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Background: Hypertension is a common chronic condition affecting nearly a quarter of Canadians. Hypertension surveillance in Canada typically relies on administrative data and/or national surveys. Routinely-captured data from primary care electronic medical records (EMRs) are a complementary source for chronic disease surveillance, with longitudinal patient-level details such as sociodemographics, blood pressure, weight, prescribed medications, and behavioural risk factors. As EMR data are generated from patient care and administrative tasks, assessing data quality is essential before using for secondary purposes. This study evaluated the quality of primary care EMR data from one province in Canada within the context of hypertension surveillance., Methods: We conducted a cross-sectional, descriptive study using primary care EMR data collected by two practice-based research networks in Alberta, Canada. There were 48,377 adults identified with hypertension from 53 clinics as of June 2018. Summary statistics were used to examine the quality of data elements considered relevant for hypertension surveillance., Results: Patient year of birth and sex were complete, but other sociodemographic information (ethnicity, occupation, education) was largely incomplete and highly variable. Height, weight, body mass index and blood pressure were complete for most patients (over 90%), but a small proportion of outlying values indicate data inaccuracies were present. Most patients had a relevant laboratory test present (e.g. blood glucose/glycated hemoglobin, lipid profile), though a very small proportion of values were outside a biologically plausible range. Details of prescribed antihypertensive medication, such as start date, strength, dose, frequency, were mostly complete. Nearly 80% of patients had a smoking status recorded, though only 66% had useful information (i.e. categorized as current, past, or never), and less than half had their alcohol use described; information related to amount, frequency or duration was not available., Conclusions: Blood pressure and prescribed medications in primary care EMR data demonstrated good completeness and plausibility, and contribute valuable information for hypertension epidemiology and surveillance. The use of other clinical, laboratory, and sociodemographic variables should be used carefully due to variable completeness and suspected data errors. Additional strategies to improve these data at the point of entry and after data extraction (e.g. statistical methods) are required.

Objective: To describe the process for linking electronic medical record (EMR) and administrative data in Alberta and examine the advantages and limitations of utilising linked data for hypertension surveillance., Methods: De-identified EMR data from 323 primary care providers contributing to the Canadian Primary Care Sentinel Surveillance Network (CPCSSN) in Alberta were used. Mapping files from each contributing provider were generated from their EMR to facilitate linkage to administrative data within the provincial health data warehouse. Deterministic linkage was conducted using valid personal healthcare number (PHN) with age and/or sex. Characteristics of patients and providers in the linked cohort were compared with population-level sources. Criteria used to define hypertension in both sources were examined., Results: Data were successfully linked for 6307 hypertensive patients (96.2% of eligible patients) from 49 contributing providers. Non-linkages from invalid PHN (n=246) occurred more for deceased patients and those with fewer primary care encounters, with differences due to type of EMR and patient EMR status. The linked cohort had more patients who were female, >60 years and residing in rural areas compared to the provincial healthcare registry. Family physicians were more often female and medically trained in Canada compared to all physicians in Alberta. Most patients (>97%) had ≥1 record in the registry, pharmacy, emergency/ambulatory care and claims databases; 44.3% had ≥1 record in the hospital discharge database., Conclusion: EMR-administrative data linkage has the potential to enhance hypertension surveillance. The current linkage process in Alberta is limited and subject to selection bias. Processes to address these deficiencies are under way., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients' health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database., Methods: EMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016-2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey., Results: Among those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category., Conclusion: Multiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

Purpose: The Canadian Primary Care Sentinel Surveillance Network (CPCSSN) is Canada's first national chronic disease surveillance system based on electronic health record (EHR) data. The purpose of this study was to develop and validate case definitions and case-finding algorithms used to identify 8 common chronic conditions in primary care: chronic obstructive pulmonary disease (COPD), dementia, depression, diabetes, hypertension, osteoarthritis, parkinsonism, and epilepsy., Methods: Using a cross-sectional data validation study design, regional and local CPCSSN networks from British Columbia, Alberta (2), Ontario, Nova Scotia, and Newfoundland participated in validating EHR case-finding algorithms. A random sample of EHR charts were reviewed, oversampling for patients older than 60 years and for those with epilepsy or parkinsonism. Charts were reviewed by trained research assistants and residents who were blinded to the algorithmic diagnosis. Sensitivity, specificity, and positive and negative predictive values (PPVs, NPVs) were calculated., Results: We obtained data from 1,920 charts from 4 different EHR systems (Wolf, Med Access, Nightingale, and PS Suite). For the total sample, sensitivity ranged from 78% (osteoarthritis) to more than 95% (diabetes, epilepsy, and parkinsonism); specificity was greater than 94% for all diseases; PPV ranged from 72% (dementia) to 93% (hypertension); NPV ranged from 86% (hypertension) to greater than 99% (diabetes, dementia, epilepsy, and parkinsonism)., Conclusions: The CPCSSN diagnostic algorithms showed excellent sensitivity and specificity for hypertension, diabetes, epilepsy, and parkinsonism and acceptable values for the other conditions. CPCSSN data are appropriate for use in public health surveillance, primary care, and health services research, as well as to inform policy for these diseases., (© 2014 Annals of Family Medicine, Inc.)

Objective: To understand how remuneration and care setting affect the implementation of electronic medical records (EMRs)., Design: Semistructured interviews were used to illicit descriptions from community-based family physicians (paid on a fee-for-service basis) and from urban, hospital, and academic family physicians (remunerated via alternative payment models or sessional pay for activities pertaining to EMR implementation)., Setting: Small suburban community and large urban-, hospital-, and academic-based family medicine clinics in Alberta. All participants were supported by a jurisdictional EMR certification funding mechanism., Participants: Physicians who practised in 1 or a combination of the above settings and had experience implementing and using EMRs., Methods: Purposive and maximum variation sampling was used to obtain descriptive data from key informants through individually conducted semistructured interviews. The interview guide, which was developed from key findings of our previous literature review, was used in a previous study of community-based family physicians on this same topic. Field notes were analyzed to generate themes through a comparative immersion approach., Main Findings: Physicians in urban, hospital, and academic settings leverage professional working relationships to investigate EMRs, a resource not available to community physicians. Physicians in urban, hospital, and academic settings work in larger interdisciplinary teams with a greater need for interdisciplinary care coordination, EMR training, and technical support. These practices were able to support the cost of project management or technical support resources. These physicians followed a planned system rollout approach compared with community physicians who installed their systems quickly and required users to transition to the new system immediately. Electronic medical records did not increase, or decrease, patient throughput. Physicians developed ways of including patients in the note-taking process., Conclusion: We studied physicians' procurement approaches under various payment models. Our findings do not suggest that one remuneration approach supports EMR adoption any more than another. Rather, this study suggests that stronger physician professional networks used in information gathering, more complete training, and in-house technical support might be more influential than remuneration in facilitating the EMR adoption experience.