Data Resource Profile: National electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN)

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. 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. 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). 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.

Health reforms in high-income countries increasingly aim to redesign primary care to improve the health of the population and the quality of health care services, and to address rising costs. Primary care improvements aim to provide patients with better access to care and develop more-integrated care systems through better communication and teamwork across sites of care, supported by health information technology and feedback to physicians on their performance. Our international survey of primary care doctors in Australia, Canada, France, Germany, the Netherlands, New Zealand, Norway, Switzerland, the United Kingdom, and the United States found progress in the use of health information technology in health care practices, particularly in the United States. Yet a high percentage of primary care physicians in all ten countries reported that they did not routinely receive timely information from specialists or hospitals. Countries also varied notably in the extent to which physicians received information on their own performance. In terms of access, US doctors were the most likely to report that they spent substantial time grappling with insurance restrictions and that their patients often went without care because of costs. Signaling the need for reforms, the vast majority of US doctors surveyed said that the health care system needs fundamental change.

The objective of this study was to assess the validity of electronic medical records-based diagnostic algorithms for 5 chronic conditions. A retrospective validation study using primary chart abstraction. A standardized abstraction form was developed to ascertain diagnoses of diabetes, hypertension, osteoarthritis, chronic obstructive pulmonary disease, and depression. Information about billing, laboratory tests, notes, specialist and hospital reports, and physiologic data was collected. An age-stratified random sample of 350 patient charts was selected from Kingston, Ontario, Canada. Approximately 90% of those charts were allocated to people aged ≥60 years. Three hundred thirteen patient records were included in the study. Patients' mean age was 68 years and 52% were women. High interrater reliability was indicated by 92% complete agreement and a κ statistic of 89.3%. The sensitivities of algorithms were 100% (diabetes), 83% (hypertension), 45% (osteoarthritis), 41% (chronic obstructive pulmonary disease), and 39% (depression). The lowest specificity was 97%, for depression. The positive predictive value ranged from 79% (depression) to 100%, and the negative predictive value ranged from 68% (osteoarthritis) to 100%. The diagnostic algorithms for diabetes and hypertension demonstrate adequate accuracy, thus allowing their use for research and policy-making purposes. The algorithms for the other 3 conditions require further refinement to attain better sensitivities.