Improving medication reconciliation in ambulatory cancer care.
Bibliographic record
Abstract
224 Background: Adverse drug events are common in ambulatory oncology where care spans multiple providers and medication documentation is often poor. We undertook a QI project with the aim of having 30% of patients have a best possible medication history (BPMH) or medication reconciliation (MedRec) documented within 30 days of starting systemic therapy. Methods: An Electronic Medical record-Integrated Tool (EMITT) was developed to facilitate documentation. 2 Plan-Do-Study-Act (PDSA) cycles have been completed to date; PDSA 1 consisted of piloting EMITT in 3 clinics run by physician champions. PDSA 2 which consisted of expanding pharmacy support and addition of a 4th clinic was impacted by care changes related to COVID. The proportion of patients with BPMH/MedRec documented in EMITT was calculated monthly for each period (PDSA 1, PDSA 2 pre-COVID and PDSA 2 post-COVID). The balancing measure of time to complete an entry was evaluated through a time motion study. Results: Between 9/9/2019 and 31/5/2020, 9.4% (233/2488) of patients had BPMH/MedRec completed; Table shows proportion of patients by month. BPMH and MedRec were most frequently performed by pharmacists followed by pharmacy students and nurses. On average, it took 5.5 minutes to complete an entry (n = 10; median number of medications per patient = 12.3). Conclusions: BPMH was documented more often than MedRec. While some usage was sustained, the changes to care as a result of COVID-19 negatively impacted ambulatory medication reconciliation. Future PDSA cycles will involve engaging patients in MedRec and extending EMITT to all ambulatory cancer clinics where medication management is a major component of care. [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 | 0.018 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".