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Autism Diagnostic Impressions in Young Children Formed by Primary Care Clinicians and through Telemedicine Expert Assessments

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Title: Autism Diagnostic Impressions in Young Children Formed by Primary Care Clinicians and through Telemedicine Expert Assessments
Language: English
Authors: Andrea Trubanova Wieckowski (ORCID 0000-0001-5811-3460), Ashley de Marchena (ORCID 0000-0003-4186-6065), Alexia F. Dickerson, Erika Frick, Georgina Perez Liz (ORCID 0000-0001-5304-0442), Ashley Dubin, Diana L. Robins
Source: Autism: The International Journal of Research and Practice. 2025 29(11):2898-2905.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 8
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Autism Spectrum Disorders, Toddlers, Disability Identification, Clinical Diagnosis, Health Services, Teleconferencing, Diagnostic Tests, Observation, Cognitive Ability, Motor Development, Adjustment (to Environment), Behavior Rating Scales, Accuracy
Assessment and Survey Identifiers: Autism Diagnostic Observation Schedule, Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales
DOI: 10.1177/13623613251355257
ISSN: 1362-3613
1461-7005
Abstract: Formal autism diagnosis is often critical for children to access early, autism-specific services and supports. However, barriers to traditional in-person evaluations, including long waitlists, delay diagnosis. The goal of the current study was to compare diagnostic impressions (i.e. clinical judgments) made by primary care clinicians and autism experts conducting brief telehealth sessions, with expert diagnosis from in-person gold-standard evaluations. Participants were toddlers (n = 32, age 12-36 months) referred for any developmental concerns by four primary care clinicians from one pediatric practice in the United States. Primary care clinicians indicated their diagnostic classification and families then completed telehealth evaluations and in-person evaluations with one of five autism diagnostic expert clinicians. When primary care clinicians classified a child as having definite autism (n = 11), they were 100% accurate, but only 57% accurate when they indicated a child definitely did not have autism. Experts providing classification after a telehealth evaluation accurately classified 72% of children and were confident in the diagnosis for 55% of cases. In high-confidence cases, telehealth diagnosis matched final diagnosis 88% of the time. These findings indicate that when primary care clinicians believe a toddler is autistic, or when autism experts indicate autism telehealth classification with confidence, the child should begin receiving autism-specific services and supports right away.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1486799
Database: ERIC
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  Value: <anid>AN0188761497;f9d01nov.25;2025Oct23.02:29;v2.2.500</anid> <title id="AN0188761497-1">Autism diagnostic impressions in young children formed by primary care clinicians and through telemedicine expert assessments </title> <p>Formal autism diagnosis is often critical for children to access early, autism-specific services and supports. However, barriers to traditional in-person evaluations, including long waitlists, delay diagnosis. The goal of the current study was to compare diagnostic impressions (i.e. clinical judgments) made by primary care clinicians and autism experts conducting brief telehealth sessions, with expert diagnosis from in-person gold-standard evaluations. Participants were toddlers (n = 32, age 12–36 months) referred for any developmental concerns by four primary care clinicians from one pediatric practice in the United States. Primary care clinicians indicated their diagnostic classification and families then completed telehealth evaluations and in-person evaluations with one of five autism diagnostic expert clinicians. When primary care clinicians classified a child as having definite autism (n = 11), they were 100% accurate, but only 57% accurate when they indicated a child definitely did not have autism. Experts providing classification after a telehealth evaluation accurately classified 72% of children and were confident in the diagnosis for 55% of cases. In high-confidence cases, telehealth diagnosis matched final diagnosis 88% of the time. These findings indicate that when primary care clinicians believe a toddler is autistic, or when autism experts indicate autism telehealth classification with confidence, the child should begin receiving autism-specific services and supports right away. There are long waitlists for autism evaluations, which greatly delay the start of interventions that are known to improve children's outcomes. We tested the accuracy of primary care clinicians' impressions of autism versus other developmental delays during well-child visits, and of experts during brief telemedicine visits, and found that more than half of the children were accurately identified through these streamlined methods. These findings support a tiered approach in which children identified through these more efficient methods begin autism intervention immediately; this approach also benefits children with more complex differentials by shortening waitlists for comprehensive evaluations for those who require them prior to treatment entry.