A novel PA-led urgent care model for medical oncology patients at a large academic cancer center.
Bibliographic record
Abstract
37 Background: Oncology patients are high users of the emergency department (ED), which often results in hospital admissions for management of cancer symptoms or cancer treatment toxicities. Interventions such as urgent care (UC) models can decrease such visits, and help improve patient management, health care utilization, and patient experience. Sunnybrook Health Sciences Centre, a large tertiary care hospital in Toronto, Canada has a high volume of medical oncology ED visits (average 4 per day) with about 50% admitted for management. Methods: A novel physician-assistant (PA) led and physician supervised UC model was developed to assist in medical oncology patient phone triage, assessment, and management of cancer or treatment related issues that would otherwise have been sent to the ED by the oncology team. There were two phases: 1) due to COVID, the patients were managed in a dedicated stream primarily using space and nursing in the ED, 2) a dedicated UC clinic with nursing support was opened for these patients. Results: In phase 1, there were 424 referrals over 24 months; 84% would have otherwise been sent through the usual ED process. 26% of patients were managed with PA navigation outside the UC program in other hospital settings. Of the 204 patients formally treated in the UC stream, 67.7% were discharged home. At 48 hours, 89% of discharged patients were stable or improved; this was 80% at 14 days, and 17.3% came back to the ED or were admitted within 14 days of the UC visit. In phase 2, there have so far been 214 referrals over 5 months; 83.6% would have otherwise been sent to the ED. Of the patients who were assessed, 77.9% were discharged home. Outcomes of these patients are being collected. The top 3 patient issues managed during both phases were: fever, pain, and dyspnea. Fifteen patient telephone surveys were completed, and 93.3% were either satisfied or highly satisfied with their UC experience. Conclusions: A novel PA-led triage and management model for urgent medical oncology patient issues was found in initial phase to be feasible and effective with streamlined care through the ED. Once a dedicated UC clinic was opened, referral volumes increased, and a high rate of ED diversion, patient discharge, and effective care was continued. Patients were also highly satisfied. Several ongoing process and outcome measures are being evaluated to help expand the scope and impact of this resource.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".