A Teleneuropsychology Consultation Service Model for Children with Neurodevelopmental and Acquired Disorders Residing in Rural State Regions

Sherwood, Andrea R.; MacDonald, Beatriz

doi:10.1093/arclin/acaa099

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Over the 10 past years, healthcare services have been involved in a progressive digitalization process [ 1 ]. The COVID-19 pandemic spurred this trend, increasing the demand for effective telehealth support for mental health [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning

Grazioli

Crippa

Rosi

et al. 2023

Eur Child Adolesc Psychiatry

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Recently, there has been an increase in telemedicine applied to child neuropsychiatry, such as the use of online platforms to collect remotely case histories and demographic and behavioral information. In the present proof-of-concept study, we aimed to understand to what extent information parents and teachers provide through online questionnaires overlaps with clinicians’ diagnostic conclusions on attention-deficit/hyperactivity disorder (ADHD). Moreover, we intended to explore a possible role that autism spectrum disorders (ASD) symptoms played in this process. We examined parent- and teacher-rated questionnaires collected remotely and an on-site evaluation of intelligence quotients from 342 subjects (18% females), aged 3–16 years, and referred for suspected ADHD. An easily interpretable machine learning model—decision tree (DT)—was built to simulate the clinical process of classifying ADHD/non-ADHD based on collected data. Then, we tested the DT model’s predictive accuracy through a cross-validation approach. The DT classifier’s performance was compared with those that other machine learning models achieved, such as random forest and support vector machines. Differences in ASD symptoms in the DT-identified classes were tested to address their role in performing a diagnostic error using the DT model. The DT identified the decision rules clinicians adopt to classify an ADHD diagnosis with an 82% accuracy rate. Regarding the cross-validation experiment, our DT model reached a predictive accuracy of 74% that was similar to those of other classification algorithms. The caregiver-reported ADHD core symptom severity proved the most discriminative information for clinicians during the diagnostic decision process. However, ASD symptoms were a confounding factor when ADHD severity had to be established. Telehealth procedures proved effective in obtaining an automated output regarding a diagnostic risk, reducing the time delay between symptom detection and diagnosis. However, this should not be considered an alternative to on-site procedures but rather as automated support for clinical practice, enabling clinicians to allocate further resources to the most complex cases.

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Section: Introductionmentioning

confidence: 99%

Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning

Grazioli

Crippa

Rosi

et al. 2023

Eur Child Adolesc Psychiatry

View full text Add to dashboard Cite

show abstract

“…Research in adult teleneuropsychology demonstrates validity for remote administration of certain standardized measures such as digit span, list learning, and verbal fluency tasks as well as the Repeatable Battery for the Assessment of Neuropsychological Status ( Brearly et al, 2017 ; Galusha-Glasscock et al, 2016 ). Nascent research on the feasibility and validity of leveraging telehealth for neurodevelopmental assessment is also promising, including successful utilization of telehealth for triage, consultation ( Ransom et al, 2020 ; Sherwood & MacDonald, 2020 ), and targeted assessments ( Harder et al, 2020 ; Salinas et al, 2020 ). Given the seismic shift in clinical practice, development of best practices for incorporating telehealth into neurodevelopmental assessment is needed.…”

mentioning

confidence: 99%

Integrating Telehealth Into Neurodevelopmental Assessment: A Model From the Cardiac Neurodevelopmental Outcome Collaborative

Cox

Butcher

Sadhwani

et al. 2022

Journal of Pediatric Psychology

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Objective In the wake of the COVID-19 pandemic, psychologists were pushed to look beyond traditional in-person models of neurodevelopmental assessment to maintain continuity of care. A wealth of data demonstrates that telehealth is efficacious for pediatric behavioral intervention; however, best practices for incorporating telehealth into neurodevelopmental assessment are yet to be developed. In this topical review, we propose a conceptual model to demonstrate how telehealth can be incorporated into various components of neurodevelopmental assessment. Methods Harnessing existing literature and expertise from a multidisciplinary task force comprised of clinicians, researchers, and patient/parent representatives from the subspecialty of cardiac neurodevelopmental care, a conceptual framework for telehealth neurodevelopmental assessment was developed. Considerations for health equity and access to care are discussed, as well as general guidelines for clinical implementation and gaps in existing literature. Results There are opportunities to integrate telehealth within each stage of neurodevelopmental assessment, from intake to testing, through to follow-up care. Further research is needed to determine whether telehealth mitigates or exacerbates disparities in access to care for vulnerable populations as well as to provide evidence of validity for a wider range of neurodevelopmental measures to be administered via telehealth. Conclusions While many practices are returning to traditional, face-to-face neurodevelopmental assessment services, psychologists have a unique opportunity to harness the momentum for telehealth care initiated during the pandemic to optimize the use of clinical resources, broaden service delivery, and increase access to care for pediatric neurodevelopmental assessment.

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“…These tools enable assessments encompassing various aspects, including psychological testing and psychometry [4]. Furthermore, the onset of the COVID-19 pandemic has further supported adopting digital tools for telehealth support, particularly within the mental health sector [5]. Digitization also includes aspects that cut across diagnoses, such as socio-anamnestic information relevant to neurodevelopmental conditions [6].…”

Section: Use Of Machine Learning Models To Differentiate Neurodevelop...mentioning

confidence: 99%

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

Grazioli,

Crippa,

Buo

et al. 2024

JMIR Form Res

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Background: Child and adolescent psychopathology diagnosis involves a multifaceted approach, which integrates clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, including online platforms for administering caregiver-rated questionnaires, are increasingly utilized in this field, especially during the first screening phase. With the rise of digital platforms for data collection, advanced psychopathology classification methods such as supervised machine learning (ML) have gained prominence in both research and clinical settings. This shift, recently referred to as psycho-informatics, has been facilitated by the gradual incorporation of computational devices into clinical workflows. However, an integrated telemedicine and ML approach is yet to be developed.Objective: This study aims to develop ML models for the classification of Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) diagnoses using online parent-reported socio-anamnestic data, with a primary focus on accuracy over interpretability. The objective is therefore to address the need for an innovative computational psychometrics framework that harnesses the potential of digital platforms and ML to improve the assessment of neurodevelopmental conditions. Methods:In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included information on sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian online screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random Forest (RF) and Logistic Regression (LR) models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables.Results: RF models demonstrated robust accuracy, achieving 84% (95% CI: 82%-85%, P < .001) for ADHD and 86% (95% CI: 84%-87%, P < .001) for ASD classifications, with high sensitivities of 93% for ADHD and of 95% for ASD. In contrast, the LR models exhibited both lower accuracy (61%, 95% CI: 57%-64%, P < .001 for ADHD, and 63%, 5% CI: 60%-67%, P < .001 for ASD) and sensitivities (62% for ADHD and 68% for ASD). Intriguingly, the independent variables considered for classification differed in importance between the two models, reflecting the distinct approaches and capabilities of RF and LR. Conclusions:The present study provides evidence for the potential of an ML approach, especially when built on RF models, in enhancing the diagnostic process of child and adolescent psychopathology. While interpretability remains crucial, the developed approach might therefore provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process. This paves the way for the integration of AI-driven tools in...

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A Teleneuropsychology Consultation Service Model for Children with Neurodevelopmental and Acquired Disorders Residing in Rural State Regions

Cited by 16 publications

References 13 publications

Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning

Exploring telediagnostic procedures in child neuropsychiatry: addressing ADHD diagnosis and autism symptoms through supervised machine learning

Integrating Telehealth Into Neurodevelopmental Assessment: A Model From the Cardiac Neurodevelopmental Outcome Collaborative

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study

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