“Giving the patients less work”: A thematic analysis of telehealth use and recommendations to improve usability for autistic adults
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".