Primary Care Clinician Adherence to Specialist Advice in Electronic Consultation
Bibliographic record
Abstract
PURPOSE: Electronic consultation (eConsult) services can improve access to specialist advice. Little is known, however, about whether and how often primary care clinicians adhere to the advice they receive. We evaluated how primary care clinicians use recommendations conveyed by specialists via the Champlain BASE (Building Access to Specialists through eConsultation) eConsult service and how eConsult affects clinical management of patients in primary care. METHODS: This is a descriptive analysis based on a retrospective chart audit of 291 eConsults done between January 20, 2017 and August 31, 2017 at the Bruyère Family Health Team, located in Ottawa, Canada. Patients' charts were reviewed until 6 months after specialist response for the following main outcomes: implementation of specialist advice by primary care clinicians, communication of the results to the patients, method, and time frame of communication. RESULTS: Primary care clinicians adhered to specialist advice in 82% of cases. Adherence ranged from 62% to 93% across recommendation categories. Questions asked by primary care clinicians related to diagnosis (63%), management (27%), drug treatment (10%), and procedures (1%). Recommendations of the eConsult were communicated to patients in 79% of cases, most often by face-to-face visit (38%), telephone call (32%), or use of the patient portal (9%). Communication occurred in a median of 5 days. CONCLUSIONS: We found little evidence of barriers to implementing specialist advice with use of eConsult, which suggests recommendations given through service were actionable. With a high primary care clinician adherence to specialist recommendations and primary care clinician-to-patient communication, we conclude that eConsult delivers good-quality care and improves patient management.
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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 | 0.025 | 0.185 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".