Safer prescribing for older patients: Preliminary findings from a large project in Canada
Bibliographic record
Abstract
Abstract Background Polypharmacy is common and deleterious for elders. We devised a QI-research collaboration, Structured Process Informed by Data, Evidence and Research (SPIDER) aimed at improving medication appropriateness for complex older patients. Methods Single-arm mixed methods feasibility study followed by a 2-arm (Intervention vs Usual Care) pragmatic cluster randomized controlled trial. Setting: seven Practice-Based Research Networks across Canada. Participants: Seven practices (each with two physicians) per arm per region will be recruited. patients 65+ years taking 10+ different medications identified by participating physicians' EMR. Intervention: three key elements: 1) Practice teams participating in QI Learning Collaboratives; 2) Support of coaching/practice facilitation; and 3) validated EMR data for feedback. Main Outcome Measures: Reduction of potentially inappropriate prescriptions (PIPs) measured using EMR data. Results Thirty-three physicians from ten family health teams/practices and one nurse practitioner and three family physicians from a community health centre in Toronto were recruited. All teams have accessed coaching support, reflecting high engagement. Teams were given flexibility in developing deprescription strategies and action plans that fit for the local context. Review and validation of patient cohort identified by the EMR were time-consuming for some, depending on the size of the cohort and the patterns practices have been using to capture medication information in their EMR. Engaging pharmacists in the medication review and deprescription process greatly alleviated the burdens on the physicians. Conclusions SPIDER appeared to be feasible and has the potential to enhance safer prescription for complex older patients. A flexible and contextually adaptable design helped with increased uptake. Having access to embedded QI and data support and working with collaborating pharmacists may enhance the sustainability of the approach. Key messages A QI-Research collaboration to improve care for complex elders is feasible. If the RCT shows improvement, this may provide an argument for QI support in primary care.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".