Bringing provincial improvements to oral chemotherapy prescribing through co-ordinated regional initiatives.
Bibliographic record
Abstract
107 Background: Oral chemotherapy delivery is complex, making safe medication practices a high priority. Cancer Care Ontario, the provincial government agency responsible for continually improving cancer services in Ontario, undertook a jurisdiction-wide quality improvement initiative to ensure that all oral chemotherapy drugs are prescribed using Computerized Prescriber Order Entry (CPOE) or standardized Pre-printed Orders (PPO). The initiative was further enabled by changes to the provincial funding approach that flows facility funding for oral chemotherapy delivery. Methods: All 35 facilities prescribing chemotherapy in Ontario across 14 regions implemented strategies to work towards the common aim of reducing handwritten/verbal oral chemotherapy prescribing to zero by June 30th, 2015. Baseline audits were completed between Sept-Nov 2014; repeat audits were performed between Mar-May 2015. Each facility reported the number of patients that received an oral chemotherapy prescription, and the method of prescribing. Results: At baseline, 30% of audited prescriptions across the province were handwritten or verbal, which decreased to 9% by June 2015. Improvements were seen in thirteen of the 14 regions. Thirteen out of 35 facilities met the aim of 0 handwritten/verbal orders, with an additional 16 facilities seeing an improvement. Alignment with funding mechanisms, an early physician engagement strategy, and education of key stakeholders on CPOE systems were identified as key enablers to implementation. Conclusions: Though the goal of zero handwritten/verbal prescriptions was not met by all facilities, the initiative encouraged a change in implementing safe prescribing practices for oral chemotherapy. Further audits will assess that the gain was sustained and that the provincial goal is achieved. This initiative is part of a larger strategy to standardize care for systemic treatment patients and promote a culture of safety in hospitals. [Table: see text]
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".