Design and Implementation of a Physician Coaching Pilot to Promote Value-Based Referrals to Specialty Care
Bibliographic record
Abstract
INTRODUCTION: Referral rates to specialty care from primary care physicians vary widely. To address this variability, we developed and pilot tested a peer-to-peer coaching program for primary care physicians. OBJECTIVES: To assess the feasibility and acceptability of the coaching program, which gave physicians access to their individual-level referral data, strategies, and a forum to discuss referral decisions. METHODS: The team designed the program using physician input and a synthesis of the literature on the determinants of referral. We conducted a single-arm observational pilot with eight physicians which made up four dyads, and conducted a qualitative evaluation. RESULTS: Primary reasons for making referrals were clinical uncertainty and patient request. Physicians perceived doctor-to-doctor dialogue enabled mutual learning and a pathway to return joy to the practice of primary care medicine. The program helped physicians become aware of their own referral data, reasons for making referrals, and new strategies to use in their practice. Time constraints caused by large workloads were cited as a barrier both to participating in the pilot and to practicing in ways that optimize referrals. Physicians reported that the program could be sustained and spread if time for mentoring conversations was provided and/or nonfinancial incentives or compensation was offered. CONCLUSION: This physician mentoring program aimed at reducing specialty referral rates is feasible and acceptable in primary care settings. Increasing the appropriateness of referrals has the potential to provide patient-centered care, reduce costs for the system, and improve physician satisfaction.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".