Telehealth Autism Diagnostic Assessments With Children, Young People, and Adults: Qualitative Interview Study With England-Wide Multidisciplinary Health Professionals
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (hereafter, autism) is a common neurodevelopmental condition. Core traits can range from subtle to severe and fluctuate depending on context. Individuals can present for diagnostic assessments during childhood or adulthood. However, waiting times for assessment are typically lengthy, and many individuals wait months or even years to be seen. Traditionally, there has been a lack of standardization between services regarding how many and which multidisciplinary health professionals are involved in the assessment and the methods (diagnostic tools) that are used. The COVID-19 pandemic has affected routine service provision because of stay-at-home mandates and social distancing guidelines. Autism diagnostic services have had to adapt, such as by switching from conducting assessments in person to doing these fully via telehealth (defined as the use of remote technologies for the provision of health care) or using blended in-person or telehealth methods. OBJECTIVE: This study explored health professionals' experiences of and perspectives about conducting telehealth autism diagnostic assessments, including barriers and facilitators to this, during the COVID-19 pandemic; potential telehealth training and supervision needs of health professionals; how the quality and effectiveness of telehealth autism diagnostic services can be enhanced; and experiences of delivering postdiagnostic support remotely. METHODS: A total of 45 health professionals, working in varied settings across England, participated in one-off, in-depth semistructured qualitative interviews. These were conducted via videoconferencing or telephone. Altogether, participants represented 7 professional disciplines (psychiatry, medicine, psychology, speech and language therapy, occupational therapy, nursing, and social work). The data were then analyzed thematically. RESULTS: Thematic analysis indicated the following 7 themes: practicalities of telehealth, telehealth autism diagnostic assessments, diagnostic conclusions, clinical considerations, postdiagnostic support, future ways of working, and health professionals' experiences and needs. Overall, telehealth autism diagnostic assessments were deemed by many participants to be convenient, flexible, and efficient for some patients, families, and health professionals. However, not all patients could be assessed in this way, for example, because of digital poverty, complex clinical presentation, or concerns about risk and safeguarding. Working remotely encouraged innovation, including the development of novel assessment measures. However, some participants expressed significant concerns about the validity and reliability of remotely assessing social communication conditions. CONCLUSIONS: A shift to telehealth meant that autism diagnostic services remained operational during the COVID-19 pandemic. However, this method of working has potentially affected the parity of service, with people presenting with clinical complexity having to potentially wait longer to be seen or given a diagnostic opinion. There is also a lack of standardization in the provision of services. Further research should identify evidence-based ways of enhancing the timeliness, accessibility, and robustness of the autism diagnostic pathway, as well as the validity and reliability of telehealth methods.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".