Browsing by Author "Drummond, Neil A."

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    Enhancing Primary Care Electronic Medical Record (EMR) Data in Alberta by Quality Assessment, Data Processing, and Linkage to Administrative Data
    (2020-07-01) Garies, Stephanie; Quan, Hude; Williamson, Tyler S.; Drummond, Neil A.; McBrien, Kerry Alison
    The growth of electronic medical record (EMR) systems in healthcare settings has created opportunities for EMR data to be reused for secondary purposes. Since EMR data are generated from clinical and administrative processes, the suitability for other uses (e.g. surveillance or research) is questionable. Assessing data quality is important for understanding the database contents, identifying potential limitations or biases, and determining how ‘fit for purpose’ the data are. This thesis focused on evaluating and improving the quality of primary care EMR data in Alberta. Data quality, which is highly contextual, was examined from the perspective of use for hypertension surveillance, as hypertension is a prevalent chronic condition associated with poor health outcomes and high cost implications. The first part of this thesis involved developing a comprehensive description of EMR data capture, extraction, and processing by the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Alberta. The second section presented a data quality assessment using CPCSSN data elements relevant to hypertension surveillance. The third part explored multiple imputation and a pattern-matching algorithm for improving smoking status records in the EMR data. Lastly, EMR and administrative data for a cohort of hypertensive patients were linked and described. The CPCSSN process documentation and data quality assessment created novel, useful, and comprehensive information for data users. CPCSSN data appear to be suitable for hypertension surveillance, though caution is warranted for several variables of inconsistent quality. Multiple imputation improved completeness of patient smoking statuses, but the lack of an appropriate external reference source made confirming accuracy difficult. The pattern-matching algorithm demonstrated high accuracy for categorizing smoking status; however, it missed classifying 24% of patients. Lastly, EMR data for 6,307 hypertensive patients were successfully linked to five administrative databases. Although this linked sample is relatively small and may be subject to selection bias (limiting the generalizability for surveillance purposes), the cohort could be useful for health outcomes research or validating elements in the EMR or administrative databases. This work has informed the development of more efficient processes for EMR-administrative linkages. Data quality assessment outcomes will be made available to inform various types of CPCSSN data users.