Can We Expand the Pool of Youth Who Receive Telehealth Assessments for ADHD? Covariates of Service Utilization

Abstract

Background:

During the COVID-19 pandemic, telehealth became widely utilized for healthcare, including psychological evaluations. However, whether telehealth has reduced or exacerbated healthcare disparities for children with Attention-Deficit/Hyperactivity Disorder (ADHD) remains unclear.

Methods:

Data (race, ethnicity, age, insurance type, ADHD presentation, comorbidities, and distance to clinic) for youth with ADHD (M_age = 10.97, SD_age = 3.42; 63.71% male; 51.62% White) were extracted from the medical record at an urban academic medical center. Three naturally occurring groups were compared: those evaluated in person prior to COVID-19 (n =780), in person during COVID-19 (n = 839), and via telehealth during COVID-19 (n = 638).

Results:

Children seen via telehealth were significantly more likely to be older, White, have fewer comorbid conditions, and live farther from the clinic than those seen in person.

Conclusions:

The current study suggests that telehealth has not eliminated barriers to care for disadvantaged populations. Providers and institutions must take action to encourage telehealth use among these groups.

Keywords

ADHD assessment telehealth COVID-19 healthcare disparities

Introduction

Many questions arose about the use of telehealth in the field of psychology, and specifically psychological evaluations for youth, with the onset of the COVID-19 pandemic. These included questions about feasibility, test security, and the validity of such evaluations (Hewitt et al., 2020). Equally important were concerns regarding the impact of telehealth on the accessibility of healthcare for vulnerable populations (Fairchild et al., 2020). Would providing evaluations via telehealth introduce additional barriers to care for some patients, potentially worsening healthcare disparities? Alternatively, might telehealth evaluations actually prove more accessible to under-resourced populations, helping to reduce disparities in access to care? In the 2 years since COVID-19 began, evidence has started to accumulate for the validity of telehealth psychological evaluations in children. Emerging research has answered questions related to feasibility and validity (Hamner et al., 2022; Hewitt et al., 2020; Koterba et al., 2020; Peterson et al., 2021; Pritchard et al., 2020); however, questions around accessibility remain unanswered.

As the most common neurodevelopmental condition of childhood (P. Pastor et al., 2015), Attention-Deficit/Hyperactivity Disorder (ADHD) is a diagnosis that is often under consideration in the psychological evaluation of youth. In addition to its high prevalence rate, ADHD has a high rate of comorbidities and symptoms that overlap with many other mental health and medical conditions (Pritchard et al., 2012). Several of the factors that may contribute to the development of ADHD symptoms (e.g., toxic exposures, genetic risk [Carlsson et al., 2021; J. H. Kim et al., 2020]) are also associated with increased healthcare disparities that are known to exist for ADHD. Disparities in ADHD diagnosis and treatment are well established. For instance, lower socioeconomic status is associated with higher risk of ADHD diagnosis (Bozinovic et al., 2021; Collins & Cleary, 2016; Danielson et al., 2018; M. Kim et al., 2019) and, historically, with lower rates of stimulant treatment (Danielson et al., 2018; Froehlich et al., 2007; Zuvekas & Vitiello, 2012). Geographic variability is also evident in ADHD diagnosis and treatment, with the highest rates of ADHD, as well as the highest rates of stimulant treatment, found in urban areas and in the South (Danielson et al., 2018; Schneider & Eisenberg, 2006; Shi et al., 2021; Winders Davis et al., 2021). With regard to race, the evidence is less consistent, with some studies suggesting lower rates of ADHD diagnosis among Black children and others suggesting higher rates (Alegria et al., 2010; Coker et al., 2016; Danielson et al., 2018; Fairman et al., 2020; Mehta et al., 2013; Morgan et al., 2013; P. Pastor et al., 2015; P. N. Pastor & Reuben, 2005; Shi et al., 2021; Siegel et al., 2016; Stevens et al., 2005; Visser et al., 2014; Winders Davis et al., 2021; Xu et al., 2018; Zablotsky & Alford, 2020). Some studies point to lower rates of treatment for ADHD among Black children, while others indicate the opposite (Alegria et al., 2010; Bussing et al., 2012; Cummings et al., 2017; Danielson et al., 2018; Fairman et al., 2020; Ji et al., 2018; Mehta et al., 2013; Morgan et al., 2013; Saloner et al., 2013; Shi et al., 2021; Toomey et al., 2013; Winders Davis et al., 2021; Zuvekas & Vitiello, 2012). However, disparities in ADHD diagnosis and treatment do appear to be consistently present with regard to ethnicity such that Hispanic children are diagnosed with and treated for ADHD at lower rates than White children (Alegria et al., 2010; Bax et al., 2019; Coker et al., 2016; Cummings et al., 2017; Danielson et al., 2018; Fairman et al., 2020; Ji et al., 2018; Mehta et al., 2013; Morgan et al., 2013; P. Pastor et al., 2015; P. N. Pastor & Reuben, 2005; Saloner et al., 2013; Siegel et al., 2016; Stein et al., 2012; Toomey et al., 2013; Visser et al., 2014; Winders Davis et al., 2021; Wong & Landes, 2022; Xu et al., 2018; Zablotsky & Alford, 2020; Zuvekas & Vitiello, 2012). As such, psychological evaluation among youth with ADHD represents an important area of study with regard to telehealth accessibility.

