Increased access to pediatric specialist healthcare using eConsult: A retrospective observational cohort and case–control study
Bibliographic record
Abstract
Objectives: Primary care practitioners (PCPs) report that using the Champlain BASE™ eConsult service (eConsult) averts one-third of face-to-face (FTF) specialist referrals, however, there are concerns about duplication of services and adverse patient outcomes. Following an eConsult, we evaluated patient healthcare utilization and associated treatment costs. Methods: Retrospective cohort study (2014 to 2018) of patients (<18 years old) for whom an eConsult visit averted a FTF specialist referral. Patients were linked to provincial health administrative databases and hospital electronic medical records for healthcare use for the same diagnosis and specialty for the 18 months following the eConsult. Concurrently, a retrospective case-control study compared utilization and costs between an eConsult versus a matched FTF visit for the same diagnosis. We also assessed PCP satisfaction. Results: In follow-up, <5% of the study cohort of eConsult patients (n = 242) later accessed the healthcare system for the identical diagnosis and specialty type. FTF visits generate more frequent outpatient visits (12.6 times more [95% CI: 2.28 to 69.66, P = 0.002]) and higher costs compared to eConsult visits. There were no hospital admissions or deaths in patients with eConsult. PCPs (98%) described eConsult as an excellent service. Conclusions: Using eConsult is associated with <5% of patients subsequently having a FTF visit for the same reason. Matched FTF visits generated more healthcare utilization and higher costs compared with eConsult. eConsult in pediatrics is safe and can minimize FTF specialist visits in elective cases and increase capacity, towards a more efficient and cost-effective healthcare system.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".