Waters Riley, Malecki Sarah, Lail Sharan, Mak Denise, Saha Sudipta, Jung Hae Young, Imrit Mohammed Arshad, Razak Fahad, Verma Amol A
St. Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.
Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
JAMIA Open. 2023 Aug 8;6(3):ooad062. doi: 10.1093/jamiaopen/ooad062. eCollection 2023 Oct.
Patient data repositories often assemble medication data from multiple sources, necessitating standardization prior to analysis. We implemented and evaluated a medication standardization procedure for use with a wide range of pharmacy data inputs across all drug categories, which supports research queries at multiple levels of granularity.
The GEMINI-RxNorm system automates the use of multiple RxNorm tools in tandem with other datasets to identify drug concepts from pharmacy orders. GEMINI-RxNorm was used to process 2 090 155 pharmacy orders from 245 258 hospitalizations between 2010 and 2017 at 7 hospitals in Ontario, Canada. The GEMINI-RxNorm system matches drug-identifying information from pharmacy data (including free-text fields) to RxNorm concept identifiers. A user interface allows researchers to search for drug terms and returns the relevant original pharmacy data through the matched RxNorm concepts. Users can then manually validate the predicted matches and discard false positives. We designed the system to maximize recall (sensitivity) and enable excellent precision (positive predictive value) with efficient manual validation. We compared the performance of this system to manual coding (by a physician and pharmacist) of 13 medication classes.
Manual coding was performed for 1 948 817 pharmacy orders and GEMINI-RxNorm successfully returned 1 941 389 (99.6%) orders. Recall was greater than 0.985 in all 13 drug classes, and the F1-score and precision remained above 0.90 in all drug classes, facilitating efficient manual review to achieve 100% precision. GEMINI-RxNorm saved time substantially compared with manual standardization, reducing the time taken to review a pharmacy order row from an estimated 30 to 5 s and reducing the number of rows needed to be reviewed by up to 99.99%.
GEMINI-RxNorm presents a novel combination of RxNorm tools and other datasets to enable accurate, efficient, flexible, and scalable standardization of pharmacy data. By facilitating efficient manual validation, the GEMINI-RxNorm system can allow researchers to achieve near-perfect accuracy in medication data standardization.
患者数据存储库通常会从多个来源收集用药数据,因此在分析之前需要进行标准化处理。我们实施并评估了一种用药标准化程序,该程序适用于所有药物类别的各种药房数据输入,支持多个粒度级别的研究查询。
GEMINI-RxNorm系统将多个RxNorm工具与其他数据集结合使用,以从药房订单中识别药物概念。GEMINI-RxNorm用于处理2010年至2017年期间加拿大安大略省7家医院245258例住院病例中的2090155份药房订单。GEMINI-RxNorm系统将药房数据(包括自由文本字段)中的药物识别信息与RxNorm概念标识符进行匹配。用户界面允许研究人员搜索药物术语,并通过匹配的RxNorm概念返回相关的原始药房数据。然后,用户可以手动验证预测的匹配项并丢弃误报。我们设计该系统以最大限度地提高召回率(敏感性),并通过高效的手动验证实现出色的精确率(阳性预测值)。我们将该系统的性能与13种用药类别的手动编码(由一名医生和一名药剂师进行)进行了比较。
对1948817份药房订单进行了手动编码,GEMINI-RxNorm成功返回了1941389份(99.6%)订单。在所有13种药物类别中,召回率均大于0.985,所有药物类别的F1分数和精确率均保持在0.90以上,便于进行高效的人工审核以达到100%的精确率。与手动标准化相比,GEMINI-RxNorm大大节省了时间,将审核一行药房订单所需的时间从估计的30秒减少到5秒,并将需要审核的行数减少了多达99.99%。
GEMINI-RxNorm展示了RxNorm工具与其他数据集的新颖组合,能够实现药房数据准确、高效、灵活且可扩展的标准化。通过促进高效的人工验证,GEMINI-RxNorm系统可以使研究人员在用药数据标准化方面达到近乎完美的准确性。