Novel methodology to measure pre-procedure antimicrobial prophylaxis: integrating text searches with structured data from the Veterans Health Administration's electronic medical record
Novel methodology to measure pre-procedure antimicrobial prophylaxis: integrating text searches with structured data from the Veterans Health Administration's electronic medical record
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DOI：
10.1186/s12911-020-1031-5
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发表时间：
2020-01-30
影响因子：
3.5
通讯作者：
Branch-Elliman, Westyn
中科院分区：
文献类型：
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作者：
Mull, Hillary J.;Stolzmann, Kelly;Branch-Elliman, Westyn
背景抗菌预防是一种经证据证明的减少手术相关感染的策略;然而，由于电子病历（EMR）中记录抗菌预防的方式，测量这一关键质量指标通常需要人工审查。我们的目标是使用退伍军人健康管理局（VA）EMR的结构化和非结构化数据以电子方式测量抗菌预防的依从性。我们开发了用于心脏装置植入手术的这种方法。方法通过临床医生的输入和临床指南的审查，我们制定了一个推荐用于预防心脏器械感染的抗菌药物名称列表。我们使用来自VA临床评估报告和跟踪电生理学（CART-EP）的2008-15财政年度（FY）现有数据训练算法，其中包含手动确定的抗菌预防信息。我们将CART-EP数据与EMR数据合并，并编程统计软件以标记来自EMR中的结构化数据字段的抗菌药物订单或药物填充，并点击记录在临床医生笔记中的抗菌药物名称的文本字符串搜索。我们迭代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VA心脏器械手术的国家队列中得到确认。对不一致病例进行专家手动审查，以确定算法错误分类的原因。结果CART-EP数据集包括38家VA机构的2102例手术，其中2056例（97.8%）采用手动识别的抗菌预防措施。结合结构化EMR字段和文本注释搜索结果的最终算法正确分类了2048个CART-EP病例（97.4%）。在验证样本中，该算法测量了18，903例心脏器械手术中16，606例（87.8%）的抗菌预防依从性。错误分类是由于EMR文件问题，例如抗菌预防措施仅记录在手写的临床记录中，无法进行电子检索。结论：我们开发了一种高准确度的方法，使用现代EMR中的数据字段来衡量心脏器械手术前抗菌药物预防性使用的指南一致性。这种方法可以取代在VA和其他医疗保健系统中使用EMR进行质量测量的手动审查;此外，这种方法可以适用于测量推荐抗菌预防的其他程序领域的依从性。
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.