</p> <p>Keywords: autism spectrum disorder; diagnosis; early detection; primary care diagnosis; telehealth; toddlers</p> <p>The median age of autism diagnosis globally is 48–60 months ([<reflink idref="bib38" id="ref1">38</reflink>])—<emph>years</emph> after diagnosis is possible for many children, as children can be reliably diagnosed as early as 14 months ([<reflink idref="bib24" id="ref2">24</reflink>]). Without a diagnosis, children rarely access autism-specific early services and supports ([<reflink idref="bib17" id="ref3">17</reflink>]; [<reflink idref="bib36" id="ref4">36</reflink>]) that lead to substantial gains (e.g. [<reflink idref="bib27" id="ref5">27</reflink>]; [<reflink idref="bib29" id="ref6">29</reflink>]; [<reflink idref="bib30" id="ref7">30</reflink>]; [<reflink idref="bib42" id="ref8">42</reflink>]). Specialists in autism diagnosis are scarce ([<reflink idref="bib5" id="ref9">5</reflink>]), concentrated in metropolitan areas, rarely accept public insurance forms of payment in the United States ([<reflink idref="bib4" id="ref10">4</reflink>]), and often have wait lists of 12–24 months ([<reflink idref="bib13" id="ref11">13</reflink>]). Worldwide, the guidelines about which professionals can readily diagnose autism, and how these processes are conducted, vary widely ([<reflink idref="bib3" id="ref12">3</reflink>]; [<reflink idref="bib25" id="ref13">25</reflink>]). These challenges disproportionately impact low-income and minoritized families ([<reflink idref="bib31" id="ref14">31</reflink>]; [<reflink idref="bib41" id="ref15">41</reflink>]). Although detailed specialist evaluation provides benefits beyond a simple diagnostic determination ([<reflink idref="bib15" id="ref16">15</reflink>]), these evaluations should not be the gatekeeper for autism-specific services and supports for young children ([<reflink idref="bib20" id="ref17">20</reflink>]; [<reflink idref="bib40" id="ref18">40</reflink>]; [<reflink idref="bib43" id="ref19">43</reflink>]).</p> <p>In the United States, the American Academy of Pediatrics recommends universal autism screening and surveillance in pediatric primary care ([<reflink idref="bib14" id="ref20">14</reflink>]). <emph>Screening</emph> refers to the use of validated developmental tools to identify children at increased likelihood of autism, whereas <emph>surveillance</emph> refers to clinical approaches including asking caregivers about developmental and behavioral concerns, and informal observation. Although critical, implementation of screening and surveillance does not directly lead to an autism <emph>diagnosis</emph>; rather, primary care clinicians (PCCs) refer families to tertiary care specialty clinics, which often have long waitlists. Autism diagnosis of readily identifiable cases ([<reflink idref="bib9" id="ref21">9</reflink>]) by PCCs is an emerging practice that may accelerate diagnosis and services and improve disparities in access to care ([<reflink idref="bib32" id="ref22">32</reflink>]). Training models for autism diagnosis in primary care (e.g. [<reflink idref="bib12" id="ref23">12</reflink>]; [<reflink idref="bib21" id="ref24">21</reflink>]) require intensive resources and have yet to be broadly adopted. Yet, emerging evidence indicates high accuracy of PCCs without specialized tools or training, especially when ruling in autism ([<reflink idref="bib1" id="ref25">1</reflink>]; [<reflink idref="bib26" id="ref26">26</reflink>]).</p> <p>Another promising way to reduce latency between referral and diagnosis is by using briefer, lower-cost procedures to diagnose less complex cases. Telehealth-based evaluations show promise in reducing economic, geographic, and time-sensitivity burdens and disparities by offering several advantages over traditional in-person evaluations, including both convenience for families (e.g. decreased cost of transportation), and a shorter, more efficient approach to assessment. Telehealth for autism diagnostic evaluation has been shown to be accurate, acceptable to families and clinicians, and successful in increasing service availability by reducing wait times ([<reflink idref="bib2" id="ref27">2</reflink>]; [<reflink idref="bib10" id="ref28">10</reflink>]; [<reflink idref="bib22" id="ref29">22</reflink>]; [<reflink idref="bib34" id="ref30">34</reflink>]; [<reflink idref="bib37" id="ref31">37</reflink>]).</p> <p>This pilot study aimed to measure the accuracy of autism diagnostic impressions made by (<reflink idref="bib1" id="ref32">1</reflink>) PCCs during well-child visits and (<reflink idref="bib2" id="ref33">2</reflink>) autism specialists conducting brief telehealth-based evaluations, indexed against traditional, in-person specialist evaluation. For the purposes of this study, we define "diagnostic impression" as a clinical judgment—without comprehensive assessment. Our long-term goal is to facilitate a future tiered diagnostic approach, in which PCCs can rule in autism in children for whom diagnosis is clear, and refer unclear or complex cases for streamlined specialist evaluations through telehealth, and finally, if needed, in-person evaluations, ultimately reducing the wait for early intervention services for clear-cut cases.</p> <hd id="AN0188761497-2">Methods</hd> <p></p> <hd id="AN0188761497-3">Participants</hd> <p>Four <emph>pediatric primary care clinicians</emph> (PCCs: three physicians, one physician assistant) from one urban pediatric practice participated. The practice conducts about 25 18-month well-child visits per month, and about 15% of its patient base receives medical assistance. PCCs had no additional autism training prior to the start of the study, although one PCC recalled a continuing education session about early detection of autism from several years prior.