Abstract
Recent developments in the field of telehealth suggest that novel technologies may ameliorate patients’ limited access to clinicians capable of conducting ASD assessments (Koch, 2006). Specifically, studies have shown that parents can capture informative behaviors that aid in autism assessment by using phone-based applications, and use of these videos result in diagnoses that are consistent with those of clinicians who interact with the same child in person (Nazneen et al., 2015; Smith et al., In press). It is yet unknown how clinicians make use of the information gleaned from videos uploaded to a store-and-forward system. Given that clinicians and physicians often exhibit bias in their use of available information, we sought to understand how cues were utilized when direct contact or observation of the patient is not possible. We used lens model analyses to evaluate one store-and-forward approach: the Naturalistic Observation Diagnostic Assessment (NODA; Smith et al., 2009). Brunswik’s (1952, 1955) lens model provides a computational approach to evaluating use of information while formulating decisions (Karelaia & Hogarth, 2008), such as in the assessment and diagnosis of ASD in children. The parents of 51 children used the NODA procedure to upload four 10-minute long videos depicting the child’s behavior in familiar in-home scenarios. Eleven children were typically developing, and the remaining 40 were seeking an Autism evaluation. Each child was observed twice: One clinician performed a standard in-person assessment (IPA), while the other performed an assessment via videos uploaded to the NODA tool. Observations for 65 classes of behavior (e.g., limited conversation, speaking volume too loud, lack of peer play, echolalia, lining up toys, preoccupation with activity) were clustered into eight nominal variables representing the seven sub-criteria associated with ASD (American Psychiatric Association, 2013) and an additional criterion for behavior labeled as typical. We computed a count for each ASD variable that represented the frequency with which the NODA clinician used the label when tagging the videos. Three pairs of linear regressions were run to estimate the weight clinicians placed on observations associated with each sub-criterion for ASD. Each pair of regressions consisted of one analysis where NODA tag counts were regressed onto the decision made by the IPA clinician and another that regressed NODA tag counts onto the NODA clinician’s decision. The three sets of regressions modeled the clinicians’ use of cues as an equal weight strategy, a conjunctive strategy, and a disjunctive strategy respectively. Our results suggest that clinicians consistently derive their decisions from a limited number of the cues available to them, as no analysis found more than two classes of observation to be predictive of diagnosis. Specifically, we found that IPA and NODA clinicians appeared to adopt a conjunctive rule, and relied most heavily on the number of typical behaviors observed. We also found a high level of agreement between the IPA and NODA clinicians with respect to use of information and diagnosis. These findings suggests that there is no dearth of information available to clinicians for distal ASD assessment when observations are made through pre-recorded video provided by parents via the NODA system as compared to IPA. The results of the reported study illustrate the promise of telehealth technology adoption for distal patient assessment and diagnosis.