Improving diabetes care at an academic community-based family practice clinic in Ontario using a customized reminder system
Bibliographic record
Abstract
Context Patients with type two diabetes have sub-optimal achievement for all treatment targets in Canadian primary care (Nandiwada et al, 2023). Objective We evaluated whether the implementation of a customized Electronic Health Record (EHR) reminder would result in improvement in diabetes care at Health for All Family Health Team in Markham, Ontario Canada. Our AIM was to increase the proportion of active diabetic patients age 40+ on a statin medication with an up-to-date prescription from 65% to 75% by March 30, 2024. Study Design and Analysis Care Canvas reports distributed November 2022 showed 67% of our patients with diabetes 40 years and older had an up-to-date statin prescription on the chart with no significant improvement since 2017.We used a Pareto chart to assess underlying causes identified through chart review. Data from our EHR showed 65% of our active diabetic patients age 40+ had an up-to-date statin prescription (April 2023). We evaluated change over time in statin prescribing using a p-chart. Setting Care Canvas is an interactive web-based dashboard using EHR data that leverages the POPLAR (Primary care Ontario Practice-based Learning and Research Network) Data Safe Haven. Care Canvas reports were used for baseline data. EHR data was used for the intervention and subsequent analysis. Population studied Patients with diabetes 40 years and older in our family health team. Intervention The main change idea was a customized EHR reminder, sent to providers based on the reason identified through chart review.The team sent customized EHR messages twice: May/June 2023 and November/December 2023. Outcome measure was the proportion of active diabetic patients age 40+ with an up-to-date statin prescription. Results Date from April 1, 2024 showed 75% of active diabetic patients over 40 had an up-to-date statin prescription on the chart. No further interventions were performed. Data from September 26, 2024 showed 78.5 % of active diabetes over 40 had an up-to-date prescription showing not only was the change sustained but continue to improve. The most common reason identified through chart review was overdue prescriptions. Conclusion We designed a successful model for improvement for statin prescribing in diabetic patients 40 and older using a customized EHR reminder.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".