</p> <p>Thirty-two <emph>toddlers</emph> (13–41 months at the time of evaluation; see Table 1), all patients of the same practice, participated. PCCs referred toddlers to the study when they had autism or other developmental concerns. Forty-five toddlers were referred; 12 were excluded for not attending the evaluation, and one was excluded for inconclusive result due to a language barrier.</p> <p>Table 1. Demographics and characterization metrics for the sample, divided by final diagnosis.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /><col align="char" char="." /></colgroup><thead><tr><th /><th align="left">Autism(<italic>n</italic> = 24)</th><th align="left">Not autism<xref ref-type="table-fn" rid="tfn2">a</xref>(<italic>n</italic> = 8)</th><th align="left">Total(<italic>n</italic> = 32)</th></tr></thead><tbody><tr><td>Mean age in months (SD)<xref ref-type="table-fn" rid="tfn3">b</xref></td><td>26.46 (7.47)</td><td>25.63 (8.93)</td><td>26.25 (7.72)</td></tr><tr><td /><td><italic>N</italic> (%)</td><td><italic>N</italic> (%)</td><td><italic>N</italic> (%)</td></tr><tr><td>Sex (male)</td><td>17 (70.8%)</td><td>6 (75.0%)</td><td>23 (71.9%)</td></tr><tr><td>Race</td><td /><td /><td /></tr><tr><td>White</td><td>10 (41.7%)</td><td>5 (62.5%)</td><td>15 (46.9%)</td></tr><tr><td>Black/African American</td><td>10 (41.7%)</td><td>1 (12.5%)</td><td>11 (34.4%)</td></tr><tr><td>Asian</td><td>1 (4.2%)</td><td>1 (12.5%)</td><td>2 (6.3%)</td></tr><tr><td>Native Hawaiian or Other Pacific islander</td><td>1 (4.2%)</td><td>0 (0%)</td><td>1 (3.1%)</td></tr><tr><td>Multiple races</td><td>1 (4.2%)</td><td>1 (12.5%)</td><td>2 (6.3%)</td></tr><tr><td>Unknown race(s)</td><td>1 (4.2%)</td><td>0 (0.00%)</td><td>1 (3.1%)</td></tr><tr><td>Ethnicity</td><td /><td /><td /></tr><tr><td>Hispanic or Latine</td><td>5 (20.8%)</td><td>0 (0%)</td><td>5 (15.6%)</td></tr><tr><td>Not Hispanic or Latine</td><td>13 (54.2%)</td><td>8 (100%)</td><td>21 (65.6%)</td></tr><tr><td>Unknown ethnicity</td><td>6 (25.0%)</td><td>0 (0%)</td><td>6 (18.8%)</td></tr><tr><td>TAP score (mean; range)<xref ref-type="table-fn" rid="tfn4">c</xref></td><td>12.9 (7–19)</td><td>10.50 (8–14)</td><td>12.23 (7–19)</td></tr><tr><td>ADOS-2 CSS (mean; range)<xref ref-type="table-fn" rid="tfn5">d</xref></td><td>7.3 (2–10)</td><td>2.3 (2–4)</td><td>6.1 (2–10)</td></tr><tr><td>Mullen ELC (mean; range)<xref ref-type="table-fn" rid="tfn6">e</xref></td><td>64.9 (49–91)</td><td>89.0 (59–117)</td><td>71.3 (49–117)</td></tr></tbody></table> </ephtml> </p> <p>1 Three participants are missing telehealth data. The missing participants fall into the following demographic categories: two females, one male; two Black or African American, one unknown; one not Hispanic or Latine, two Hispanic or Latine; all diagnosed with autism.</p> <ulist> <item>2 Not Autism group included five children with no diagnoses, and three children with other developmental disorders.</item> <item>3 Age refers to age at time of the telehealth evaluation.</item> <item>4 TAP – TELE-ASD-PEDS; range = 7–21; 11+ indicates greater likelihood of autism.</item> <item>5 ADOS-2 CSS: Autism Diagnostic Observation Schedule, Second Edition Calibrated Severity Score; range = 1–10.</item> <item>6 Mullen ELC: Mullen Scales of Early Learning, Early Learning Composite standard score; Mean = 100, SD = 15.</item> </ulist> <hd id="AN0188761497-4">Measures</hd> <p></p> <hd id="AN0188761497-5">Screening measures</hd> <p>Diagnostic Impression Form (DIF) asks PCCs to report: (<reflink idref="bib1" id="ref34">1</reflink>) diagnostic classification (definitely Autism, definitely Not Autism, Unsure); (<reflink idref="bib2" id="ref35">2</reflink>) confidence in classification (5-point scale: "Not Very Confident" to "Extremely Confident"); (<reflink idref="bib3" id="ref36">3</reflink>) type of concern and information leading to concern (e.g. social engagement concern through observation); and (<reflink idref="bib4" id="ref37">4</reflink>) specific behavior contributing to classification, adapted from a 5-Minute Impressions form collected from expert clinicians ([<reflink idref="bib35" id="ref38">35</reflink>]), including impaired social reciprocity, nonverbal communication, and so on.</p> <hd id="AN0188761497-6">Telehealth evaluation measures</hd> <p>The TELE-ASD-PEDS (TAP; [<reflink idref="bib6" id="ref39">6</reflink>]) is a 15- to 20-min remote autism assessment. Caregivers assemble recommended materials in advance, and a clinician guides the caregiver and child through play-based activities and then scores the child on seven key behaviors using a 1–3 scale. Total scores ⩾11 indicate probable autism diagnosis. Clinicians also report <emph>diagnostic impression</emph> (Autism vs Not Autism; no option to select "Unsure"), <emph>confidence in impression</emph> (1–4 scale), and whether or not they would <emph>refer the child for further evaluation</emph>. The TAP was administered using a HIPAA-secure Zoom session between the caregiver–child dyad at home and an expert autism clinician. Caregivers also completed a semi-structured <emph>Diagnostic and Statistical Manual of Mental Disorders</emph> (5th ed.; DSM-5) Autism Interview.</p> <hd id="AN0188761497-7">In-person comprehensive evaluation measures</hd> <p>Participants completed the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2; [<reflink idref="bib18" id="ref40">18</reflink>]; [<reflink idref="bib19" id="ref41">19</reflink>]). Caregivers completed the Toddler Autism Symptom Inventory (TASI; [<reflink idref="bib8" id="ref42">8</reflink>]). Clinical characterization measures included Mullen Scales of Early Learning ([<reflink idref="bib23" id="ref43">23</reflink>]), Vineland Adaptive Behavior Scales, Third Edition (Vineland-3; [<reflink idref="bib33" id="ref44">33</reflink>]), and a history form. Final DSM-5 diagnoses were made based on in-person evaluation.