Toward a Common Goal: Improving Safety of Oral Chemotherapy Prescribing Practices at a Jurisdictional Level
Bibliographic record
Abstract
PURPOSE: Extending the safety agenda from parenteral to oral chemotherapy was identified as a provincial improvement priority in the 2014-2019 Cancer Care Ontario (CCO) Systemic Treatment Provincial Plan. Elimination of handwritten prescriptions for oral chemotherapy was one of the specific goals and led to a provincial quality improvement (QI) initiative involving systemic treatment facilities across 14 regional cancer programs. METHODS: The initiative was centrally organized by CCO but locally implemented by the regional partners. CCO provided templates and tools, such as preprinted orders (PPOs), project charters, and an evaluation plan, and facilitated cross-jurisdictional knowledge sharing and exchange. Regions had flexibility in determining their local implementation strategies and were responsible for conducting chart audits to evaluate implementation success. Each participating hospital completed 3 audits-at baseline, immediately after implementation (audit 1), and 1 year later (audit 2)-using either a clinic-based or an outpatient pharmacy-based assessment. RESULTS: Thirty-five facilities providing systemic treatment participated. At baseline, the provincial average for the use of computerized physician order entry (CPOE) or PPOs for prescribing oral chemotherapy was 71%. After implementation of the QI initiative, the provincial average for the use of CPOE or PPO increased to 91% at audit 1 and 95% at audit 2. CONCLUSION: Although not all facilities met the goal of 100% CPOE or PPO compliance, the QI initiative led to improvement in safe prescribing practices for oral chemotherapy. A coordinated QI approach between a central decision maker and local partners can be an effective strategy to encourage high-quality cancer care and promote a culture of safety across a jurisdiction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".