Sinai Health, Toronto, ON, Canada.
University of Toronto, Toronto, ON, Canada.
J Prim Care Community Health. 2023 Jan-Dec;14:21501319231210616. doi: 10.1177/21501319231210616.
Electronic medical record (EMR) prescription data may identify high antibiotic prescribers in primary care. However, practitioners doubt that population differences between providers and delayed antibiotic prescriptions are adequately accounted for in EMR-derived prescription rates. This study assessed the validity of using EMR prescription data to produce antibiotic prescription rates, accounting for these factors.
The study was a secondary analysis of antimicrobial prescriptions collected from 4 primary care clinics from 2015 to 2017. For adults with selected respiratory and urinary infections, EMR diagnostic codes, prescription data, clinical diagnoses and demographics were abstracted. Overall and delayed prescription rates were produced for EMR diagnostic codes, clinical diagnoses, by clinic, and types of infection. Direct standardization was used to adjust for case mix differences by clinic. High antibiotic prescribers, above the 75th percentile for prescriptions, were compared with low antibiotic prescribers.
Of 3108 EMR visits, there were 2577 (85.4%) eligible visits with a clinical diagnosis and prescription information. Overall antibiotic prescription rates were similar utilizing EMR records (31.6%) or clinical diagnoses (32.6%, = .40). When delayed prescriptions were removed, prescribing rates were lower (22.4%, < .01). EMR data overestimated prescribing rates for conditions where antibiotics are usually not indicated (17.7% EMR vs 7.6% clinical diagnoses, < .001). High antibiotic prescribers saw more cases where antibiotics are usually indicated (23.4%) compared to low prescribers (16.8%; = .001).
Electronic medical record prescribing rates are similar to those using clinical diagnoses overall, but overestimate prescribing by clinicians for conditions usually not needing antibiotics. EMR prescription rates do not account for delayed antibiotic prescriptions or differences in infection case-mix.
电子病历（EMR）处方数据可能识别出初级保健中的高抗生素处方医生。然而，从业者怀疑提供者之间的人群差异和延迟的抗生素处方在 EMR 衍生的处方率中没有得到充分考虑。本研究评估了使用 EMR 处方数据生成抗生素处方率的有效性，同时考虑了这些因素。
本研究是对 2015 年至 2017 年从 4 个初级保健诊所收集的抗菌药物处方进行的二次分析。对于患有选定的呼吸道和尿路感染的成年人，提取 EMR 诊断代码、处方数据、临床诊断和人口统计学信息。根据诊所和感染类型，为 EMR 诊断代码、临床诊断生成总体和延迟处方率。通过诊所直接标准化来调整病例组合差异。将高于处方第 75 百分位数的高抗生素处方医生与低抗生素处方医生进行比较。
在 3108 次 EMR 就诊中，有 2577 次（85.4%）有临床诊断和处方信息的合格就诊。使用 EMR 记录（31.6%）或临床诊断（32.6%，= 0.40），抗生素总体处方率相似。当去除延迟处方时，处方率较低（22.4%，<.01）。EMR 数据高估了通常不需要抗生素的情况下的处方率（17.7% EMR 比临床诊断 7.6%，<.001）。高抗生素处方医生看到更多通常需要使用抗生素的病例（23.4%），而低处方医生则为 16.8%（= 0.001）。
总体而言，电子病历的处方率与使用临床诊断的处方率相似，但高估了通常不需要抗生素的情况下临床医生的处方。EMR 处方率没有考虑到延迟的抗生素处方或感染病例组合的差异。
