Improving care for elderly patients living with polypharmacy: protocol for a pragmatic cluster randomized trial in community-based primary care practices in Canada
Bibliographic record
Abstract
BACKGROUND: Elders living with polypharmacy may be taking medications that do not benefit them. Polypharmacy can be associated with elevated risks of poor health, reduced quality of life, high care costs, and persistently complex care needs. While many medications could be problematic, this project targets medications that should be deprescribed for most elders and for which guidelines and evidence-based deprescribing tools are available. These are termed potentially inappropriate prescriptions (PIPs) and are as follows: proton pump inhibitors, benzodiazepines, antipsychotics, and sulfonylureas. Implementation strategies for deprescribing PIPs in complex older patient populations are needed. METHODS: This will be a pragmatic cluster randomized controlled trial in community-based primary care practices across Canada. Eligible practices provide comprehensive primary care and have at least one physician that consents to participate. Community-dwelling patients aged 65 years and older with ten or more unique medication prescriptions in the past year will be included. The objective is to assess whether the intervention reduces targeted PIPs for these patients compared with usual care. The intervention, Structured Process Informed by Data, Evidence and Research (SPIDER), is a collaboration between quality improvement (QI) and research programs. Primary care teams will form interprofessional Learning Collaboratives and work with QI coaches to review electronic medical record data provided by their regional Practice Based Research Networks (PBRNs), identify areas of improvement, and develop and implement changes. The study will be tested for feasibility in three PBRNs (Toronto, Montreal, and Edmonton) using prospective single-arm mixed methods. Findings will then guide a pragmatic cluster randomized controlled trial in five PBRNs (Calgary, Winnipeg, Ottawa, Montreal, and Halifax). Seven practices per PBRN will be recruited for each arm. The analysis will be by intention to treat. Ten percent of patients who have at least one PIP at baseline will be randomly selected to participate in the assessment of patient experience and self-reported outcomes. Qualitative methods will be used to explore patient and physician experience and evaluate SPIDER's processes. CONCLUSION: We are testing SPIDER in a primary care population with complex care needs. This could provide a widely applicable model for care improvement. TRIAL REGISTRATION: Clinicaltrials.gov NCT03689049 ; registered September 28, 2018.
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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 | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.006 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.042 | 0.005 |
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".