Evaluation of an electronic consultation service in psychiatry for primary care providers
Bibliographic record
Abstract
BACKGROUND: This study explores the effectiveness of an electronic consultation (eConsult) service between primary care providers and psychiatry, and the types and content of the clinical questions that were asked. METHODS: This is a retrospective eConsult review study. All eConsults directed to Psychiatry from July 2011 to January 2015 by Primary care providers were reviewed. Response time and the amount of time reported by the specialist to answer each eConsult was analyzed. Each eConsult was also categorized by clinical topic and question type in predetermined categories. Mandatory post-eConsult surveys for primary care providers were analyzed to determine the number of traditional consults avoided and to gain insight into the perceived value of eConsults. RESULTS: Of the 5597 eConsults, 169 psychiatry eConsults were completed during the study period. The average response time for a specialist to a primary care provider was 2.3 days. Eighty-seven percent of clinical responses were completed by the psychiatrist in less than 15 min. The primary care providers most commonly asked clinical questions were about depressive and anxiety disorders. 88.7% of PCPs rated the eConsult service a 5 (excellent value) or 4. CONCLUSIONS: This study indicates that an eConsult psychiatry service has tremendous potential to improve access to psychiatric advice and expand the capacity to treat mental illness in primary care. Future research may include follow-up with PCPs regarding the implementation of specialist advice.
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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 | 0.013 | 0.092 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".