Assessing the Implementation and Effectiveness of the Electronic Patient-Reported Outcome Tool for Older Adults With Complex Care Needs: Mixed Methods Study
Notice bibliographique
Résumé
Background Goal-oriented care is being adopted to deliver person-centered primary care to older adults with multimorbidity and complex care needs. Although this model holds promise, its implementation remains a challenge. Digital health solutions may enable processes to improve adoption; however, they require evaluation to determine feasibility and impact. Objective This study aims to evaluate the implementation and effectiveness of the electronic Patient-Reported Outcome (ePRO) mobile app and portal system, designed to enable goal-oriented care delivery in interprofessional primary care practices. The research questions driving this study are as follows: Does ePRO improve quality of life and self-management in older adults with complex needs? What mechanisms are likely driving observed outcomes? Methods A multimethod, pragmatic randomized controlled trial using a stepped-wedge design and ethnographic case studies was conducted over a 15-month period in 6 comprehensive primary care practices across Ontario with a target enrollment of 176 patients. The 6 practices were randomized into either early (3-month control period; 12-month intervention) or late (6-month control period; 9-month intervention) groups. The primary outcome measure of interest was the Assessment of Quality of Life-4D (AQoL-4D). Data were collected at baseline and at 3 monthly intervals for the duration of the trial. Ethnographic data included observations and interviews with patients and providers at the midpoint and end of the intervention. Outcome data were analyzed using linear models conducted at the individual level, accounting for cluster effects at the practice level, and ethnographic data were analyzed using qualitative description and framework analysis methods. Results Recruitment challenges resulted in fewer sites and participants than expected; of the 176 target, only 142 (80.6%) patients were identified as eligible to participate because of lower-than-expected provider participation and fewer-than-expected patients willing to participate or perceived as ready to engage in goal-setting. Of the 142 patients approached, 45 (32%) participated. Patients set a variety of goals related to self-management, mental health, social health, and overall well-being. Owing to underpowering, the impact of ePRO on quality of life could not be definitively assessed; however, the intervention group, ePRO plus usual care (mean 15.28, SD 18.60) demonstrated a nonsignificant decrease in quality of life (t24=−1.20; P=.24) when compared with usual care only (mean 21.76, SD 2.17). The ethnographic data reveal a complex implementation process in which the meaningfulness (or coherence) of the technology to individuals’ lives and work acted as a key driver of adoption and tool appraisal. Conclusions This trial experienced many unexpected and significant implementation challenges related to recruitment and engagement. Future studies could be improved through better alignment of the research methods and intervention to the complex and diverse clinical settings, dynamic goal-oriented care process, and readiness of provider and patient participants. Trial Registration ClinicalTrials.gov NCT02917954; https://clinicaltrials.gov/ct2/show/NCT02917954
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 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 | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».