Development of a process map for the delivery of virtual clinical pharmacy services at Odette Cancer Centre during the COVID-19 pandemic
Bibliographic record
Abstract
Virtual methods have been innovatively utilized to provide clinical and supportive care to patients with cancer. Oncology pharmacists have been actively involved in this movement, in order to minimize patient contact and decrease the risk of viral transmission for this high-risk group. In response to COVID-19 restrictions, the Odette Cancer Centre pharmacy modified the delivery of clinical pharmacy services (CPS), including medication histories and patient education/counseling, to a remote telephone-based model. Process maps were created to visualize workflow before and after the pandemic. Process metrics were tracked over a 6-week period. From March 25th to May 1st, 2020, 202 best-possible medication histories and baseline assessments were completed; 149 of these (74%) were completed remotely. For medication therapy counsels, 72 of 199 were completed remotely (36%). Despite workflow disruptions caused by the pandemic, these results demonstrate that clinical pharmacy service levels could be maintained by incorporating remote delivery approaches without significant investment in resources. Challenges included acceptance by patients and lack of technology to support system-level processes. Further research to develop, refine, and individualize virtual clinical pharmacy care models will help to consolidate the role of these approaches in the post-COVID-19 era.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".