Provider-to-provider telephone consultations for cardiovascular concerns: A retrospective cohort study evaluating safety and efficacy
Bibliographic record
Abstract
Introduction: General internal medicine (GIM) clinics receive many referrals for cardiovascular concerns without clinical red flags. These can be addressed through provider-to-provider telephone consultations that support primary care providers (PCPs) in managing patient concerns. This modality of care demonstrates how health systems can manage rising referral volumes and complexity of patient care needs while empowering PCPs. We aimed to evaluate the safety and efficacy of provider-to-provider telephone consultations between GIM and PCPs for cardiovascular diagnoses. Methods: We conducted a retrospective cohort study of patients referred to GIM at a Canadian tertiary-care centre for whom the referral was resolved through a provider-to-provider telephone consultation between 2017 and 2019. Data collected included clinical diagnoses, previous cardiac assessments, consults and recommendations, and whether patients had in-person encounter(s) within 6 months of the consult. Results: In 2 years, 130 telephone consultations were completed. Triage was informed by 84% of patients having had previous cardiac testing and 29% having seen a cardiologist previously. Diagnoses managed included hypertension (>20%), atrial fibrillation (15%), and palpitations (12%). Over 90% of patients did not require direct specialist care within 6 months. Only 2 patients (<2%) may have benefited from an upfront in-person assessment by GIM. Discussion: Telephone consultations between providers avoided the need for direct patient care in >90% of cardiovascular referrals without clinical red flags. We observed no associated adverse outcomes. Telephone consults improved wait times to access GIM care. Formalizing telephone communication programs between internists and PCPs provides a low-barrier, easily implementable access point for specialty 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".