A Portal-Based Intervention (PATTERN) Designed to Support Medication Use Among Older Adults: Feasibility and Acceptability Study
Notice bibliographique
Résumé
Background: Poor medication adherence among older adults with multiple chronic conditions and polypharmacy is a public health concern stemming from distinct challenges. Prior interventions have largely used a one-size-fits-all approach or resource-intensive approaches inappropriate for busy primary care clinics. Objective: To address this, Phenotyping Adherence Through Technology-Enabled Reports and Navigation (PATTERN) was adapted from prior work. PATTERN is a portal-based intervention for monitoring self-reported medication adherence challenges among older adults in primary care. This study sought to implement and evaluate PATTERN's feasibility and acceptability. Methods: We conducted a patient randomized study with a posttest design. Primary care physicians at the participating health center were informed of the study, and approval was obtained to contact their patients. Patient eligibility included being aged 60 years or older, having prescription medications for ≥8 chronic conditions, and an upcoming visit with a physician who had provided approval. Potentially eligible patients were identified using an electronic health record query, and a research coordinator phoned them to confirm eligibility, assess interest, obtain consent, and conduct enrollment. Randomization occurred following enrollment. Those randomized to PATTERN received a medication adherence assessment in their patient portal accounts several days ahead of their visit. The assessment identified whether a patient was experiencing a medication adherence challenge, and if so, the type (cognitive, psychological, medical, regimen-related, social, or economic). Identified challenges were sent to the patient's primary care physician. Assessment delivery several days ahead of a visit was thought to offer sufficient time for patients to complete it and clinicians to review any challenges. Approximately 2 weeks after visits, the coordinator recontacted participants to conduct posttest interviews. This ensured clinicians had sufficient time to respond to challenges during or after visits. Posttest interviews measured the self-reported use of the portal, demographic and health characteristics, and for those randomized to PATTERN, intervention satisfaction. Self-reported data were captured in REDCap and analyzed descriptively. Electronic health record data were also analyzed descriptively to objectively identify feasibility, that is, whether intervention arm participants completed the PATTERN assessment. Results: We enrolled 64 participants (32 received usual care, and 32 received intervention). Most were female (66%, 42/64), not Hispanic or Latino (94%, 60/64), and identified as White (58%, 37/64). The average (SD) age was 75 (6.8) years. Most participants (80%) self-reported using the patient portal ≥12 times per year. However, electronic health record data revealed that less than half of all participants randomized to PATTERN (47%, 15/32) completed the medication adherence assessment. Of those who remembered completing it, 60% (3/5) were very satisfied with the experience and 20% (1/5) were a little satisfied. Conclusions: PATTERN has the potential for use with older primary care patients experiencing multiple chronic conditions and polypharmacy. Yet, further adaptation is needed to ensure recipients access their patient portal accounts and complete assessments.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 tête enseignante, 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 ».