At the start of the COVID-19 pandemic, the literature addressing the use of telehealth services for different patient populations was limited. In the past year, several studies (reviewed in the following section) have offered initial insights into what socio-demographic factors may impact access to or use of telehealth services in the time of COVID, including race/ethnicity, socioeconomic status, geography, and age. These studies, which generally reference adult populations, vary greatly in their definition of “telehealth” (e-messaging vs. telephone-only vs. video visits), their samples (large insurance member databases vs. smaller disease- or institution-specific samples), and their methodologies, making it difficult to draw definitive conclusions. Furthermore, none have focused on pediatric populations and only one has a focus on psychological evaluation specifically (Caze et al., 2020). This study, however, was primarily concerned with cancellation and no-show rates depending on modality, rather than with telehealth utilization rates across variables such as age, SES, and race. Although this study is the only investigation of telehealth related to neuropsychological evaluation, other research has examined factors related to disparities in telehealth use more generally. In providing an overview of the existing literature related to disparities in telehealth use, below, we will also highlight the differences in study samples and methodologies that may have contributed to discrepant findings.

Cultural Disparities in Telehealth Use

The literature on the impact of culture on telehealth usage is inconclusive, and is limited to adult patients; however, the majority of studies suggest that White patients use telehealth services at higher rates than non-White patients. Of the 12 studies that examined telehealth use by race and ethnicity, eight found that White patients used telehealth more than non-White or Hispanic patients during the COVID-19 pandemic (Friedman et al., 2022; Jewett et al., 2022; Neeman et al., 2021; Pierce & Stevermer, 2020; Rivera et al., 2021; Sachs et al., 2021; Weber et al., 2020; Wegermann et al., 2022). All eight of these studies utilized data from medical records to examine telehealth use, and sample sizes ranged from 594 patients to 334,666 visits. In contrast, one study examining medical records of 687 inflammatory bowel disease patients found no difference in telehealth use by race (Hayrapetian et al., 2021), and two Internet-survey-based studies found that Black and Hispanic patients self-reported greater telehealth use compared to White patients (Campos-Castillo & Anthony, 2021; Smith & Blavin, 2021). Additionally, Qian et al. s’ (2021) examination of a very large healthcare system database found that Hispanic patients had the largest increase in telehealth visits pre-pandemic to early-pandemic, although their rate of telehealth use returned to pre-pandemic levels (which tended to be lower than that of non-Hispanics) by October 2020.

With regard to preferred language, findings across the small number of available studies are consistent: three studies found that during the pandemic, patients who prefer English utilized telehealth significantly more than patients whose primary language is not English (Neeman et al., 2021; Rodriguez et al., 2021; Sachs et al., 2021).

