The use of technology to improve the delivery process of ambulatory chemotherapy at the Odette Cancer Centre (OCC).
Bibliographic record
Abstract
290 Background: The OCC manages over 24,000 chemotherapy patient visits/year. Over time the number of patients, complexity of treatment and staffing requirements increase. Barriers to efficient delivery of chemotherapy include: same day (as clinic visit) treatment, missing MD orders, manual appointment scheduling and poor communication between chemo nurses and pharmacists during drug preparation. These issues were addressed by (1) non-same day chemotherapy, (2) MD orders expected day prior to treatment, (3) development of two web-based tools for chemotherapy scheduling and communication. Methods: Two independent process reviews were undertaken confirming system inefficiencies. The move to non-same day chemotherapy was implemented. An electronic Chemotherapy Appointment Reservation Manager (CHARM) was developed and linked to the computerized physician order entry (CPOE) system, and reminders were sent to MDs with outstanding orders 72, 48, and 24 hours pre-chemotherapy. The tool was developed to facilitate staff communication during chemotherapy preparation. Data relating to each process improvement was collected pre- and post-implementation. Results: Over 300 of the more than 400 chemotherapy regimens were reviewed for nurse assessment, medication preparation, and in-chair infusion times, with the results used to build the algorithms for CHARM. With the move to non-same day chemotherapy over 80% of patients are treated on a non-clinic day compared with 40% pre-implementation. With CHARM the average number of patients booked/day went from 68 to 100, a 47% increase. Currently, 90% of physician orders are entered by 2pm the day before treatment. Use of the communication tool resulted in an 89% reduction in phone calls between the nurses and pharmacy. 36% of patients started treatment +/- 30 min of scheduled time in both time peroids. Conclusions: OCC introduced several innovative approaches to improving the safe delivery of chemotherapy to cancer patients. Patient volumes have increased while communication around care delivery has improved. The approach allows ongoing research and development to improve workflow and communication.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".