Using positive deviance to improve timely access in primary care
Bibliographic record
Abstract
Background Improving timely access in primary care is a continued challenge in many countries. We used positive deviance to try and identify best practices for achieving timely access in our primary care organisation in Toronto, Canada. Methods Semistructured interviews were used to identify practice strategies used by physicians who successfully maintained a low third next available appointment (TNA) (positive deviants, n=6). We then conducted a cross-sectional survey to understand the prevalence of identified promising practices among all physicians (n=70) in the practice. We used χ 2 testing to understand whether uptake of promising practices among survey respondents was different for those with a median TNA of 7 days or less vs a median TNA over 7 days. Results We identified seven promising practice strategies used by positive deviants: adjusting the appointment template based on demand; reviewing the appointment schedule in advance; max-packing of visits; using phone, email and secure messaging; customising care for complex patients; managing planned absences; and involving the interprofessional team. 65 of 70 physicians responded to the survey on promising practices. Uptake of the promising practices was variable among survey respondents. In general, we found no association between uptake of promising practices and median TNA. One exception was that those with a median TNA of 7 or less were more likely to review the schedule in advance to potentially mitigate a visit using phone/email (62% vs 31%, p=0.0159). Conclusion Promising practices used by a small group of physicians (‘positive deviants’) to maintain good access were generally not associated with timely access among a larger sample of physicians in the practice. Our findings highlight the difficulty of untangling physician practice style and its contribution to timely access in primary care.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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 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".