Socioeconomic Status Disparities in Telehealth Use

Socioeconomic status (SES) is another socio-demographic variable that can impact access to healthcare services. The majority of studies examining the impact of SES on telehealth use found that lower SES was associated with lower telehealth use (Neeman et al., 2021; Pierce & Stevermer, 2020; Rivera et al., 2021; Sachs et al., 2021; Wegermann et al., 2022). These five studies, ranging in sample size from 2,999 to 334,666, all used insurance type as a proxy for SES such that patients with Medicare/Medicaid were considered to have lower SES than patients with commercial insurance. In contrast, one survey-based study found that patients with Medicaid self-reported greater use of telehealth than patients with commercial insurance (Smith & Blavin, 2021). However, there is evidence, via a very large healthcare system database, to suggest that the increased use of telehealth among low-income patients may have been temporary, as rates returned to pre-pandemic levels by October 2020 (Qian et al., 2021).

Geographic Disparities in Telehealth Use

Lack of geographic proximity to healthcare services has been considered a barrier to access that telehealth could potentially address. Census data indicate that even before the pandemic, in 2018, 92% of American households had at least one computer, and 85% had a broadband internet subscription (United States Census Bureau, 2021). Thus, the large majority of households in the United States should have the infrastructure in place to access telehealth. Few studies have evaluated the impact of geographic proximity to services on telehealth utilization. During the first year of the pandemic, two studies, one self-report from a nationally representative sample, and another medical records-based study of a single institution’s cancer patients, found that patients living in more rural areas were less likely to use telehealth than patients living in more urban areas (Jewett et al., 2022; Smith & Blavin, 2021). A single study of patients receiving family medicine services during the first month of the pandemic found the opposite (Pierce & Stevermer, 2020).

Age Disparities in Telehealth Use

Of the studies that examined the impact of age on telehealth utilization during the COVID-19 pandemic, the majority found that older patients were less likely to use telehealth than younger patients (Jewett et al., 2022; Neeman et al., 2021; Pierce & Stevermer, 2020; Qian et al., 2021; Rivera et al., 2021; Sachs et al., 2021; Weber et al., 2020; Wegermann et al., 2022). However, one study failed to find differences by age (Hayrapetian et al., 2021) and another found younger adults (ages 31–45 years) were less likely to use telehealth than older adults (ages 40–60 years; Friedman et al., 2022).

To answer questions related to telehealth accessibility, the present study aimed to compare demographic and clinical characteristics of pediatric patients evaluated in person versus via telehealth. We hypothesized, based on the existing literature, that higher rates of telehealth use would be found among patients of higher SES and among patients who are White. Given the lack of prior studies with pediatric samples, examination of the impact of child age on telehealth use was considered exploratory in the present study. Similarly, the existing literature’s focus on rates of telehealth use among rural versus urban patients does not map well onto our study data focused on distance to clinic; therefore, examination of this factor will also be considered exploratory.

Methods

Participants

Participants included 2,257 pediatric patients seen for psychological evaluation between July 1, 2019 and December 2021 in a large, outpatient department of an urban, East Coast academic medical center. All patients included in this study were diagnosed with ADHD by a clinical psychologist based on DSM-5 criteria as part of the clinical evaluation. Table 1 presents descriptive information about the sample. The majority (58%) met criteria for a diagnosis of ADHD, Combined Presentation, while 40% met criteria for ADHD, predominantly Inattentive Presentation and only 2% for ADHD, predominantly Hyperactive/Impulsive Presentation. Common comorbid conditions within the sample (in order of frequency) included anxiety, specific learning disability in reading, specific learning disability in math, specific learning disability in writing, depression, mixed receptive-expressive language disorder, and intellectual disability. Ninety-nine percent of patients reported English as their primary language. Within this sample, 28% of evaluations were completed via telehealth. Participants represent three naturally occurring groups: those who completed evaluations in-person pre-COVID (n = 780), those who were evaluated in person during COVID (n = 839), and those who were evaluated by telehealth during COVID (n = 638). This study was approved by the local institutional review board.

Table 1.

Differences on Demographic and Clinical Characteristics Before and During COVID, In-Person and Telehealth.