</p> <hd id="AN0188761497-8">Procedure</hd> <p></p> <hd id="AN0188761497-9">PCC referral</hd> <p>PCCs saw children under their care as they normally would. For any patient on their caseload between 12 and 36 months for whom the PCC had any developmental concerns, the PCC referred the family for the study and completed the DIF. PCCs referred children with developmental concerns other than autism to reduce assumptions about PCC concerns by expert clinicians. For referred children between 15 and 36 months of age, caregivers completed the Modified Checklist for Autism in Toddlers–Revised (M-CHAT-R/F; [<reflink idref="bib28" id="ref45">28</reflink>]) as part of standard practice (<emph>n</emph> = 28); screening results could inform PCCs impression ratings on the DIF.</p> <hd id="AN0188761497-10">Specialist assessments</hd> <p>All referred families were invited for two visits with independent autism specialists: (<reflink idref="bib1" id="ref46">1</reflink>) a 1.5-h telehealth appointment, followed by (<reflink idref="bib2" id="ref47">2</reflink>) a 3.5-h in-person, traditional autism evaluation. Telehealth visit occurred first for the majority of participants, since caregivers may have reported about their child's behaviors differently after they received a diagnosis in the in-person session, since oral feedback happened in the evaluation session or shortly thereafter. Specialists (four licensed psychologists, one physician) at the A.J. Drexel Autism Institute had extensive training and experience in early autism diagnosis. Telehealth and in-person evaluations were completed by separate evaluators, masked to data from other visits. Following the in-person evaluation, caregivers received feedback and a written report. All study procedures were approved by the university Institutional Review Board.</p> <hd id="AN0188761497-11">Statistical analysis</hd> <p>To characterize the frequency of definite PCC diagnostic classifications (vs Unsure), we calculated the proportion of all definite Autism/Not Autism impressions. PCC and telehealth accuracy was determined by match to final diagnosis; assessment of PCC accuracy of definite diagnostic classifications excluded "Unsure" PCC classifications. Confidence ratings were dichotomized into low confidence (1–2) and high confidence (3–5) for ease of interpretation.</p> <hd id="AN0188761497-12">Community involvement</hd> <p>The development of the study benefited considerably from input of the PCCs, stakeholders in autism research. In addition, one of the authors is a parent of an autistic child.</p> <hd id="AN0188761497-13">Results</hd> <p></p> <hd id="AN0188761497-14">PCC accuracy</hd> <p>Of the 18 cases for whom PCCs classified definite Autism or Not Autism, they were accurate, meaning impressions matched final diagnosis for 15 (83% of cases; Table 2a). Specifically, PCCs were 100% accurate when they classified a child as definite autism (<emph>n</emph> = 11). When they indicated that a child definitely did <emph>not</emph> have autism, but suspected another developmental delay (DD; <emph>n</emph> = 7), they were accurate for only four cases (57%).</p> <p>Table 2. Classification impression accuracy for (a) PCCs and (b) telehealth expert evaluation.</p> <p>Graph</p> <p> <ephtml> <table><colgroup><col align="left" /><col align="char" char="." /><col align="char" char="." /></colgroup><tbody><tr><td colspan="3"><graphic href="10.1177_13623613251355257-img2.tif" content-type="Graph" /></td></tr></tbody></table> </ephtml> </p> <p>7 Shaded cells indicate mismatch of classification to final diagnosis.</p> <p>PCCs gave an "Unsure" classification to 14 children (44%), 10 of whom were diagnosed with autism. Children with definite classification did not significantly differ from those classified as "Unsure" on sex (χ<sups>2</sups>(<reflink idref="bib1" id="ref48">1</reflink>, _I_N_i_ = 32) = 0.002, <emph>p</emph> > 0.00, <emph>ø</emph> = 0.009), race (χ<sups>2</sups>(<reflink idref="bib1" id="ref49">1</reflink>, _I_N_i_ = 31) = 0.313, <emph>p</emph> = 0.72, <emph>ø</emph> = 0.10), or age (<emph>t</emph>(<reflink idref="bib30" id="ref50">30</reflink>) = 0.341, <emph>p</emph> = 0.74, <emph>d</emph> = 0.12). The TAP scores for these children classified as "Unsure" ranged from 7 to 17, with five children (36%) scoring below the cut-off for a probable autism diagnosis and three children (21%) ultimately scoring below the cut-off on the ADOS-2. In addition, their cognitive scores ranged widely from 49 to 117 (SS Mean = 76.33; SD = 20.1). Demographic and characterization metrics for each group are provided in Supplemental Table. Those correctly classified did not differ significantly from those classified incorrectly or classified as "Unsure" on sex (χ<sups>2</sups>(<reflink idref="bib1" id="ref51">1</reflink>, _I_N_i_ = 32) = 0.922, <emph>p</emph> = 0.44, <emph>ø</emph> = 0.17), race (χ<sups>2</sups>(<reflink idref="bib1" id="ref52">1</reflink>, _I_N_i_ = 31) = 0.313, <emph>p</emph> = 0.72, <emph>ø</emph> = −0.10), or age (<emph>t</emph>(<reflink idref="bib30" id="ref53">30</reflink>) = 0.283, <emph>p</emph> = 0.78, <emph>d</emph> = −0.10). Within the autism sample, those correctly classified did not differ significantly from those classified incorrectly or not classified on ADOS-2 CSS score <emph>t</emph>(<reflink idref="bib22" id="ref54">22</reflink>) = 0.327, <emph>p</emph> = 0.37, <emph>d</emph> = 0.13); however, those correctly classified as Autism had significantly higher TAP scores compared to those classified incorrectly or not classified (<emph>t</emph>(<reflink idref="bib19" id="ref55">19</reflink>) = 2.11, <emph>p</emph> = 0.02, <emph>d</emph> = 0.93), with a large effect size. Of the 18 cases for whom PCCs indicated a definite classification, they gave high confidence ratings for 16 children (89%).