Feasibility Assessment for Implementation of Heart Failure Clinical Caremaps using Electronic Medical Records in Primary Practice
Bibliographic record
Abstract
Objectives: The primary aim of this project is to evaluate the impact and level of use of Electronic Medical Records (EMRs) by family physicians (FPs) specifically with respect to HF management. This study provides pilot work towards successful implementation of HF clinical caremaps in EMRs to support decision making for FPs.Methods: A survey questionnaire was sent to 207 FPs from which 42 (20%) replies were received. The survey included questions on demographic information of the FP's practice, specifics about HF patients and their management, EMR use and whether they have improved management in HF patients. Results: Among the 42 FPs who responded, 39 (93%) practice in the urban area of Hamilton and each have over 10 confirmed HF patients at their family practices, supporting the need for proper management of HF at the primary care level. FPs expressed concerns about difficulty in treating HF preserved vs. systolic HF, in managing HF patients with renal insufficiency and difficulty in the use of beta blockers. There was no consensus on whether EMRs have helped in improving the management of HF patients.Conclusions: There is a perceived need for management tools which can be integrated into EMRs to provide decision making support for FPs in managing HF. Tools such as caremaps may help provide optimal care in managing HF patients as per the Canadian Cardiovascular Society guidelines.
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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 | 0.128 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".