Use of performance data to drive process change and improve patient wait time for chemotherapy treatment.
Bibliographic record
Abstract
223 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 designed that includes workflow communication between pharmacy and nursing. A multidisciplinary team created a value stream map of the process. Rate limiting steps and key milestones in the delivery process were identified. CHARM timestamps stages in the chemotherapy process, from nurse approval to pharmacy verification, medication processing and medications received on the chemotherapy unit. Extracted data was used to create a weekly report to monitor key performance indicators (KPIs). Results: An analysis of six months of data led to the establishment of KPIs and targets. Our baseline targets were established as pharmacy turnaround time (AR) < 1 hour, pharmacy verification (HP)< 13 minutes, medication processing (PR) <45 minutes and chemotherapy ready (CR) +/- 30 minutes of the patient appointment time. Since April 2012, KPIs have been reported. Over a four month period (81 clinic days and 5,920 appointments) the median AR time was 1:02 (hh:min), HP was 00:12 and PR was 00:43. The KPI targets were met AR 46%, HP 52% and PR 51% and CR 29% of the time. The data infrastructure now drives the quality improvement initiatives. An early strategy implemented queuing theory, to process orders by appointment rather than approval time, resulting in no change in median AR time, but a decrease of seven minutes AR range. Conclusions: Quality improvement is hinged on measurement and evaluation. The OCC has successfully implemented a continuous quality improvement data infrastructure that monitors quality of ambulatory chemotherapy delivery. These data describe pharmacy and nursing processes. Further work to improve the delivery process and data infrastrucutre 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 | 0.015 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".