Effective Integration of an eConsult Service into an Existing Referral Workflow Within a Primary Care Clinic
Bibliographic record
Abstract
Background: When implementing e-health solutions, effective integration into a clinic's existing processes is essential to facilitate adoption and sustained usage. Introduction: This article examines the effectiveness of adoption/utilization of an electronic consultation (eConsult) service by primary care providers (PCPs) using a “delegate model,” through which referral clerks manage requests on behalf of PCPs, thereby reducing PCPs' administrative burden. Materials and Methods: We conducted a retrospective cross-sectional study of all eConsults submitted between May 1, 2013, and December 31, 2017, by the Bruyère Academic Family Health Team (FHT), after the clinic implemented the service using a delegate model. We assessed system utilization, including monthly volume of submitted eConsults, requested specialties, and impact on PCP referral behavior based on the mandatory closeout surveys. We also conducted a subanalysis to compare the volumes of eConsults per provider between the FHT and all other providers. Results: The Bruyère Academic FHT submitted 3,233 eConsult cases. Volume increased 3.5 fold, from 285 in the first year to 1,016 in the last year. Active Bruyère Academic FHT providers (those who submitted ≥3 cases in 6 months) submitted a median of 25 eConsults (interquartile range [IQR]: 14.75–35.25) versus 14 (IQR 8–24) for all other active users. In 36% of cases, a referral was originally contemplated but avoided based on specialist advice. In 5% of cases, the referral was not originally contemplated but deemed appropriate by the PCP based on specialist advice. Discussion: Our findings show high levels of eConsult use in the clinic utilizing a delegate model, which persisted throughout the study period and was reported to significantly reduce the backlog of traditional referrals at the clinic. Conclusions: The integration of eConsult capability into existing clinic operations was successful in that it allowed the PCPs to request eConsult using a familiar process, avoiding the challenges associated with adopting a new and unfamiliar technology.
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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 | 0.024 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".