Improving the quality of oral cancer drug delivery across a health system.
Bibliographic record
Abstract
184 Background: Oral systemic therapy (ST) presents unique care delivery challenges. Gaps in patient education and monitoring for patients on oral ST delivery are well documented and can result in decreased adherence or increased toxicity. Within Ontario, cancer care is provincially coordinated through Ontario Health- Cancer Care Ontario (OH-CCO), but locally implemented by 14 Regional Cancer Programs (RCPs). Using a centrally coordinated, but regionally implemented quality improvement (QI) approach, we aimed to improve the quality of oral ST delivery across Ontario by enabling the use of patient specific monitoring plans to optimize treatment adherence and toxicity management. Methods: Between 2018 and 2020 a 2 year focused QI project was undertaken. A suite of 19 quality measures were developed to measure different quality domains for oral ST delivery including treatment plan documentation, patient education, toxicity/adherence monitoring and toxicity outcomes. In year 1, all regions used the suite of quality measures to establish baseline performance and develop a QI plan using rapid cycle improvement methodology to improve performance in at least 1 domain based on regional gaps and priorities. Projects were implemented and evaluated during year 2. OH-CCO provided QI coaching through dissemination of standardized QI tools, a monthly discussion forum and project specific feedback. At the end of year 2, a post-implementation evaluation was performed for each region. Results: 15 centers participated, representing all RCPs across Ontario. The participating centers implemented QI projects focused on treatment plan documentation (N = 3), patient education (N = 10) and toxicity/adherence management (N = 5), with some focusing on multiple domains. All centers reported an improvement in at least 1 domain (see Table). Key enablers identified include engagement with a multi-disciplinary team and the use of technology, while barriers include lack of onsite dispensing pharmacy. Future work will continue to focus on quality of oral ST delivery and better pharmacy integration. Conclusions: Through a centrally coordinated, locally implemented QI project, improvement in quality of oral ST care was achieved across Ontario. This model of QI focus has the potential to be adaptable across health systems. [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 | 0.020 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".