Advancing Primary Care Use of Electronic Medical Records in Canada
Bibliographic record
Abstract
In 2010, the federal government's Economic Action Plan funded Canada Health Infoway to co-invest with provinces, territories, and health care providers in electronic medical records (EMRs) in primary care. The goal is to help improve access to care, quality of health services, and productivity of the health system, as well as to deliver economic benefits. The decision to fund EMRs was consistent with a long-term framework for digital health established in consultation with stakeholders across the country, spurred by analysis demonstrating the economic impact of such investments and data on Canada's low rate of EMR use in primary care compared with other countries. The decision reflected widespread public and stakeholder consensus regarding the importance of such investments. EMR adoption has more than doubled since 2006, with evaluations identifying efficiency and patient care benefits (e.g., reduced time managing laboratory test results and fewer adverse drug events) in community-based practices. These benefits are expected to rise further as EMR adoption continues to grow and practices gain more experience with their use. En 2010, le Plan d'Action Economique du gouvernement fédéral a doté financièrement Inforoute Santé du Canada pour aider les provinces, les territoires et les producteurs de soins à investir dans l'utilisation des dossiers médicaux électroniques (DME) en soins primaires. L'objectif est d'améliorer l'accès aux soins, la qualité des services délivrés et la productivité d'ensemble du système de soins, ainsi que de soutenir l'économie. Cette décision de financer les DME découle logiquement d'un engagement de long terme en faveur de la santé digitale, établi après consultations des principaux acteurs à travers le pays, et motivé par une analyse établissant l'impact économique de tels investissements ainsi que des données sur le faible taux d'utilisation des DME en soins primaire relativement à d'autres pays. La décision reflète un consensus partagé parmi les acteurs et le public sur le fait que de tels investissements sont importants. Le taux d'adoption des DME a plus que doublé depuis 2006 et les exercices d'évaluations pointent des gains d'efficience ou pour les patients (par exemple, les délais de retour des examens de laboratoire pourraient être réduits, ainsi que la fréquence des événements iatrogènes médicamenteux) en médecine de famille. Ces gains devraient croître avec l'augmentation de l'adoption des DME, à mesure que les praticiens deviennent plus expérimentés dans leur utilisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".