Pilot to scale: Implementing a digital triage solution across a large comprehensive cancer centre.
Bibliographic record
Abstract
225 Background: Princess Margaret Cancer Centre (PM) sees over 80,000 patients annually and primarily operates using an outpatient model. Historically patients use a phone/voicemail triage service to communicate with their care team between scheduled visits. Many patient queries lack critical information necessary for effective screening, assessment, and processing. In turn leading to delays or missed calls that negatively impact patient experience, system efficiency, and safety. To address these challenges, we piloted a digital triage service and subsequently implemented in sixteen clinics. This project evaluated initial adoption, usage, care patterns and satisfaction in the first 5 months post-implementation. Methods: We used human-centred service design to develop the digital triage service within the EMR (Epic). Implementation strategies to scale across all outpatient clinics included leadership buy-in, co-design with key stakeholders, a phased implementation approach, and improvement cycles. To evaluate initial adoption, usage and care patterns, we used EMR system data. To assess patient satisfaction, we surveyed patients who used the service during the first three months. We conducted semi-structured interviews with staff to understand their experience integrating digital triage into their workflow. Results: Following the go-live period, patients sent an average of 1096 digital requests per month, representing 16% of all triage requests (phone + digital). The most common requests were symptom management (36% of nursing requests) and appointment change requests (40% of administrative requests). The algorithm automatically routed 93% of requests to the most appropriate care team member, compared to 74% pre-implementation. When asked if they were satisfied with their experience, most patients agreed or were neutral (69% and 10%, respectively), while 21% disagreed. In semi-structured interviews, nurses and administrative staff described improved efficiency by having triage requests integrated within the EMR, while have challenges related to prioritizing requests across multiple channels (e.g. digital, phone, others). Conclusions: Digital triage is foundational for better cataloguing and understanding our patients' unmet needs at home and between visits, enabling a continuous learning process to understand when and why people struggle throughout their care journey. Ongoing analyses will explore disease site-specific trends, compare phone to digital triage, and identify differences between users of each method. The data will inform further improvement cycles on the build and workflows, while enabling appropriate resourcing of this critical service and shifts towards proactive care.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".