Improving Patient Health Literacy During Telehealth Visits Through Remote Teach-Back Methods Training for Family Medicine Residents: Pilot 2-Arm Cluster, Nonrandomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: As telemedicine plays an increasing role in health care delivery, providers are expected to receive adequate training to effectively communicate with patients during telemedicine encounters. Teach-back is an approach that verifies patients' understanding of the health care information provided by health care professionals. Including patients in the design and development of teach-back training content for providers can result in more relevant training content. However, only a limited number of studies embrace patient engagement in this capacity, and none for remote care settings. OBJECTIVE: We aimed to design and evaluate the feasibility of patient-centered, telehealth-focused teach-back training for family medicine residents to promote the use of teach-back during remote visits. METHODS: We codeveloped the POTENTIAL (Platform to Enhance Teach-Back Methods in Virtual Care Visits) curriculum for medical residents to promote teach-back during remote visits. A patient participated in the development of the workshop's videos and in a patient-provider panel about teach-back. We conducted a pilot, 2-arm cluster, nonrandomized controlled trial. Family medicine residents at the intervention site (n=12) received didactic and simulation-based training in addition to weekly cues-to-action. Assessment included pre- and postsurveys, observations of residents, and interviews with patients and providers. To assess differences between pre- and postintervention scores among the intervention group, chi-square and 1-tailed t tests were used. A total of 4 difference-in-difference models were constructed to evaluate prepost differences between intervention and control groups for each of the following outcomes: familiarity with teach-back, importance of teach-back, confidence in teach-back ability, and ease of use of teach-back. RESULTS: Medical residents highly rated their experience of the teach-back training sessions (mean 8.6/10). Most residents (9/12, 75%) used plain language during training simulations, and over half asked the role-playing patient to use their own words to explain what they were told during the encounter. Postintervention, there was an increase in residents' confidence in their ability to use teach-back (mean 7.33 vs 7.83; P=.04), but there was no statistically significant difference in familiarity with, perception of importance, or ease of use of teach-back. None of the difference-in-difference models were statistically significant. The main barrier to practicing teach-back was time constraints. CONCLUSIONS: This study highlights ways to effectively integrate best-practice training in telehealth teach-back skills into a medical residency program. At the same time, this pilot study points to important opportunities for improvement for similar interventions in future larger-scale implementation efforts, as well as ways to mitigate providers' concerns or barriers to incorporating teach-back in their practice. Teach-back can impact remote practice by increasing providers' ability to actively engage and empower patients by using the features (whiteboards, chat rooms, and mini-views) of their remote platform.
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 | 0,006 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 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 ».