</p> <hd id="AN0188761497-15">Telehealth accuracy and confidence</hd> <p>Telehealth clinicians accurately classified 21 of the 29 cases (72%; Table 2b). Sensitivity was 0.71; specificity was 0.75; positive predictive value was 0.88; and negative predictive value was 0.50. Both cases inaccurately classified as Autism had low confidence. Clinicians inaccurately classified six children as Not Autism, indicating low confidence for four of them. For the two inaccurately classified cases with high confidence, clinicians indicated need for full evaluation. Overall, clinicians had low confidence for 45% of the cases. Importantly, all eight cases marked as not requiring a full in-person diagnostic evaluation (i.e. 28%) had accurate telehealth classifications. Clinicians were more confident when their classification matched the final diagnosis (average rating = 3.29) than when it did not (average rating = 2.25; <emph>t</emph>(<reflink idref="bib27" id="ref56">27</reflink>) = 2.17, <emph>p</emph> = 0.04, <emph>d</emph> = 0.90), with a large effect.</p> <p>Children correctly classified did not significantly differ from those incorrectly classified by sex (χ<sups>2</sups>(<reflink idref="bib1" id="ref57">1</reflink>, _I_N_i_ = 29) = 0.004, <emph>p</emph> = 1.00, <emph>ø</emph> = 0.01), race (χ<sups>2</sups>(<reflink idref="bib1" id="ref58">1</reflink>, _I_N_i_ = 29) = 0.514, <emph>p</emph> = 0.68, <emph>ø</emph> = −0.13), or age (<emph>t</emph>(<reflink idref="bib27" id="ref59">27</reflink>) = 0.371, <emph>p</emph> = 0.71, <emph>d</emph> = 0.15). Similarly, children with high clinician confidence ratings did not significantly differ from those with low confidence ratings by sex (χ<sups>2</sups>(<reflink idref="bib1" id="ref60">1</reflink>, _I_N_i_ = 29) = 0.014, p = 1.00, <emph>ø</emph> = −0.02), race (χ<sups>2</sups>(<reflink idref="bib1" id="ref61">1</reflink>, _I_N_i_ = 29) = 0.909, <emph>p</emph> = 0.46, <emph>ø</emph> = 0.18), or age (<emph>t</emph>(<reflink idref="bib27" id="ref62">27</reflink>) = −0.82, <emph>p</emph> = 0.42, <emph>d</emph> = −0.31). Within the autism sample, those correctly classified did not differ significantly from those incorrectly classified on the ADOS-2 CSS score (<emph>t</emph>(<reflink idref="bib19" id="ref63">19</reflink>) = 0.266, <emph>p</emph> = 0.40, <emph>d</emph> = 0.13); however, those correctly classified as Autism had significantly higher TAP scores compared to those incorrectly classified (<emph>t</emph>(<reflink idref="bib19" id="ref64">19</reflink>) = 4.14, <emph>p</emph> < 0.001, <emph>d</emph> = 2.00), with a large effect size.</p> <hd id="AN0188761497-16">Discussion</hd> <p></p> <hd id="AN0188761497-17">PCC classification</hd> <p>In this pilot study, we found that when PCCs were certain about their diagnostic impression of autism, they identified young autistic children on their caseloads with 100% accuracy. In contrast, when PCCs indicated a child definitely did <emph>not</emph> have autism, they were accurate in only about half of cases presenting with developmental concerns—43% were ultimately diagnosed with autism. This pattern—low false positive and high false negative rate—is consistent with other work on PCC diagnostic accuracy ([<reflink idref="bib26" id="ref65">26</reflink>]). This suggests that PCC determinations of autism are likely to be accurate, whereas PCC impressions of non-autism—in the context of general developmental concerns—require follow-up by specialists. Critically, the high true positive rate of PCC diagnostic impressions of autism suggests that children should begin receiving autism-specific services and supports immediately, prior to formal evaluation by a specialist. PCC diagnosis of autism could reduce the volume of children referred for autism expert evaluation by nearly half, substantially accelerating access to autism-specific early services and supports for this subset of children and shortening waitlists for children with complex profiles.</p> <p>Importantly, although PCCs were highly accurate at <emph>ruling in</emph> autism, PCCs often incorrectly labeled children as having non-autism delays, or were unsure about diagnosis in children with developmental concerns. Thus, referral to a specialist is essential for children with delays or concerns when the PCC does not have an impression of autism.