Ask the eConsultant: Improving access to haematology expertise using an asynchronous eConsult system
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
Introduction The Champlain BASE (Building Access to Specialists through eConsultation) eConsultation service was designed to address the limited access to specialist care in Canada, which can lead to long waiting times and, subsequently, negative patient outcomes. Our primary objective was to perform an in-depth analysis of the use, content, and perceived value of haematology electronic consults (eConsults) submitted by primary care providers (PCPs) to the eConsult service. Methods We conducted a cross-sectional study using descriptive statistics to examine post-eConsult surveys for PCPs and other collected data including PCP designation, time for specialist to complete the eConsult, specialist response time, perceived value of the eConsult by the PCP, and the need for a face-to-face referral following the eConsult. A medically-trained author reviewed all haematology eConsults from April 2011 to January 2015, and categorized them by clinical topic and question type using validated taxonomies. Results Haematology accounted for 436 out of 5601 (7.8%) total eConsults, making it the third most popular service utilized. In 66% of haematology eConsults, a face-to-face consultation was not needed. Anaemia, neutropenia, and hyperferritinemia were the most common clinical queries. Most eConsult question types concerned the management of haematological disorders or the interpretation of laboratory tests. Most eConsults were answered within three days, using less than 15 minutes of the specialists' time. PCPs highly valued the service. Discussion This initiative increases access to haematology care and has the potential to reduce the long waiting times for non-urgent traditional consultation, along with the benefit of cost savings to the healthcare system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it