Clinical Integration and Evaluation of the STrategic Optimization of Prescription Medication Use in Patients on HemoDialysis (STOPMed-HD) Intervention
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,030 | 0,047 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».