</p> <hd id="AN0188761497-18">Telehealth assessment</hd> <p>Overall agreement between telehealth and in-person diagnostic classifications was 72%, which is somewhat lower than previously published concordance as high as 92% ([<reflink idref="bib7" id="ref66">7</reflink>]; [<reflink idref="bib22" id="ref67">22</reflink>]). In addition, telehealth agreement in the current study was lower than that found for PCC classification; however, this is likely due to PCCs' option to note when they were uncertain, which was unavailable to telehealth specialists, and may also relate to PCCs' established relationship with the child and family. Consistent with our PCC data, and with findings from other telehealth studies (e.g. [<reflink idref="bib7" id="ref68">7</reflink>]), the false negative rate was much higher than the false positive rate via telehealth, indicating a greater likelihood of <emph>missing</emph> an autism diagnosis via telehealth versus incorrectly assigning one. This demonstrates that a brief telehealth evaluation can provide a definitive diagnosis for some children with autism, providing another opportunity to shorten delay to diagnosis as telehealth evaluations can be scheduled and completed more quickly than traditional in-person evaluations.</p> <p>Of note, two children were incorrectly classified as not having autism with high confidence by autism experts based on telehealth evaluations. In both cases, the telehealth clinicians recommended a full in-person evaluation. These findings highlight that the telehealth clinician's indication of need for a specialized evaluation does not always align with their confidence in their impressions and should be assessed separately. It is important to point out that the telehealth assessment included only the TAP (observation of caregiver–child interaction) and a brief autism-specific interview, with no additional history or demographic information except for age and sex, whereas the in-person evaluation included a developmental battery and a thorough medical and developmental history.</p> <hd id="AN0188761497-19">Proposed tiered approach to autism diagnoses</hd> <p>These findings together provide initial evidence for feasibility of a tiered diagnostic approach, in which PCCs can rule in autism in children for whom diagnosis is clear, refer cases in which autism is not clear for streamlined specialist evaluations through telehealth, and finally, if needed, in-person evaluations, reducing the damagingly long waitlists for autism diagnosis. Such an approach would provide a systematic triage for children with suspected autism, greatly reducing delays to initiating autism-specific early services and supports. This is critical, as earlier onset of autism-specific services and supports improves outcomes (e.g. [<reflink idref="bib11" id="ref69">11</reflink>]; [<reflink idref="bib16" id="ref70">16</reflink>]; [<reflink idref="bib39" id="ref71">39</reflink>]). Children diagnosed with autism by PCC or through telehealth may seek additional comprehensive expert evaluations for ongoing treatment planning or monitoring, but these evaluations could be undertaken in parallel to services and supports, so that evaluation is not a gatekeeper to accessing evidence-based services and supports.</p> <p>Study limitations include collecting data only from one pediatric practice in the United States. PCCs may have been better suited to identifying autism, as they are connected to research; expansion to additional practices will allow for greater generalizability. In addition, children for whom the PCCs <emph>did not</emph> have any developmental concerns were not included in this study, potentially missing some autistic children. Future studies should replicate findings in larger, more diverse samples, explore both PCC and child factors that may relate to classification accuracy and confidence, and evaluate the acceptability of the tiered approach for families and service providers, to present compelling evidence to community service agencies and insurance companies in support of streamlined diagnosis and earlier access to autism services and supports.</p> <hd id="AN0188761497-20">Supplemental Material</hd> <p>Graph: Supplemental material, sj-docx-1-aut-10.1177_13623613251355257 for Short report: Autism diagnostic impressions in young children formed by primary care clinicians and through telemedicine expert assessments by Andrea Trubanova Wieckowski, Ashley de Marchena, Alexia F Dickerson, Erika Frick, Georgina Perez Liz, Ashley Dubin and Diana L Robins in Autism</p> <p>We thank all the children and their families for participating in the study, and members of the research team who participated in data collection. We also thank Alexis Lieberman, MD, Kristen Smith, PA-C, IBCLC, Heather Ann Ruddock, MD, and Susan Ramirez-Chung, MD, the primary care clinicians at Advocare Fairmount Pediatrics, who contributed to this project.</p> <ref id="AN0188761497-21"> <title> References </title> <blist> <bibl id="bib1" idref="ref25" type="bt">1</bibl> <bibtext> Ahlers K., Gabrielsen T. P., Ellzey A., Brady A., Litchford A., Fox J...Carbone P. S. (2019). A pilot project using pediatricians as initial diagnosticians in multidisciplinary autism evaluations for young children. Journal of Developmental & Behavioral Pediatrics, 40(1), 1–11.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref27" type="bt">2</bibl> <bibtext> Alfuraydan M., Croxall J., Hurt L., Kerr M., Brophy S. (2020). Use of telehealth for facilitating the diagnostic assessment of Autism Spectrum Disorder (ASD): A scoping review. 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Journal of Child Psychology and Psychiatry, 62(2), 143–145.</bibtext> </blist> </ref> <ref id="AN0188761497-22"> <title> Footnotes </title> <blist> <bibtext> Andrea Trubanova Wieckowski</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0001-5811-3460 Ashley de Marchena</bibtext> </blist> <blist> <bibtext>Graph</bibtext> </blist> <blist> <bibtext>https://orcid.org/0000-0003-4186-6065 Georgina Perez Liz</bibtext> </blist> <blist> <bibtext>Graph https://orcid.org/0000-0001-5304-0442</bibtext> </blist> <blist> <bibtext> The research involved human participants. The study protocol was approved by the Drexel University Institutional Review Board.