Engaging Physicians in the Use of Electronic Medical Records
Bibliographic record
Abstract
23 Although this article could be applied to physicians across all specialties, our focus is on the engagement of Canadian primary care physicians in the use of Electronic Medical Record (EMR) systems. The objective of our article is to suggest a methodology that can be followed in order to assist in the physician’s adoption of EMR. It would be presumptuous to state that we have all the answers when it comes to an issue as complex as the adoption of EMR by physicians; however, there are broad principles that can provide an organized and logical approach towards the implementation of these systems. Before we begin to describe the methodology, let’s have a quick look at where the Canadian healthcare system is when it comes to EMR. Despite Canada being essentially a single-payer system (in contrast to the much more complex U.S. healthcare system), there has been a relatively poor uptake and use of EMR systems in primary care, in contrast to European countries such as The Netherlands and Denmark. Ninety-five percent or more of all primary care physicians in Finland, the Netherlands, Sweden, Germany and the United Kingdom use computers in their practices. (The countries where the largest proportions of general practitioners are using electronic medical records are Sweden (90%); The Netherlands (88%); Denmark (62%); The United Kingdom (58%); Finland (56%); and Austria (55%).). The average for all 15 EU countries is 80%. The apathy toward electronic medical record systems in Canada has created a significant challenge. What can be done, and is it the physician or the system that is at fault? The government is starting to do its part as funding is being committed federally and provincially to assist the primary care physician in moving towards electronic medical record systems. This is being done with the hope that the studies, money and talk will enhance the uptake of the technology. However, all the work is overshadowed by a national need for data communication standards and the approval of electronic signatures before wider use of technology becomes more commonplace. Fortunately, the development of standards and legislative approval of electronic signatures is currently taking place at both a provincial and federal level. Some examples of the funding being committed at federal and provincial levels to support the uptake and increased use of EMR systems by physicians include the POSP project in Alberta and the Ontario Family Health Network. In addition, on a national scale, the Engaging Physicians in the Use of Electronic Medical Records
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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 | 0.123 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".