Clinical Integration and Evaluation of the STrategic Optimization of Prescription Medication Use in Patients on HemoDialysis (STOPMed-HD) Intervention
Bibliographic record
Abstract
Background: People undergoing hemodialysis (HD) take an average of 12 medications daily, with 93% prescribed at least one potentially inappropriate medication or dose. Polypharmacy is commonly defined as taking 5 or more medications per day; however, it can also refer to the use of inappropriate medication choices and doses. Polypharmacy can lead to serious health consequences, including drug-drug interactions, falls, and hospitalizations. Deprescribing, the process of stopping or gradually reducing the dose of a medication that may be causing harm or offers no benefit, can reduce polypharmacy among older adults. However, little research focuses on deprescribing in the HD population, and few tools exist to support deprescribing in HD. To address this gap, we developed and validated the STrategic Optimization of Medication Use in Patients on HemoDialysis (STOPMed-HD) intervention, a deprescribing toolkit which includes clinician-focused algorithms, monitoring forms, and evidence tables, and patient-facing information bulletins and videos. Co-developed with clinicians and patients, the toolkit reflects patient priorities and aligns with their deprescribing goals. This report describes the implementation and evaluation strategy of the STOPMed-HD intervention at 4 HD sites across Canada, and presents insights into barriers, facilitators, and key considerations for implementation. Knowledge mobilization and implementation methods: Our knowledge mobilization and implementation strategy involves a collaborative approach to implement the evidence-based deprescribing toolkit. Our strategy prioritizes engagement with several key partners, including patients and clinicians, to support implementation and foster a culture of deprescribing within HD units across Canada. Clinician buy-in at participating sites was established during toolkit development, and we continue to support clinicians throughout implementation at their respective HD units. Diverse patient partners have been actively involved since the inception of the study, and ongoing patient engagement remains central. To explore facilitators and barriers to uptake, we conducted interviews with patients and clinicians who participated in the 6-month deprescribing intervention at the Toronto site. Interviews at other sites will be completed in the coming months. The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework guided our data collection and analysis approach. Key findings and implementation considerations: The 6-month deprescribing intervention has been implemented in Toronto, ON; Halifax, NS; Calgary, AB; and Victoria, BC. This report includes key barriers and facilitators identified from the Toronto site. Patient-level barriers include fear of withdrawal, medication dependence, disengagement, and lack of follow-up support, while facilitators include clear messaging about deprescribing and regular monitoring and follow-up. Clinician-level barriers involve time constraints and unclear deprescribing roles and responsibilities among the care team. Barriers faced by facilitators include a lack of evidence-based deprescribing tools, integration of deprescribing into routine practice, a deprescribing champion, and multidisciplinary collaboration. System-level challenges include inadequate resources, fragmented electronic medical record systems, and a need for further research on deprescribing outcomes in the HD population. Potential cost savings and sharing learnings across HD care teams are additional facilitators. Future directions: This study demonstrates the potential of a structured, evidence-informed deprescribing approach to enhance patient safety, reduce medication burden, support shared decision-making, and promote team-based medication management in HD. Challenges to sustainability and rollout remain, and further research is needed to identify scalable, sustainable strategies that support long-term success of deprescribing across diverse HD settings.
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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 | 0.030 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".