</bibtext> </blist> <blist> <bibtext> Andrea Trubanova Wieckowski: Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Writing – original draft; Writing – review & editing.Ashley de Marchena: Data curation; Formal analysis; Investigation; Methodology; Writing – original draft; Writing – review & editing.Alexia F Dickerson: Investigation; Project administration; Writing – review & editing.Erika Frick: Data curation; Project administration; Writing – review & editing.Georgina Perez Liz: Data curation; Investigation; Supervision; Writing – review & editing.Ashley Dubin: Data curation; Investigation; Supervision; Writing – review & editing.Diana L Robins: Conceptualization; Data curation; Funding acquisition; Investigation; Project administration; Resources; Supervision; Writing – review & editing.</bibtext> </blist> <blist> <bibtext> The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We are thankful to the Pennsylvania Medical Society for providing funding for this project.</bibtext> </blist> <blist> <bibtext> The author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Dr Robins is a co-owner of M-CHAT LLC, which licenses use of the M-CHAT in electronic products. No royalties were received for any of the data presented in the current study. Dr Robins serves on the program quality committee of Bancroft. The other authors have indicated they have no potential conflicts of interest to disclose.</bibtext> </blist> <blist> <bibtext> The data that support the findings of this study are available from the corresponding author, upon reasonable request.</bibtext> </blist> <blist> <bibtext> Supplemental material for this article is available online.</bibtext> </blist> </ref> <aug> <p>By Andrea Trubanova Wieckowski; Ashley de Marchena; Alexia F Dickerson; Erika Frick; Georgina Perez Liz; Ashley Dubin and Diana L Robins</p> <p>Reported by Author; Author; Author; Author; Author; Author; Author</p> </aug> <nolink nlid="nl1" bibid="bib38" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib24" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib17" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib36" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib27" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib29" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib30" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib42" firstref="ref8"></nolink> <nolink nlid="nl9" bibid="bib13" firstref="ref11"></nolink> <nolink nlid="nl10" bibid="bib25" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib31" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib41" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib15" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib20" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib40" firstref="ref18"></nolink> <nolink nlid="nl16" bibid="bib43" firstref="ref19"></nolink> <nolink nlid="nl17" bibid="bib14" firstref="ref20"></nolink> <nolink nlid="nl18" bibid="bib32" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib12" firstref="ref23"></nolink> <nolink nlid="nl20" bibid="bib21" firstref="ref24"></nolink> <nolink nlid="nl21" bibid="bib26" firstref="ref26"></nolink> <nolink nlid="nl22" bibid="bib10" firstref="ref28"></nolink> <nolink nlid="nl23" bibid="bib22" firstref="ref29"></nolink> <nolink nlid="nl24" bibid="bib34" firstref="ref30"></nolink> <nolink nlid="nl25" bibid="bib37" firstref="ref31"></nolink> <nolink nlid="nl26" bibid="bib35" firstref="ref38"></nolink> <nolink nlid="nl27" bibid="bib18" firstref="ref40"></nolink> <nolink nlid="nl28" bibid="bib19" firstref="ref41"></nolink> <nolink nlid="nl29" bibid="bib23" firstref="ref43"></nolink> <nolink nlid="nl30" bibid="bib33" firstref="ref44"></nolink> <nolink nlid="nl31" bibid="bib28" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib11" firstref="ref69"></nolink> <nolink nlid="nl33" bibid="bib16" firstref="ref70"></nolink> <nolink nlid="nl34" bibid="bib39" firstref="ref71"></nolink>
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  Data: Autism Diagnostic Impressions in Young Children Formed by Primary Care Clinicians and through Telemedicine Expert Assessments
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  Data: <searchLink fieldCode="AR" term="%22Andrea+Trubanova+Wieckowski%22">Andrea Trubanova Wieckowski</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5811-3460">0000-0001-5811-3460</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ashley+de+Marchena%22">Ashley de Marchena</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4186-6065">0000-0003-4186-6065</externalLink>)<br /><searchLink fieldCode="AR" term="%22Alexia+F%2E+Dickerson%22">Alexia F. Dickerson</searchLink><br /><searchLink fieldCode="AR" term="%22Erika+Frick%22">Erika Frick</searchLink><br /><searchLink fieldCode="AR" term="%22Georgina+Perez+Liz%22">Georgina Perez Liz</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5304-0442">0000-0001-5304-0442</externalLink>)<br /><searchLink fieldCode="AR" term="%22Ashley+Dubin%22">Ashley Dubin</searchLink><br /><searchLink fieldCode="AR" term="%22Diana+L%2E+Robins%22">Diana L. Robins</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22Autism+Spectrum+Disorders%22">Autism Spectrum Disorders</searchLink><br /><searchLink fieldCode="DE" term="%22Toddlers%22">Toddlers</searchLink><br /><searchLink fieldCode="DE" term="%22Disability+Identification%22">Disability Identification</searchLink><br /><searchLink