Progress in the Enhanced Use of Electronic Medical Records: Data From the Ontario Experience
Bibliographic record
Abstract
BACKGROUND: This paper describes a change management strategy, including a self-assessment survey tool and electronic medical record (EMR) maturity model (EMM), developed to support the adoption and implementation of EMRs among community-based physicians in the province of Ontario, Canada. OBJECTIVE: The aim of our study was to present an analysis of progress in EMR use in the province of Ontario based on data from surveys completed by over 4000 EMR users. METHODS: The EMM and the EMR progress report (EPR) survey tool clarify levels of capability and expected benefits of improved use. Maturity is assessed on a 6-point scale (0-5) for 25 functions, across 7 functional areas, ranging from basic to more advanced. A total of 4214 clinicians completed EPR surveys between April 2013 and March 2016. Univariate and multivariate descriptive statistics were calculated to describe the survey results. RESULTS: Physicians reported continual improvement over years of use, perceiving that the longer they used their EMR, the better patient care they provided. Those with at least two years of experience reported the greatest progress. CONCLUSIONS: From our analyses at this stage we identified: (1) a direct correlation between years of EMR use and EMR maturity as measured in our model, (2) a similar positive correlation between years of EMR use and the perception that these systems improve clinical care in at least four patient-centered areas, and (3) evidence of ongoing improvement even in advanced years of use. Future analyses will be supplemented by qualitative and quantitative data collected from field staff engagements as part of the new EMR practice enhancement program (EPEP).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.006 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".