Recovery of a quality improvement project during the COVID pandemic.
Bibliographic record
Abstract
246 Background: Prior to COVID we undertook a QI project with the aim improving the documentation of a best possible medication history (BPMH) or medication reconciliation (MedRec) for patients initiating systemic therapy (ST) in ambulatory oncology, where care spans multiple providers and patients may be at increased risk of adverse drug events. While initial improvements were realized (16.7% and 3.9% increases for BPMH and MedRec, respectively), completion rates returned to baseline following the start of the COVID pandemic. Methods: Guided by the four-phase Quality Implementation Framework we sought to recover implementation of MedRec. We initially undertook a purposeful re-examination of the MedRec process (Phase 1) to identify barriers to conducting MedRec during COVID. This guided the tailored selection of Expert Recommendations for Implementing Change (ERIC) implementation strategies utilized during the successive phase of the project. During each phase the proportion of patients with documented BPMH or MedRec within 30 days of initiating ST out of those eligible was calculated. Results: Major barriers to conducting MedRec during COVID included reduced resources (time, human resources and physical resources), loss of dedicated staff, and change in workflow/ clinical models brought on by the introduction of virtual care. This informed our strategy to improve capacity to conduct MedRec (Phase 2) through the development and distribution of educational materials, revisions of professional roles, and creation of a new dedicated clinical team consisting of existing modified duty nurses to conduct MedRec. To support ongoing implementation (Phase 3), additional implementation strategies included the staged implementation scale-up, conduct of educational meetings/ outreach visits, facilitation, and provision of clinical supervision. The impact of each phase of implementation on BPMH and MedRec completion rates is summarized in Table. Conclusions: Recovery of a quality improvement intervention during COVID was realized through the utilization of a structured, implementation process model approach to identify and address barriers to implementation. Future work will focus on improvement of MedRec completion rates by clinicians, and on embedding processes into practice (Phase 4) to support sustainability of the intervention.[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 | 0.097 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".