fieldCode="DE" term="%22Clinical+Diagnosis%22">Clinical Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Health+Services%22">Health Services</searchLink><br /><searchLink fieldCode="DE" term="%22Teleconferencing%22">Teleconferencing</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+Tests%22">Diagnostic Tests</searchLink><br /><searchLink fieldCode="DE" term="%22Observation%22">Observation</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Ability%22">Cognitive Ability</searchLink><br /><searchLink fieldCode="DE" term="%22Motor+Development%22">Motor Development</searchLink><br /><searchLink fieldCode="DE" term="%22Adjustment+%28to+Environment%29%22">Adjustment (to Environment)</searchLink><br /><searchLink fieldCode="DE" term="%22Behavior+Rating+Scales%22">Behavior Rating Scales</searchLink><br /><searchLink fieldCode="DE" term="%22Accuracy%22">Accuracy</searchLink>
– Name: SubjectThesaurus
  Label: Assessment and Survey Identifiers
  Group: Su
  Data: <searchLink fieldCode="SU" term="%22Autism+Diagnostic+Observation+Schedule%22">Autism Diagnostic Observation Schedule</searchLink><br /><searchLink fieldCode="SU" term="%22Mullen+Scales+of+Early+Learning%22">Mullen Scales of Early Learning</searchLink><br /><searchLink fieldCode="SU" term="%22Vineland+Adaptive+Behavior+Scales%22">Vineland Adaptive Behavior Scales</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1177/13623613251355257
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1362-3613<br />1461-7005
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Formal autism diagnosis is often critical for children to access early, autism-specific services and supports. However, barriers to traditional in-person evaluations, including long waitlists, delay diagnosis. The goal of the current study was to compare diagnostic impressions (i.e. clinical judgments) made by primary care clinicians and autism experts conducting brief telehealth sessions, with expert diagnosis from in-person gold-standard evaluations. Participants were toddlers (n = 32, age 12-36 months) referred for any developmental concerns by four primary care clinicians from one pediatric practice in the United States. Primary care clinicians indicated their diagnostic classification and families then completed telehealth evaluations and in-person evaluations with one of five autism diagnostic expert clinicians. When primary care clinicians classified a child as having definite autism (n = 11), they were 100% accurate, but only 57% accurate when they indicated a child definitely did not have autism. Experts providing classification after a telehealth evaluation accurately classified 72% of children and were confident in the diagnosis for 55% of cases. In high-confidence cases, telehealth diagnosis matched final diagnosis 88% of the time. These findings indicate that when primary care clinicians believe a toddler is autistic, or when autism experts indicate autism telehealth classification with confidence, the child should begin receiving autism-specific services and supports right away.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2025
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1486799
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1486799
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1177/13623613251355257
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 8
        StartPage: 2898
    Subjects:
      – SubjectFull: Autism Spectrum Disorders
        Type: general
      – SubjectFull: Toddlers
        Type: general
      – SubjectFull: Disability Identification
        Type: general
      – SubjectFull: Clinical Diagnosis
        Type: general
      – SubjectFull: Health Services
        Type: general
      – SubjectFull: Teleconferencing
        Type: general
      – SubjectFull: Diagnostic Tests
        Type: general
      – SubjectFull: Observation
        Type: general
      – SubjectFull: Cognitive Ability
        Type: general
      – SubjectFull: Motor Development
        Type: general
      – SubjectFull: Adjustment (to Environment)
        Type: general
      – SubjectFull: Behavior Rating Scales
        Type: general
      – SubjectFull: Accuracy
        Type: general
      – SubjectFull: Autism Diagnostic Observation Schedule
        Type: general
      – SubjectFull: Mullen Scales of Early Learning
        Type: general
      – SubjectFull: Vineland Adaptive Behavior Scales
        Type: general
    Titles:
      – TitleFull: Autism Diagnostic Impressions in Young Children Formed by Primary Care Clinicians and through Telemedicine Expert Assessments
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Andrea Trubanova Wieckowski
      – PersonEntity:
          Name:
            NameFull: Ashley de Marchena
      – PersonEntity:
          Name:
            NameFull: Alexia F. Dickerson
      – PersonEntity:
          Name:
            NameFull: Erika Frick
      – PersonEntity:
          Name:
            NameFull: Georgina Perez Liz
      – PersonEntity:
          Name:
            NameFull: Ashley Dubin
      – PersonEntity:
          Name:
            NameFull: Diana L. Robins
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 11
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-print
              Value: 1362-3613
            – Type: issn-electronic
              Value: 1461-7005
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 11
          Titles:
            – TitleFull: Autism: The International Journal of Research and Practice
              Type: main
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