Factors Associated With Portal and Telehealth Uptake and Use in a Minoritized, Low-Income Community: Mixed Methods Study
Notice bibliographique
Résumé
Background: Despite evidence that use of patient portals and telehealth is associated with many health benefits, disparities exist in awareness, adoption, and use. Understanding factors and strategies specific to underserved populations is key to achieving digital equity and better health. Objective: This study assesses portal and telehealth experiences among residents of a minoritized and lower-resource area of Dallas, Texas. Methods: Using an explanatory sequential design, we conducted surveys and semistructured interviews with English- and Spanish-speaking adults in 15 ZIP Codes surrounding a community-based clinic. We recruited participants via a patient portal, flyers, emails distributed by clinic and community partners, and in person. Surveys were offered online and on paper. We used Fisher exact tests to identify factors associated with telehealth and/or portal use. We also recruited a subsample of survey participants to expound on survey findings in semistructured interviews. Our thematic analysis assessed convergence in survey and interview findings. Results: Among 182 survey respondents, most were older (n=109, 66%; age ≥60 years), African American or Black (n=120, 65%), and female (n=142, 79%); a little more than half (n=97, 54%) had completed ≥1 telehealth appointment, and a majority (n=131, 72%) had used a patient portal at least once. Compared with those who used the portal and/or telehealth, those reporting no use of portal or telehealth were more likely to have a high school education or less (P<.001) or be Spanish speakers (P<.011). A majority, regardless of portal or telehealth use, agreed with health promotion activity survey statements like "Using the Internet for health-related activities makes me feel actively involved with my health care" (n=103, 59%) and "I find the Internet useful for monitoring my health" (n=100, 58%). In interviews with 20 individuals, most of whom were older, Black, female, and had digital technology experience, seven factors were key to increased engagement in portals and telehealth: (1) improving patient autonomy, (2) integrating digital health technology into daily life, (3) receiving recommendations from trusted individuals, (4) appreciating the value of digital health technologies, (5) enlisting the support of care partners or peers, (6) managing severe or chronic illness, and (7) accessing test results rapidly. Conclusions: This study builds on previous work by confirming and contributing insights about factors key to technology uptake and use among underserved populations. Interventions using digital health technologies should focus on these factors to promote digital and health equity and achieve better health outcomes. Future research should explore which clinical settings and contexts are most conducive to increasing digital technology uptake and use, and implementation should leverage partnerships with community groups.
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 | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 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 ».