The Ontario Electronic Consultation (eConsult) Service: Cross-sectional Analysis of Utilization Data for 2 Models (Preprint)
Bibliographic record
Abstract
BACKGROUND The Ontario electronic consultation (eConsult) service allows a primary care provider (PCP) to access specialist advice through 2 models: the direct-to-specialist (DTS) model, where PCPs select a specialist from a directory, and the Building Access to Specialists Through eConsultation (BASE)–managed specialty service, where PCPs choose a specialty group and are assigned a specialist from a qualified pool based on availability. OBJECTIVE The aim of this study is to examine patterns of use between the 2 models of eConsult delivery. METHODS We conducted a cross-sectional analysis of utilization data collected from eConsults completed between October 2018 and September 2019. Cases were grouped based on the model used for submission (ie, BASE or DTS). Each model was assessed for the number of cases over time, specialty distribution, proportion resulting in new or additional information, impact on PCPs’ decisions to refer, and billing time. RESULTS PCPs submitted 26,121 eConsults during the study period. The monthly case volume increased by 43% over the duration of the study, primarily in the BASE model (66% compared to 6% for DTS). PCPs were able to confirm a course of action that they originally had in mind in 41.4% (6373/15,376) of BASE cases and 41.3% (3363/8136) of DTS cases and received advice for a new or additional course of action in 54.7% (8418/15,376) of BASE cases and 56.3% (4582/8136) of DTS cases. A referral was originally contemplated but avoided in 51.3% (7887/15,376) of BASE cases and 53.3% (4336/8136) of DTS cases, originally contemplated and still needed in 19.4% (2986/15,376) of BASE cases and 17.7% (1438/8136) of DTS cases, and neither originally contemplated nor needed in 21.7% (3334/15,376) of BASE cases and 21.9% (1781/8136) of DTS cases. CONCLUSIONS Both eConsult models had strong uptake. Use patterns varied between models, with the majority of growth occurring under BASE, but survey responses showed that both models provided similar outcomes in terms of new information offered and impact on decision to refer.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".