A comparison of referral patterns to a multispecialty eConsultation service between nurse practitioners and family physicians: The case for eConsult
Bibliographic record
Abstract
PURPOSE: To explore referral patterns of nurse practitioners (NPs) and family physicians (FPs) using an electronic consultation (eConsult) service, and assess their perspectives on the service's value to their patients and themselves. DATA SOURCES: A mixed methods study including a cross-sectional analysis of utilization data drawn from all eConsults completed from April 15, 2011 to September 30, 2014, and a content analysis of NP survey responses completed from January 1 to September 30, 2014. CONCLUSIONS: A total of 4260 eConsults were included in the cross-sectional analysis (3686 from FPs and 574 from NPs). In our sample, NPs directed more cases to dermatology and fewer cases to cardiology and neurology (p < .0001) than did FPs, and were more likely to report that an eConsult led to new advice for a new or additional course of action (62.8% vs. 57.5%) and less likely to report it resulted in an avoided referral (35.5% vs. 41.8%, p = .005). NPs reported slightly higher levels of perceived value of eConsults for their patients and themselves. IMPLICATIONS FOR PRACTICE: Differences in use and impact of eConsult exist between NPs and FPs. NPs value the service highly for their patients and themselves. The service reduces potential inequities related to outdated payment and scope of practice policies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".