Safe and reliable inpatient chemotherapy administration: Impact of an ambulatory oncology pharmacist.
Bibliographic record
Abstract
389 Background: Given their extensive adverse effect profiles, any errors involving chemotherapy medications are at high risk for causing patient harm. Analysis of inpatient chemotherapy related incidents at Trillium Health Partners, a Canadian academically-affiliated health system, between May 2019 and April 2021 revealed a number of key drivers for incidents. These included gaps in coordination of care, provider ordering practices and communication between different clinical teams. Methods: A dedicated inpatient oncology pharmacist role, staffed by an ambulatory care oncology pharmacist, was implemented from August 2021 - July 2022. The primary goal was to achieve a 50% reduction in inpatient incidents per 100 doses of chemotherapy by July 2022. Secondary goals included improving interdisciplinary communication and coordination of care between providers especially at transitions in care. Three data elements were collected: number and severity of incidents, types of pharmacist interventions and provider satisfaction. The clinical and organizational impact of the pharmacists interventions were assessed by a multidisciplinary team including an oncologist, a nurse and a pharmacist using the validated CLEO tool. Results: Incidents per 100 doses of administered inpatient chemotherapy decreased by 80%, with a significant shift from actual incidents to reportable circumstances. Chemotherapy coordination, chemotherapy dose adjustments and adapting orders for inpatient use were the most common pharmacist interventions. The interventions had major and moderate clinical impact and a positive organizational impact as assessed by the CLEO tool. Feedback through provider satisfaction surveys showed a 52% increase in satisfaction with the inpatient chemotherapy process. Qualitative feedback indicated the specialized oncology pharmacist role facilitated collaboration and improved communication and coordination of care between inpatient and ambulatory settings. In addition, the implementation of this role facilitated safe provision of inpatient chemotherapy on non-oncology inpatient units. Conclusions: Implementing a dedicated ambulatory oncology pharmacist in an inpatient setting facilitated safe administration of inpatient chemotherapy and improved multidisciplinary coordination, allowing for a seamless patient experience. This demonstrates the value of utilizing the niche outpatient clinical knowledge of oncology pharmacists, applied to inpatient roles to coordinate care in corresponding specialized inpatient populations. This model can be extrapolated to other cancer centers to maximize the utilization of specialized skills and knowledge leading to system-wide impact.[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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".