	Pre-COVID in person		During COVID in person		During COVID telehealth		F value/χ²	p Value	η²/Cramer’s V
	n	Mean (SD)/%	N	Mean (SD)/%	N Mean (SD)/%		F value/χ²	p Value	η²/Cramer’s V
Age at visit	780	11.05 (3.39)	839	10.70 (3.48)	638	11.22 (3.36)	4.66	.010	.004
Race							10.08	.039	.047
Black	241	30.90%	258	30.71%	177	27.74%
White	420	53.85%	411	48.93%	334	52.35%
Other	119	15.26%	171	20.36%	127	19.91%
Ethnicity							0.73	.693	.023
Hispanic/Latinx	37	9.00%	42	7.94%	40	9.46%
Non-Hispanic/Non-Latinx	374	91.00%	487	92.06%	383	90.54%
Sex							5.70	.222	.036
Female	268	34.36%	305	36.31%	244	38.24%
Male	512	65.64%	533	63.45%	394	61.76%
Insurance type							5.56	.062	.049
Public	317	40.64%	320	38.10%	221	34.64%
Commercial	463	59.36%	520	61.90%	417	65.36%
ADHD presentation							5.52	.238	.035
Inattentive	289	37.05%	330	39.29	273	42.79
Hyperactive/impulsive	18	2.31%	17	2.02	16	2.51
Combined	473	60.64%	493	58.69	349	54.70
Comorbidities
Reading SLD	184	30.31%	196	29.70%	115	25.22%	3.79	.150	.047
Math SLD	133	23.05%	163	26.38%	78	18.48%	8.79	.012	.074
Writing SLD	115	23.28%	105	22.06%	55	16.37%	6.20	.045	.069
Intellectual disability	68	9.07%	66	8.26%	40	6.70%	2.52	.283	.034
Depression	138	20.26%	158	21.50%	97	17.48%	3.31	.191	.041
Anxiety	329	48.89%	386	54.21%	260	50.19%	4.37	.113	.048
MRELD	110	17.54%	110	16.30%	74	13.31%	4.10	.129	.047
Distance from clinic > 19 mi.	369	47.74%	397	47.49%	344	54.52%	9.62	.008	.067

Note. SLD = specific learning disability; MRELD = mixed receptive expressive language disorder. p Values less than .05 are bolded.

Procedures

Demographic and clinical characteristics were extracted from the hospital’s electronic medical record for analysis. De-identified data were analyzed under an approved IRB protocol. These included race (Black vs. White vs. Other/Multiracial), ethnicity (Hispanic/Latinx vs. non-Hispanic/Non-Latinx), age at time of visit, insurance type (commercial vs. public insurance), ADHD diagnostic presentation (Inattentive vs. Hyperactive/Impulsive vs. Combined), comorbid conditions (Specific Learning Disorders, Intellectual Disability, Mixed Receptive-Expressive Language Disorder, depression, anxiety), and patient’s home zip code.

Patient race was caregiver-reported, with the large majority of patients reported as Black or White. Due to the small number of responses indicating other races, these were collapsed into an “other or multiracial” category for the purpose of statistical analysis. Patient ethnicity was also caregiver-reported, but was missing for 40% of the sample. These data were collected during a brief phone intake that all patients new to the institution complete prior to being scheduled for a clinical visit, and, unfortunately, they were not reliably collected during the study period.

Prior to the onset of the COVID-19 pandemic in March 2020, all clinical evaluations were completed in person. Starting in late March 2020, in response to the pandemic, clinical evaluations were only offered via telehealth until July 2020. Since July 2020, clinical evaluations have been offered both in person and via telehealth. Decisions about which modality is most appropriate for a given patient are made by providers in collaboration with patients and families, taking into account a wide variety of factors. This decision-making process is detailed in Pritchard et al. (2020).

Both ADHD and comorbid conditions were identified or confirmed during the course of the clinical evaluation, based on DSM-5 criteria. Diagnoses were identified or confirmed using review of records, detailed history information, information gained via clinical interviews, and the results of performance-based testing and rating scales.

A continuous distance to clinic variable was calculated as the distance between the central point of the patient’s zip code and the clinic location. The resulting variable was highly positively skewed, with the majority of patients living closer to the clinic. Therefore, the continuous distance to clinic variable was recoded into a dichotomous variable with a cut point at the 50^th percentile (19 miles from the clinic).

