Streamlining acute oncology (AO) at the Princess Margaret (PM) Cancer Centre: An AO centralized referral and triage.
Bibliographic record
Abstract
76 Background: Acutely ill cancer patients (pts) often present with medical needs that cannot be managed in a standard oncology clinic. Several AO clinics have been established at the PM to support pts and promote emergency department (ED) avoidance, these include Urgent Care Clinic (UCC), Day Oncology (DO) and Radiation RN clinic (RNC). We recently developed a process to integrate three distinct AO clinics, to optimize pt flow and improve pt/provider experience. Methods: A prospective (2-week) audit was completed to collect key metrics and a survey circulated to collect data on referrer/AO team satisfaction with current process. A multidisciplinary/interprofessional working group was established to review audit data, complete process mapping, and establish a centralized pathway for triage and referral. This included developing a novel EPIC referral form that integrated oncology and ED specific fields (vitals, CEDIS complaints and treatment times) and creating a RN/physician assistant (PA) clinical triage team. Pre-evaluation from AO staff and referrer experience utilized to assess staff perspectives. Post implementation (3-month) audit used to analyze key metrics and post AO staff and referrer surveys were repeated. A pt experience survey has also been developed for longitudinal monitoring of pt satisfaction and creating a RN/PA clinical triage team. Results: The AO centralized process was implemented June 2024 with post-implementation evaluation being completed in Sept 2024. After implementation, both referring providers and AO staff reported improved satisfaction with overall process: referral process (43 to 85% and 36 to 78%); clarity of acceptance criteria (29 to 85% and 21 to 67%), ease of transfer into AO (21 to 76% and 36 to 67%) and communication between OP and AO (50 to 85% and 50 to 89% and 21 to 56%). While referrer reported improvement in necessity to direct to ED transfer (29 to 42%) and AO staff reported ease of transfer out of AO (27 to 44%) and communication between AO clinics (50 to 89%). The average turnaround time from referral to triage was 24 min (mean 19 min) with average monthly volume of 375 referrals within the first 3 months post implementation. The time from AO inpatient bed request to admission remained consistent at approximately 3 hours pre and post implementation. Conclusions: The introduction of a dedicated, interprofessional triage team for AO at the PM has streamlined referrals for AO support, improving staff satisfaction with the referral process amongst referrers and AO providers. Teams report reduced administrative burden and improved ability to focus on direct pt care. We have not observed an increase in pt volumes managed in AO clinics related to limitations in AO clinic capacity due to boarding of admitted pts. Further work is on-going to improve inpatient flow and thereby increase AO clinic capacity, in addition to collecting data on pt experience.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".