Krysko Kristen M, Ivers Noah M, Young Jacqueline, O'Connor Paul, Tu Karen
University of Toronto, Toronto, Canada.
Institute for Clinical Evaluative Sciences (ICES), Toronto, Canada/University of Toronto, Canada/Women's College Hospital, Toronto, Canada.
Mult Scler. 2015 Feb;21(2):217-24. doi: 10.1177/1352458514538334. Epub 2014 Jun 12.
The increasing use of electronic medical records (EMRs) presents an opportunity to efficiently evaluate and improve quality of care for individuals with MS.
We aimed to establish an algorithm to identify individuals with MS within EMRs.
We used a sample of 73,003 adult patients from 83 primary care physicians in Ontario using the Electronic Medical Record Administrative data Linked Database (EMRALD). A reference standard of 247 individuals with MS was identified through chart abstraction. The accuracy of identifying individuals with MS in an EMR was assessed using information in the cumulative patient profile (CPP), prescriptions and physician billing codes.
An algorithm identifying MS in the CPP performed well with 91.5% sensitivity, 100% specificity, 98.7% PPV and 100% NPV. The addition of prescriptions for MS-specific medications and physician billing code 340 used four times within any 12-month timeframe slightly improved the sensitivity to 92.3% with a PPV of 97.9%.
Data within an EMR can be used to accurately identify patients with MS. This study has positive implications for clinicians, researchers and policy makers as it provides the potential to identify cohorts of MS patients in the primary care setting to examine quality of care.
电子病历（EMR）使用的增加为有效评估和改善多发性硬化症（MS）患者的医疗质量提供了机会。
我们旨在建立一种算法，以在电子病历中识别患有MS的个体。
我们使用了安大略省83名初级保健医生的73,003名成年患者的样本，这些数据来自电子病历管理数据链接数据库（EMRALD）。通过病历摘要确定了247名患有MS的个体作为参考标准。使用累积患者档案（CPP）、处方和医生计费代码中的信息评估在电子病历中识别患有MS个体的准确性。
在CPP中识别MS的算法表现良好，灵敏度为91.5%，特异性为100%，阳性预测值为98.7%，阴性预测值为100%。在任何12个月时间范围内使用四次的MS特异性药物处方和医生计费代码340的加入，将灵敏度略微提高到92.3%，阳性预测值为97.9%。
电子病历中的数据可用于准确识别患有MS的患者。这项研究对临床医生、研究人员和政策制定者具有积极意义，因为它提供了在初级保健环境中识别MS患者队列以检查医疗质量的潜力。