Analysis Plan

Both descriptive (e.g., means, standard deviations) and bi-variate (e.g., ANOVAs and chi-square) statistics were used to summarize and examine group differences across the three groups (in-person pre-COVID, in-person during COVID, and telehealth during COVID). Minimal (<5%) data were missing for the majority of study variables. Study variables with more than 5% missing data included: ethnicity (40% missing) and all comorbid conditions with the exception of Intellectual Disability (ranging from 5% to 42% missing). All analyses were performed in STATA 13.0 (College Station, TX).

Results

Patient ethnicity, sex, and insurance type proportions were not significantly different across the in-person and telehealth evaluation groups (see Table 1). Similarly, the groups did not differ significantly in terms of ADHD presentation.

Age at visit, distance from clinic, race, and comorbid Specific Learning Disorders (SLD) in math and writing were significantly different across groups (see Table 1). Patients seen for evaluation in person pre-COVID tended to be 11 years, on average. During COVID, patients seen in person were significantly younger, by approximately 6 months, than patients evaluated via telehealth.

A significantly larger proportion of the telehealth group was comprised of patients who resided farther from the clinic (55%) than either in-person group (47% both groups).

Black patients were slightly underrepresented in the telehealth group, with approximately 3% fewer Black patients in the telehealth group than in either in-person group. Patients who identified as other races or multiracial represented a (approximately 5%) larger proportion of the overall patient population evaluated during COVID than pre-COVID. These patients were similarly distributed among the in-person and telehealth groups during COVID. White patients were overrepresented in the telehealth group relative to the group seen in person during COVID; however, telehealth rates for this group were similar to pre-COVID in-person rates.

Patients evaluated by telehealth were significantly less likely to have comorbid SLD in math or writing than those evaluated in person. Rates of other comorbid conditions (including SLD in reading, intellectual disability, depression, anxiety, and Mixed Receptive-Expressive Language Disorder) were not statistically significantly different across groups; however, overall, comorbid conditions were less frequently diagnosed in patients evaluated via telehealth.

Discussion

During the COVID-19 pandemic, the use of telehealth services increased for a variety of patient populations. While several studies have assessed demographic disparities in telehealth use, few have examined telehealth access among youth evaluated for mental health or neurodevelopmental conditions. The current study is the first to evaluate telehealth disparities based on clinical and demographic characteristics in children with ADHD. We found that patients evaluated by telehealth were more likely to be older, White, be diagnosed with fewer comorbid conditions, and live farther from the clinic.

Our findings regarding age are inconsistent with previous studies with adults, where older adults were less likely to utilize telehealth services than younger adults. In our pediatric sample, children evaluated by telehealth tended to be older, which may reflect developmental attentional and self-regulatory capacities that impact participation in evaluations conducted via telehealth. In-person testing allows clinicians to apply a more hands-on approach to behavioral management, which may be necessary for younger patients who might generally struggle more with attention and behavior regulation. Both clinicians and caregivers typically have input into the decision of whether to conduct an assessment via telehealth so that age and attentional and behavioral characteristics of the patient can factor into the decision. Our findings regarding age are consistent with a recent study of caregiver satisfaction with telehealth evaluations which found that caregivers of older children were more likely to indicate that they would use telehealth again, compared with parents of younger children (Zabel et al., 2022).

Consistent with some previous studies (Friedman et al., 2022; Jewett et al., 2022; Neeman et al., 2021; Pierce & Stevermer, 2020; Rivera et al., 2021; Sachs et al., 2021; Weber et al., 2020; Wegermann et al., 2022), we found that White patients were more likely to use telehealth than Black patients. Unfortunately, limitations of the present data do not allow us to speak to why this is the case—whether this finding reflects shared decision making between clinicians and parents, parent preferences, or clinician preferences alone. Patients of other races were equally likely to be evaluated via telehealth as in person during the pandemic; however, these patients of other races were assessed at higher rates overall during COVID than before COVID. This finding may reflect procedural changes in data collection over the course of the study period, rather than a true increase in other race or multiracial patients being assessed. The start of the study period coincided with implementation of a new electronic medical records system for the institution. In the process of adjusting to this new system’s requirements over the course of the 2 years of the study period, institutional intake personnel may have become more accurate and consistent in collecting demographic information such as race and ethnicity, expanding the options for the other and multiracial categories.

