eConsultations to Infectious Disease Specialists: Questions Asked and Impact on Primary Care Providers’ Behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Since 2010, the Champlain BASE (Building Access to Specialist Advice through eConsultation) has allowed primary care providers (PCPs) to submit clinical questions to specialists through a secure web service. The study objectives are to describe questions asked to Infectious Diseases specialists through eConsultation and assess impact on physician behaviors. METHODS: eConsults completed through the Champlain BASE service from April 15, 2013 to January 29, 2015 were characterized by the type of question asked and infectious disease content. Usage data and PCP responses to a closeout survey were analyzed to determine eConsult response time, change in referral plans, and change in planned course of action. RESULTS: Of the 224 infectious diseases eConsults, the most common question types were as follows: interpretation of a clinical test 18.0% (41), general management 16.5 % (37), and indications/goals of treating a particular condition 16.5% (37). The most frequently consulted infectious diseases were as follows: tuberculosis 14.3% (32), Lyme disease 14.3% (32), and parasitology 12.9% (29). Within 24 hours, 63% of cases responded to the questions, and 82% of cases took under 15 minutes to complete. In 32% of cases, a face-to-face referral was originally planned by the PCP but was no longer needed. In 8% of cases, the PCP referred the patient despite originally not planning to make a referral. In 55% of cases, the PCP either received new information or changed their course of action. CONCLUSIONS: An eConsult service provides PCPs with timely access to infectious disease specialists' advice that often results in a change in plans for a face-to-face referral.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 it