Demographics and Social Factors Associated With Persistent Nonuse of Video Appointments at a Multisite Health Care Institution: Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: During the COVID-19 outbreak, video appointments became a popular method for health care delivery, particularly in the early stages of the pandemic. Although Mayo Clinic aimed to reduce face-to-face (F2F) appointments to prevent the spread of the virus, some patients continued seeing their health care providers in person. In the later stages of the pandemic, many patients became comfortable with video appointments, even if they were initially hesitant. However, a subset of patients continued to avoid video appointments. It is not yet clear what sociodemographic factors may be associated with this group of patients. OBJECTIVE: This cross-sectional study aimed to examine demographic and social determinant of health (SDoH) factors associated with persistent nonusers of video appointments among a sample of patients within a multistate health care organization. We also explored patient beliefs about the use of video for health care appointments. METHODS: We conducted a 1-time cross-sectional paper survey, mailed between July and December 2022, of patients matching the eligibility criteria: (1) aged ≥18 years as of April 2020, (2) Mayo Clinic Midwest, Florida, or Arizona patient, (3) did not use video appointment services during April-December 2020 but attended F2F appointments in the departments of primary care and psychiatry/psychology. The survey asked patients, "Have you ever had a video appointment with a healthcare provider?" "Yes" respondents were defined as "users" (adapted to video appointments), and "no" respondents were defined as "persistent nonusers" of video appointments. We analyzed demographics, SDoH, and patient beliefs toward video appointments in 2 groups: persistent nonusers of video appointments and users. We used chi-square and 2-tailed t tests for analysis. RESULTS: Our findings indicate that patients who were older, lived in rural areas, sought care at Mayo Clinic Midwest, and did not have access to the patient portal system were likely to be persistent nonusers of video appointments. Only 1 SDoH factor (not having a disability, handicap, or chronic disease) was associated with persistent nonuse of video appointments. Persistent nonusers of video appointments held personal beliefs such as discomfort with video communication, difficulty interpreting nonverbal cues, and personal preference for F2F appointments over video. CONCLUSIONS: Our study identified demographic (older age and rural residence), sociodemographic factors (not having a disability, handicap, or chronic disease), and personal beliefs associated with patients' decisions to choose between video versus F2F appointments for health care delivery. Health care institutions should assess patients' negative attitudes toward technology prior to introducing them to digital health care services. Failing to do so may result in its restricted usage, negative patient experience, and wasted resources. For patients who hold negative beliefs about technology but are willing to learn, a "digital health coordinator" could be assigned to assist with various digital health solutions.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 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 ».