Can the Use of Cardiology Medical Record to Deliver Educational Intervention Improve Care? On Behalf of TAPP Program: Thinking Approach Towards Physician Support in Patient Management
Bibliographic record
Abstract
Background: Despite clear and concise practice guidelines, strategies for lowering LDL-C are often poorly adopted in clinical practice, and many patients fail to reach guideline-recommended levels despite physician education and quality improvement programs. We studied whether physician focussed, guideline-based practice level educational intervention can improve lipid lowering management. Methods: Cardiologists or internal medicine specialists from the province of Ontario, Canada who were using a cardiology specific EMR (CEREBRUM, WELL Health Technologies Corporation) were invited to participate. Practice level data of patients with history of acute coronary syndromes (ACS) and lipid profile were studied. Physicians were alerted when patients in their practice were not treated according to recommendations. The primary endpoint was proportion of patients achieving the recommended LDL-C level of below 1.8 mmol/L. Results: Of the invited 378 specialists, 178 agreed to participate and shared their practice involving 7,683 ACS patients who were 70.4 ± 10.3 years of age and 27.2% were women. Overall, 57.7% of patients had LDL-C < 1.8 mmol/L at the start of the program (1.84 ± 0.87 mmol/L) and 63.0% (1.75 ± 0.79 mmol/L) at the end of the program (p<0.0001). With respect to the lipid lowering therapy, statin therapy was used in 52.9% of patients at the start of the program and increased to 72.1% at the end (p<0.0001). The use of ezetimibe increased from 12.6% to 19.0% (p<0.0001) and the use of PCSK9i from 1.2% to 2.4% (p<0.0001). Conclusion: The results indicate the feasibility of using EMR as a platform to deliver educational intervention and overcoming treatment inertia and improving LDL-C lowering.
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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 | 0.016 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".