Improving management of hypersensitivity reactions: A BC Cancer-Victoria quality improvement initiative.
Bibliographic record
Abstract
230 Background: Hypersensitivity reactions (HSR) are a documented, predictable side effect of multiple chemotherapy agents. Reactions negatively affect the patient experience, increase the amount of chair time, nursing and physician resources, may result in the omission of a potentially effective cancer management tool from a patient’s treatment plan and could potentially result in death. BC Cancer is a Health Care Organization with 6 cancer centres across British Columbia, Canada. Guideline(GL)s have been developed at BC Cancer to support clinicians to manage reactions acutely and reduce the risk of reactions with subsequent cycles. A recent audit identified that the GLs were not always being followed at the Victoria Centre. Our goal was to encourage physician and nursing staff to follow GLs, which we hypothesized would result in decreased rates of HSR. Methods: Our aim was to decrease HSR to < 5% of doses delivered within 1 year at BC Cancer-Victoria. We engaged stakeholders (nursing, physicians, pharmacy, clerical staff and administration). Our change ideas improved adherence to GLs by focusing on: physician attendance and documentation, written orders for rescue medication, and rate of infusion of the chemotherapy drug rechallenge. Our interventions included: two physician-education sessions, one nursing education session, daily huddles, pre-printed order development for management of the reaction (PPOA) and prophylaxis for subsequent cycles (PPOB), and a modified clinic flow. All interventions were introduced and underwent modifications through PDSA cycles. Our family of measures were: Outcome: number of reactions, percent of reactions per dose given. Process: percent of PPO use per reaction, physician attendance and notes dictated per reaction. Balancing: physician and nursing satisfaction. We analyzed the data using quality improvement run charts and control charts. Results: After the start of our initiative, our total number of reactions displayed special cause variation, and a shift in the baseline from a mean of 11.27 HSR per month to 7.526. This change was reflected in the percentage of reactions per doses given which fell from 3.1% to 1.9%. Average percentage of dictated notes per reaction increased from 55% to 64%. Physician attendance per reaction also showed special cause variation with the average increasing from 57% to 90%. PPOA and PPOB use both increased over time. Nursing and Physician satisfaction data will also be presented. Conclusions: Our successful initiative has resulted in HSR management which more closely reflects GLs, including increased physician attendance and notes, and clear consistent written orders detailed on PPO A and B. This has led to decreased HSRs at our site, resulting in decreased resource use and increased patient safety and quality. This has provincial implications as there is the potential to spread this initiative to other BC Cancer sites.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".