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Identifying individuals with multiple sclerosis in an electronic medical record


Background — The increasing use of electronic medical records (EMRs) presents an opportunity to efficiently evaluate and improve quality of care for individuals with MS.

Objectives — We aimed to establish an algorithm to identify individuals with MS within EMRs.

Methods — 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.

Results — 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%.

Conclusions — 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.



Krysko KM, Ivers NM, Young J, O'Connor P, Tu K. Mult Scler. 2015; 21(2):217-24. Epub 2014 Jun 12.

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