Optimizing patient scheduling for ambulatory chemotherapy.
Bibliographic record
Abstract
199 Background: The Odette Cancer Centre (OCC) manages more than 24,000 chemotherapy visits annually. The delivery process is complex and patients have significant wait times for treatment. The OCC was faced with improving this process with no data infrastructure to support continuous quality improvement. Methods: An electronic scheduling manager, Chemotherapy Appointment Reservation Manager (CHARM) was built in house to improve scheduling logic and optimize bed and chair utilization. The chemotherapy unit has recently undergone renovations to change the staff-to-patient ratio and chair distribution. At baseline, a nurse is assigned to 4 chairs in a “pod” without adjustment for patient and chemotherapy intensity variation. An interprofessional team participated in a Kaizen event to create a Value Stream Map of the scheduling process. Scheduling logic considerations were identified to better match nursing and chair resources to patient appointment times. An analysis was performed to evaluate the distribution of patients throughout the chemotherapy unit by time of day, and day of week to identify opportunities to align the schedule with nursing and pharmacy resources. Results: The mean number of patients seen per day was 85 with a range of 65 to 105. 80% of patients are scheduled before 11:30 (the unit operations 08:30 to 18:00). The mean number of patients assigned to a pod was 8 with a range of 3 to 15. Unit performance on days of >95 patients was observed to be poorest. Load levelling techniques were established to reduce the range of patients booked per day throughout the week. New considerations for scheduling are: maximum 12 patients per nurse per pod per day, maximum 3 new patients per nurse per pod per day, maximum 10 clinical trials per day, and maximum 50% of patients scheduled before 11:30 per day. Conclusions: Matching the patient schedule to the nursing and pharmacy resources of the unit is critical to efficient and safe chemotherapy delivery. A Plan-Do-Study-Act is scheduled for September 2013 to implement the scheduling changes and evaluate the impact of the new logic on unit operations. Further work to improve the delivery process and pharmacy medication processing is ongoing.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".