The impact of electronic medical records data sources on an adverse drug event quality measure

度量(数据仓库) 药物不良事件 病历 质量(理念) 事件(粒子物理) 健康档案 不利影响 医疗急救 医学 计算机科学 数据挖掘 药理学 内科学 医疗保健 经济 哲学 物理 认识论 量子力学 经济增长
作者
Michael G. Kahn,Daksha Ranade
出处
期刊:Journal of the American Medical Informatics Association [Oxford University Press]
卷期号:17 (2): 185-191 被引量:30
标识
DOI:10.1136/jamia.2009.002451
摘要

Objective To examine the impact of billing and clinical data extracted from an electronic medical record system on the calculation of an adverse drug event (ADE) quality measure approved for use in The Joint Commission's ORYX program, a mandatory national hospital quality reporting system. Design The Child Health Corporation of America's "Use of Rescue Agents—ADE Trigger" quality measure uses medication billing data contained in the Pediatric Health Information Systems (PHIS) data warehouse to create The Joint Commission-approved quality measure. Using a similar query, we calculated the quality measure using PHIS plus four data sources extracted from our electronic medical record (EMR) system: medications charged, medication orders placed, medication orders with associated charges (orders charged), and medications administered. Measurements Inclusion and exclusion criteria were identical for all queries. Denominators and numerators were calculated using the five data sets. The reported quality measure is the ADE rate (numerator/denominator). Results Significant differences in denominators, numerators, and rates were calculated from different data sources within a single institution's EMR. Differences were due to both common clinical practices that may be similar across institutions and unique workflow practices not likely to be present at any other institution. The magnitude of the differences would significantly alter the national comparative ranking of our institution compared to other PHIS institutions. Conclusions More detailed clinical information may result in quality measures that are not comparable across institutions due institution-specific workflow, differences that are exposed using EMR-derived data.
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