“Giving the patients less work”: A thematic analysis of telehealth use and recommendations to improve usability for autistic adults
Notice bibliographique
Résumé
Virtual visits are a telehealth service where patients and providers communicate in real-time using audio and/or video technology. Setting up a virtual visit is complex and may pose challenges for some autistic adults. We conducted semi-structured interviews with autistic adults ( n = 7), family members of autistic adults ( n = 12), and clinic personnel ( n = 6) from one US-based clinic and used thematic analysis to identify factors affecting usability of virtual visits. We found virtual visit preparation involves multiple contacts between clinic personnel and patients or family members via a variety of channels and usability was affected by technology considerations, logistical considerations, and expectations for visits. Participants said technological experience and using the patient portal enhanced usability, but technological issues could increase anxiety. Clinic personnel reported time constraints created logistical barriers to virtual visits; streamlining the process before the visit via the patient portal may improve the usability of virtual visits for autistic adults, family members, and clinic personnel. Participants also reported unclear expectations for virtual visits reduced usability and recommended reminders, instructional videos, and estimated wait-times to clarify expectations. While our findings are based on a single clinic, they may help inform usability improvement efforts in other clinics offering virtual visits for autistic adults. Lay abstract Real-time telehealth visits, called “virtual visits,” are live video chats between patients and healthcare professionals. There are lots of steps involved in setting up a virtual visit, which may be difficult for some autistic adults. We interviewed 7 autistic adults, 12 family members of autistic adults, and 6 clinic staff from one clinic in the United States. Our goal was to understand their experiences with virtual visits and see how we can make virtual visits easier to use. We re-read text from the interviews to organize experiences and advice that was shared into topics. We found that autistic adults (or their family members) had to connect with clinic staff many times by phone or online over several days to set up a virtual visit. Participants said that having more experience with technology and using the online patient portal made virtual visits easier to use. But, having issues with technology before the visit could make autistic adults and family members anxious. Clinic staff said it was hard for them to meet the needs of people who were using virtual visits and those who were being seen in person at the clinic. Participants recommended reducing the number of calls between staff and autistic adults or family members using the online patient portal instead. Participants also recommended reminder messages, instruction videos, and approximate wait-times to help autistic adults and family members know what to expect for the virtual visit. Our results are based on peoples’ experiences at one clinic, but could help other clinics make virtual visits easier to use for autistic adults and their family members.
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 | 0,034 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,004 |
| Études des sciences et des technologies | 0,007 | 0,007 |
| Communication savante | 0,005 | 0,006 |
| Science ouverte | 0,003 | 0,007 |
| Intégrité de la recherche | 0,002 | 0,003 |
| 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 ».