observable ERAS computational phenotype that identifies ERAS cases reliably. This is a step forward in using the richness of EMR data to study ERAS implementation, efficacy, and how they can contribute to surgical care improvement.
术后加速康复（ERAS）旨在通过整合术前、术中和术后护理中的循证实践来改善手术效果。电子病历（EMR）中的数据有助于深入了解ERAS的实施方式及其对手术效果的影响。由于ERAS是由多名医生和医疗服务提供者在一段时间内提供的多模式路径，因此在电子病历中识别ERAS病例并非易事。为了更好地理解如何利用电子病历研究ERAS，我们描述了使用当前方法的经验以及一种用于在电子病历中回顾性识别ERAS病例的新方法的开发过程和基本原理。
利用北卡罗来纳大学教堂山分校外科部门的电子病历数据，我们首先使用基于方案的方法识别ERAS病例，该方法使用的基本信息包括ERAS实施日期、手术程序和日期以及主刀医生。我们进一步检查了电子病历中的两个操作标志，一个护理医嘱和一个手术室医嘱的病例申请。两种方法之间存在很大差异，这促使我们咨询ERAS手术团队并查阅电子病历，以开发一种更精确的识别ERAS病例的方法。
我们开发了一种两步法，第一步基于方案定义，第二步基于ERAS特定药物定义。为了测试我们的方法，我们随机抽取了2016年1月1日至2017年3月30日期间进行的150例普通外科、妇科和泌尿外科手术。根据方案定义、护理医嘱、手术室医嘱的病例申请以及我们的两步法，将手术病例分为ERAS病例或非ERAS病例。为了评估每种方法的准确性，两名独立的评审人员评估病历以确定病例是否为ERAS病例。
在审查的150份病历中，74例为ERAS病例。仅基于方案的方法和护理医嘱标志的表现相似，分别正确识别了74%和73%的真正ERAS病例。手术室医嘱的病例申请标志表现较差，仅正确识别了44%的真正ERAS病例。我们的两步法表现良好，正确识别了98%的真正ERAS病例。
ERAS路径复杂，使得从电子病历中对其进行研究变得困难。目前这样做的策略相对容易实施，但不可靠。我们开发了一种可重复且可观察的ERAS计算表型，能够可靠地识别ERAS病例。这是利用电子病历数据的丰富性来研究ERAS实施、疗效以及它们如何有助于改善手术护理方面向前迈出的一步。
