Abstract:BackgroundWith the introduction and implementation of a variety of government programs and policies to encourage adoption of electronic medical records (EMRs), EMRs are being increasingly adopted in North America. We sought to evaluate the completeness of a variety of EMR fields to determine if family physicians were comprehensively using their EMRs and the suitability of use of the data for secondary purposes in Ontario, Canada.MethodsWe examined EMR data from a convenience sample of family physicians distrib… Show more
“…At the time of the study the data in EMRALD were contributed by 83 primary care physicians who had been using their EMR for at least 2 years and whose data met quality and completeness standards. 3 EMRALD patients and physicians are similar to all Ontario patients and physicians for most characteristics. 3 A random sample of 7500 adult patients aged 20 years or older as of December 31, 2010 was drawn from 73,014 adult patients of EMRALD physicians.…”
Identifying patients with AF can be done using administrative data, and the algorithm can be used to assess trends in disease burden over time and patterns of care in large populations.
“…At the time of the study the data in EMRALD were contributed by 83 primary care physicians who had been using their EMR for at least 2 years and whose data met quality and completeness standards. 3 EMRALD patients and physicians are similar to all Ontario patients and physicians for most characteristics. 3 A random sample of 7500 adult patients aged 20 years or older as of December 31, 2010 was drawn from 73,014 adult patients of EMRALD physicians.…”
Identifying patients with AF can be done using administrative data, and the algorithm can be used to assess trends in disease burden over time and patterns of care in large populations.
“…The physician level factor is years the physician has been using EMRs. It was previously shown that it takes approximately 2 years for a physician to adequately populate the EMR records for their practice 12. Patient level factors examined were age and sex.…”
Section: Methodsmentioning
confidence: 99%
“…However, research networks, such as the Canadian Primary Care Sentinel Surveillance Network11 and the Electronic Medical Record Administrative data Linked Database (EMRALD)12 in Canada, and the Electronic Pediatric Research in Office Settings (ePROS) network in the United States, have attempted to move EMR research forward by acting as an intermediary between primary care providers and researchers. Multiple studies have been published using these data sources to estimate both child and adult obesity rates 13 – 15.…”
Background Electronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However, the use of EMRs in research has been impeded by lack of standardisation of EMRs systems, data access and concerns about the quality of the data. Objectives The study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes. Methods A cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Record Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the World Health Organization Growth Standards and Reference. Results A total of 54,964 children were identified from EMRALD. Overall, 93% had at least one complete set of growth measurements to calculate a body mass index (BMI) z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.
“…The representativeness of EMRALD patients and physicians, in addition to the quality and comprehensiveness of EMRALD data, has been previously found to be generally reflective of the Ontario population. 28,29 Young adults and people with lower socioeconomic status are slightly under represented in EMRALD compared with the general Ontario population; however, this is likely characteristic of the types of people that see a physician and not anything specific to EMRALD patients. Relative to all Ontario physicians, EMRALD physicians practise more in rural locations and fewer are foreign-trained physicians.…”
Section: Measurement Of Quality Indicatorsmentioning
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