Coaching Patients to Understand and Use Patient-Reported Outcome Data: Intervention Design and Evaluation
Notice bibliographique
Résumé
Background: Providing patients with information about their health and treatment options is important to ensure care that best reflects patient needs, values, and preferences. Patient-reported outcomes (PROs), measures of health status, are regularly collected in clinical contexts and scores can be returned to patients in personalized decision aids. One example of a PRO-based decision aid is the Arthritis care through Shared Knowledge (ASK) report, which shares individual PRO data on knee and hip arthritis-related pain and functional limitations with patients. However, given that the use of such data in clinical consultations is unfamiliar to many patients, support may be required to ensure this information is understood and used as intended. Objective: This paper describes ASK coaching, an online 1-hour group session designed to ensure patients understood the ASK report, including their PRO scores, and how to use the information in conversations with their clinicians. We present (1) quantitative evaluation results associated with attendance and self-assessment of learning and (2) qualitative evaluation results on motivation to attend, acceptability of the session format, and achievement of session goals. Methods: The session was designed and refined collaboratively with clinical experts and patient advisers. Patients in one arm of a pragmatic cluster-randomized trial evaluating the ASK report were invited to attend this session. To understand the profile of attendees (N=438) sociodemographic and clinical data were compared with all participants invited to coaching (N=1545) and a patient-reported assessment of self-efficacy was collected on a subset (N=692). In addition, a postsession survey was used to self-assess learning. Qualitative data were synthesized from semistructured postcoaching interviews, paired pre- and postcoaching interviews, and free-text responses to a postsession survey. A qualitative descriptive approach was used for analysis. Results: Compared with nonattendees, patients reporting higher education, greater health literacy, Medicare insurance, and lower self-efficacy for managing treatments were more likely to attend ASK coaching when invited. Participants' self-assessment of learning showed an improved understanding of current and projected osteoarthritis symptoms and where to find additional information. Qualitatively, patients reported attending coaching to gain information that could benefit their treatment or aid in research. The online group format was generally described as acceptable, and the session goals related to understanding the report and preparing for future conversations with clinicians were met. Suggestions for improvement, such as providing more opportunities for within-group interaction, were also provided. Conclusions: Our results highlight the value of coaching as an intervention to help patients understand and use novel health information, including PRO data, in conversations with clinicians. Given that it was well-liked by patients, promoting a greater understanding of the PRO-based ASK report, and increased feelings of preparedness for clinical consultation, coaching appears to be a promising intervention to support patients in understanding and using their personal health data.
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 | 0,028 | 0,021 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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 ».