Implementation of an Interdepartmental Collaborative Medication Review to Reduce Potentially Inappropriate Medication Use in Hospitalized Older Adults: Protocol for a Mixed Methods Study
Notice bibliographique
Résumé
BACKGROUND: The inappropriate use of multiple medications known as polypharmacy is a growing concern for older populations with comorbid conditions in India. Polypharmacy can lead to serious adverse health outcomes, increased health care costs, and reduced quality of life. Screening tools such as the Medication Appropriateness Index (MAI) and Screening Tool for Older Persons' Prescriptions (STOPP) or Screening Tool to Alert to Right Treatment (START) criteria can help identify potentially inappropriate medications, and interventions such as medication review clinics and prescribing audits can help improve the appropriateness. A collaborative medication review (CMR) involving a team approach is important to ensure that patients receive the best possible care. OBJECTIVE: The primary objective is to assess the feasibility of implementation of interdepartmental CMR to reduce potentially inappropriate medications in hospitalized older adults. The secondary objectives are to (1) explore the facilitators and barriers in this implementation from the health care providers' perspective, (2) determine the costs involved in the implementation from a health system perspective, and (3) analyze the efficacy of interdepartmental CMR by using MAI, postdischarge adverse events, and number of medication-related admissions. METHODS: This study consists of 5 phases aimed at improving CMR practices in India. Phase 1 focused on conducting a scoping review of CMR practices. Phase 2 involved creating standard operating procedures to establish a CMR team, delineating roles and responsibilities, and providing training. Phase 3 will evaluate the efficacy of CMR by using standardized tools such as MAI and STOPP/START criteria. Phase 4 assesses the challenges faced in implementing CMR. Finally, phase 5 analyzes the costs related to CMR implementation. This study employs a multicentered mixed methods approach, combining qualitative methods (in-depth interviews and focus group discussions) to explore implementation challenges and quantitative analysis through a quasi-experimental study involving 280 hospitalized older adults. It aims to measure costs and the reduction of potentially inappropriate medications post discharge. RESULTS: This study received a grant from the Indian Council of Medical Research-Safe and Rational Use of Medicine Task Force in December 2023. All study preparatory approvals were obtained. Phase 1 and phase 2 were completed by December 2024. Phase 3 is scheduled to finish by June 2025. Phases 4 and 5 are planned for completion by August 2025. Final data analysis and manuscript submission are expected by December 2025. CONCLUSIONS: This study can provide insights into the implementation and effectiveness of CMR and help to understand the facilitators and barriers to implementing interdepartmental CMR and the cost incurred in its implementation. Interprofessional teams will collaboratively review and optimize medications for older patients with multimorbidity in India-a strategy expected to enhance care coordination, improve clinical outcomes, and reduce health care costs. TRIAL REGISTRATION: Clinical Trials Registry - India CTRI/2024/06/069220; https://ctri.nic.in/Clinicaltrials/pmaindet2.php?EncHid=OTgyNDg=&Enc=&userName=.
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 | 0,074 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,008 | 0,009 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,005 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,053 | 0,008 |
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 ».