INTRODUCTION
Chronic diseases are largely managed in the primary health care setting.1–3 The adoption of electronic medical records (EMRs) in primary care has been associated with expectations that these applications would support quality improvement efforts for chronic conditions.4,5
However, the implementation of EMRs has not been consistently associated with better care.6–10 Improvements in care require management of data to support quality improvement activities.11,12 Gaps exist in terms of the EMR software used to manage data, the availability of data management personnel in primary care and the quality of data in the EMRs.
EMR applications were built to help record care for individual patients rather than for analyzing data to manage quality for practice populations.12,13 These applications often have rudimentary reporting, data export and analytic capabilities.14 In addition, running large queries can tax servers, slowing them down and interfering with daily clinical activities.15
In primary care, EMR-based measurement has often relied on the efforts of individual physicians in querying their own applications. Physicians may be often be too busy with daily patient care and may not have time to undertake these activities.16,17 Primary care teams may be able to reallocate some of the work of measuring and reporting care and outcomes to nonclinical team members such as data managers.18–20
The quality of data in EMRs continues to present challenges.21–24 Diagnostic coding may be missing.25 Free text may be used instead of structured data and data may be entered in inconsistent fields.26–34 EMRs often require structured or coded data to enable automated recalls, point of care reminders, practice population quality improvement activities and computerized decision support.4,11,35,36
A recent analysis of 11.5 million primary care electronic records in the U.S. found significantly better quality of care for patients when a coded diagnosis of diabetes was present in the problem list.25 Lack of standardization and coding in EMRs is associated with challenges in benchmarking and comparisons, which are important activities for primary care clinical quality improvement efforts.37,38 As a result, there have been calls to improve data and implement consistent coding for chronic conditions in primary care.39
Changes in the organisation of primary care in Ontario, Canada
Primary care in Ontario, Canada, has recently evolved through the formation of interdisciplinary family health teams (FHTs)40 and the adoption of EMRs. Currently, almost 3000 family physicians are working in 240 FHTs and are serving 3 million patients or 25% of Ontario’s population.41,42 Eighty-five percent of Ontario’s family physicians report using an EMR.43
Evidence to date on improvement in FHT performance is limited,44,45 and until recently, FHTs had not been required to systematically report quality of care.
As of 2013, the Excellent Care for All Act 2010 in Ontario46 mandated the development and public reporting of quality improvement plans by FHTs. The Ministry of Health and Long Term Care of Ontario recently funded quality improvement and decision support specialist (QIDSS) positions to provide analytic services to FHTs.47
Our objectives were to describe the adoption of data management activities in a primary care organisation in Ontario, Canada, and to evaluate effects on coding to support the formation of registries of specific chronic conditions.