BACKGROUND Antimicrobial prophylaxis is an evidence-proven strategy for reducing procedure-related infections; however, measuring this key quality metric typically requires manual review, due to the way antimicrobial prophylaxis is documented in the electronic medical record (EMR). Our objective was to electronically measure compliance with antimicrobial prophylaxis using both structured and unstructured data from the Veterans Health Administration (VA) EMR. We developed this methodology for cardiac device implantation procedures. METHODS With clinician input and review of clinical guidelines, we developed a list of antimicrobial names recommended for the prevention of cardiac device infection. We trained the algorithm using existing fiscal year (FY) 2008-15 data from the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP), which contains manually determined information about antimicrobial prophylaxis. We merged CART-EP data with EMR data and programmed statistical software to flag an antimicrobial orders or drug fills from structured data fields in the EMR and hits on text string searches of antimicrobial names documented in clinician's notes. We iteratively tested combinations of these data elements to optimize an algorithm to accurately classify antimicrobial use. The final algorithm was validated in a national cohort of VA cardiac device procedures from FY2016-2017. Discordant cases underwent expert manual review to identify reasons for algorithm misclassification. RESULTS The CART-EP dataset included 2102 procedures at 38 VA facilities with manually identified antimicrobial prophylaxis in 2056 cases (97.8%). The final algorithm combining structured EMR fields and text note search results correctly classified 2048 of the CART-EP cases (97.4%). In the validation sample, the algorithm measured compliance with antimicrobial prophylaxis in 16,606 of 18,903 cardiac device procedures (87.8%). Misclassification was due to EMR documentation issues, such as antimicrobial prophylaxis documented only in hand-written clinician notes in a format that cannot be electronically searched. CONCLUSIONS We developed a methodology with high accuracy to measure guideline concordant use of antimicrobial prophylaxis before cardiac device procedures using data fields present in modern EMRs. This method can replace manual review in quality measurement in the VA and other healthcare systems with EMRs; further, this method could be adapted to measure compliance in other procedural areas where antimicrobial prophylaxis is recommended.

中文翻译：

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测量术前抗菌预防的新颖方法：将文本搜索与退伍军人卫生管理局电子病历中的结构化数据相结合。

背景技术抗菌药物的预防是减少与手术有关的感染的经实践证明的策略。但是，由于在电子病历（EMR）中记录了抗菌药物的预防措施，因此通常需要手动检查该关键质量指标。我们的目标是使用来自退伍军人卫生管理局（VA）EMR的结构化和非结构化数据，以电子方式测量对抗菌药物预防的依从性。我们为心脏装置植入程序开发了这种方法。方法通过临床医生的意见和对临床指南的审查，我们制定了推荐用于预防心脏装置感染的抗菌药物名称清单。我们使用来自VA临床评估报告和跟踪电生理学（CART-EP）的现有2008-15财政年度数据对算法进行了训练，其中包含人工确定的有关抗菌素预防的信息。我们将CART-EP数据与EMR数据和编程的统计软件合并，以标记EMR中结构化数据字段中的抗菌药物订单或药品填充，并点击临床医生笔记中记录的抗菌名称的文本字符串搜索。我们反复测试了这些数据元素的组合，以优化算法来准确分类抗菌药物的使用。最终算法在2016-2017财年的全国VA心脏设备程序队列中得到了验证。不协调的案例经过专家人工审查，以找出算法分类错误的原因。结果CART-EP数据集包括在38个VA设施中进行的2102例程序，其中2056例病例中有人工识别的抗生素预防措施（97.8％）。结合结构化EMR字段和文本注释搜索结果的最终算法可正确分类2048个CART-EP案例（97.4％）。在验证样本中，该算法在18,903例心脏设备手术中的16,606例中测量了抗菌预防的依从性（87.8％）。错误分类归因于EMR文档问题，例如仅在手写临床医生笔记中记录的抗微生物药物预防性使用无法电子搜索的格式。结论我们开发了一种高精度的方法，该方法可使用现代EMR中存在的数据字段，在心脏设备手术之前测量抗菌药物预防的一致使用指南。这种方法可以用EMR代替VA和其他医疗保健系统中质量测量中的人工检查；进一步，