Next-day chemotherapy scheduling: a multidisciplinary approach to solving workload issues in a tertiary oncology center
Bibliographic record
Abstract
Introduction/background information. In 1998, increasing patient volumes and workloads related to ambulatory chemotherapy began to result in delays in the outpatient clinics, laboratory, pharmacy and the chemotherapy administration areas. The various departments that were being impacted met to review the situation and propose a solution. Program description. The team proposed that a next-day chemotherapy schedule be implemented, whereby patients would have their laboratory and physician appointment on one day and their chemotherapy administered on the next day. Originally, this was turned down by senior management, however, a year later the same issues resurfaced and the next-day chemotherapy scheduling was once again proposed and approval received to implement. Program implementation. A timetable was created and chemotherapy administration appointments were scheduled based on the available timeslots. The goal was to have the majority of patients scheduled for chemotherapy administration on the day following their appointment with the physician. Patients who would be exceptions to the next-day scheduling system were identified and education of staff and patients regarding the change was completed. Resistance from patients and staff was encountered and managed through continued education and reinforcement regarding the reasons for the change. Pharmacy issues/evaluation. Pharmacy conducted a follow-up timing study and the results supported that the timetable change was successful in improving pharmacy’s ability to prepare the chemotherapy for the patient’s appointment time. Pharmacy operational issues had to be worked through, however, overall the change was found to be positive. Conclusion. The implementation of a next-day chemotherapy administration schedule has resulted in improved efficiencies for pharmacy and nursing and a decrease in waiting times for patients to receive their chemotherapy.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".