Addressing challenges in telephone triage for outpatient oncology care: A data-centred digital routing solution.
Bibliographic record
Abstract
591 Background: As cancer care shifts to a predominately outpatient model, there is growing recognition that optimizing communication between patients and their care providers between scheduled visits can improve efficiency and patient outcomes. Princess Margaret Cancer Centre (PM), which sees over 80,000 patients annually, operates with many parallel telephone triage lines as the primary means of patients-provider communication. When a patient needs to contact their care team, they are instructed to leave a voicemail and wait for a response. In a high-volume setting this model presents many challenges to the patient experience, provider efficiency, and patient safety. The purpose of this pilot project was to design, implement, and assess the feasibility of a digital channel for patients to reach their care team. Methods: We used a human-centred design approach to develop a new digital triage service. First, we analyzed manually collected triage call data over a one-year period to understand the most common patient queries of the telephone triage service. Through co-design, a new digital-triage service was conceptualized and prototyped. Subsequently, we tested a data-centred digital routing algorithm for patient concerns and symptoms. To evaluate implementation and assess feasibility, we collected usage data, satisfaction surveys, and conducted staff shadowing and interviews. We used PDSA cycles to refine user experience and clinical workflows. Results: Patient adoption of digital triage increased continuously over 9 months, with volumes surpassing voicemail after four months: over the last four months digital triage has encompassed on average 63% of the total request volumes. The response rate to patient surveys was 21%. Patients indicated being very satisfied or satisfied (74.4%), neutral (6.7%), and dissatisfied or very dissatisfied (18.9%). Interviews with leading triage staff suggest the digital channel improves triage efficiency and redirecting/escalation by providing structured and complete patient queries. Conclusions: The pilot highlights the feasibility and acceptability of a digital-triage service in a comprehensive cancer care setting. By leveraging digital tools and real-time data, PM addressed the challenges of telephone triage, enabling a more efficient process to respond to patients’ needs, while demonstrating high adoption and patient satisfaction. Subsequent work will expand the use of digital triage to all disease sites across the cancer centre, and create foundational workflows and technology for proactive care models, such as Remote Patient Monitoring.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".