Improving Documentation of Oral Chemotherapy at a Community Cancer Center
Bibliographic record
Abstract
PURPOSE: Safe administration of oral chemotherapy is a complex process that represents a potential threat to patient safety. Clear documentation of the plan of care for patients receiving oral chemotherapy can improve patient safety by ensuring complete health information is available to the health care team. METHODS: We undertook a rapid-cycle improvement project to improve documentation of oral chemotherapy by increasing the number of components of an oral chemotherapy care plan (as outlined by American Society of Clinical Oncology and Oncology Nursing Society) documented in the medical record before starting a new oral chemotherapy drug. Three improvement cycles were implemented, including: introduction of a standardized nursing flow sheet, use of computerized physician order entry for oral chemotherapy prescribing, and a review of computerized physician order entry to ensure all oral chemotherapy regimens were included. RESULTS: Our intervention resulted in a meaningful and sustained improvement in the number of components of oral chemotherapy care plans documented in the medical record, from a mean of 67% (eight of 12 components) to a mean of 92% (11 of 12). CONCLUSION: We are hopeful that this improvement project will enhance patient safety by improving communication within the health care team regarding the details of the chemotherapy care plan.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".