A Unique Way to Axe the Fax – Using Business Automation Workflow to expedite eReferral adoption, bridging eReferral and Fax: Pilot Study (Preprint)
Bibliographic record
Abstract
BACKGROUND It is estimated that 88% of Ontario physicians still use fax technology to share patient information. Transitioning to eReferral has been shown to have numerous benefits, but the major barrier to eReferral adoption is the need to have both sending/receiving clinicians on the same platform to enable information sharing. The traditional onboarding process takes time and effort. An innovative method is required to improve eReferral adoption by bridging the gap between eReferral senders and referral receivers still using fax. OBJECTIVE To explore the technological feasibility of leveraging a Business Automation Workflow (BAW) platform to connect the digital (eReferral) and non-digital referral platform (Fax), enabling eReferral senders to use eReferral to send referrals to clinicians using Fax, with the goal of improving clinician experience. METHODS An eReferral via eFax solution was developed and evaluated on the BAW platform to connect the eReferral platform and the clinicians using fax. A selected number of fax receivers were identified and enabled on the eReferral platform as eFax receivers. Sending clinicians initiated eFaxes through the familiar eReferral workflow, with eFaxes transmitted to BAW and delivered to the target receiver via fax. Retry and reminder logic were built to improve the user experience. If the eFax failed after all the retries, a message was sent to the sending clinician through the eReferral platform explaining the failure reason. The appointment information was entered into the eReferral platform by the sending clinicians to trigger patient email notifications. Surveys and focused interviews were conducted to collect clinicians’ feedback. RESULTS From May 2022 to December 2023, 224 eFax receivers were enabled on the platform, processing 4,504 eFaxes for 4,132 unique patients and 843 unique senders across the province. Nearly 70% of patients received email notifications, with 19% of appointment details entered in the eReferral platform. On average, eFax referrals contained 5.6 pages, with a minimal 0.7% exceeding 30 pages. Initially, fax service retries were disabled to observe delivery error rates. This resulted in a 37.7% fax failure. However, after implementing new retry logic in March 2023, the failure rate dropped significantly to 9.9% and 98.7% of eFaxes were successfully delivered after automatic retries. Clinician feedback revealed a positive impact on sending clinicians' experience, maintaining their familiar workflow while accommodating fax-reliant receivers who can gradually transition to the new platform at their own pace. CONCLUSIONS This project demonstrates the potential of the BAW platform to bridge the gap between fax and eReferral systems. It minimizes disruption for sending clinicians while allowing receiving clinicians using fax to incrementally adopt the new platform. This technology can significantly expedite eReferral adoption by reducing reliance on the adoption of the eReferral by receiving clinics, ultimately enhancing the experience for both clinicians and patients.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".