Implementation of medication reconciliation in outpatient cancer care
Bibliographic record
Abstract
BACKGROUND: Medication reconciliation (MedRec) is a process where providers work with patients to document and communicate comprehensive medication information by creating a complete medication list (best possible medication history (BPMH)) then reconciling it against what patient is actually taking to identify potential issues such as drug-drug interactions. We undertook an environmental scan of current MedRec practices in outpatient cancer care to inform a quality improvement project at our centre with the aim of 30% of patients having a BPMH or MedRec within 30 days of initiating treatment with systemic therapy. METHODS: We conducted semi-structured interviews with key stakeholders from 21 cancer centres across Canada, probing on current policies, and barriers and facilitators to MedRec. Guided by the findings of the scan, we then undertook a quality improvement project at our cancer centre, comprising six iterative improvement cycles. RESULTS: Most institutions interviewed had a process in place for collecting a BPMH (81%) and targeted patients initiating systemic therapy (59%); however, considerable practice variation was noted and completion of full MedRec was uncommon. Lack of resources, high patient volumes, lack of a common medical record spanning institutions and settings which limits access to medication records from external institutions and community pharmacies were identified as significant barriers. Despite navigating challenges related to the COVID-19 pandemic, we achieved 26.6% of eligible patients with a documented BPMH. However, uptake of full MedRec remained low whereby 4.7% of patients had a documented MedRec. CONCLUSIONS: Realising improvements to completion of MedRec in outpatient cancer care is possible but takes considerable time and iteration as the process is complex. Resource allocation and information sharing remain major barriers which need to be addressed in order to observe meaningful improvements in MedRec.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".