The current study found that children with more complex presentations (i.e., those with more comorbidities) were less likely to be assessed via telehealth. In particular, children with SLD in math and writing were significantly less likely to be evaluated using telehealth. Though not statistically significant, this pattern was consistent across the other comorbid conditions included: SLD in reading, intellectual disability, depression, anxiety, and MRELD. This pattern of findings may reflect hesitancy on the part of clinicians to assess more complex cases via the telehealth format or limitations of the telehealth format for assessing the specific skills necessary to make a particular diagnosis. Several factors may contribute to this. First, some skills are less amenable to assessment via telehealth: fine motor and writing skills, for instance, are especially challenging to assess remotely, given the particular demands of the assessment tasks for these skills. Additionally, clinicians may be wary of using telehealth for assessing “high stakes” conditions, such as intellectual disability, for fear that schools and agencies may not accept the findings of evaluations conducted via telehealth due to concerns about validity of the modality. Finally, clinicians may prefer to evaluate patients presenting with emotional concerns (e.g., suicidality) in person in case the severity of the concern warrants immediate clinical action. This pattern of findings may also reflect a shared decision-making process between families and clinicians such that parents of children with more complex presentations may also prefer or request in-person assessment.

As hypothesized, we found that distance to clinic was significantly associated with telehealth use such that those who lived farther away were more likely to be evaluated via telehealth. This finding preliminarily suggests that telehealth may reduce at least one barrier to care for patients who live farther away from services. However, as the clinic is located in an urban area in close proximity to several other urban areas, distance to clinic does not necessarily indicate whether the patient’s home is in a rural or an urban area. Nevertheless, distance to clinic is a barrier to access. For those without means of travel, telehealth provides one approach to reducing that barrier.

SES, as indicated by insurance type, was not significantly associated with assessment modality in the present study, though the pattern of findings suggests a trend toward patients with public insurance having lower telehealth than in-person utilization rates during COVID. This trend is consistent with much of the existing literature (Neeman et al., 2021; Pierce & Stevermer, 2020; Rivera et al., 2021; Sachs et al., 2021; Wegermann et al., 2022), but did not reach statistical significance. The discrepancy between our findings and existing literature may be related to the fact that our institution adopted various strategies to improve telehealth access for lower SES families during the pandemic. At the start of the pandemic, our institution obtained a federal grant to purchase laptops, iPads, and WiFi hotspots to loan to families to ensure that lack of access to appropriate technology was not a barrier to telehealth use. Although the rate of telehealth utilization tended to be slightly lower in our clinic for patients with public insurance, the small magnitude of this difference could well be due to the particular supports that were put in place for families in need at the start of COVID.

Although the present study fills an important gap in the literature, there are limitations. Since our clinics did not offer telehealth services prior to the onset of the pandemic, we do not have a pre-COVID telehealth comparison group; therefore, we cannot adequately evaluate cohort effects in telehealth use that may be due to COVID. We did, however, attempt to mediate this concern somewhat by including comparison of in-person data pre-COVID versus data during COVID. No data were available for the present study regarding urbanicity or rurality of patients’ homes. Additionally, data were not available regarding caregiver age. Given that caregivers, rather than patients themselves, are typically responsible for managing the technology necessary for a telehealth appointment, this seems relevant. Caregivers are also the ones consenting (or not consenting) to a telehealth appointment; thus, their age and familiarity with technology are relevant variables in this context and are important issues for future studies to address. While the present data represent a large number of pediatric patients, the data set was not large enough to examine individual COVID “waves” to determine whether telehealth use increased in proportion to COVID case rates in the local population. Similarly, our dataset is not large enough to allow for examination of all racial and ethnic groups separately, as discussed above. Comparison of the racial breakdown of our sample to that of the geographic region in which the study was conducted indicates that the study sample appears to be largely representative of the geographic region in terms of race. United States Census data for the state in which the clinic is located indicates that 48.7% of state residents identify as White, while 29.5% identify as Black, a comparable breakdown to the sample demographics presented in Table 1 (United States Census Bureau, 2020). A final limitation of the present study is the degree of missing data for the ethnicity variable. With more than 1/3 of the study sample missing these data, and not knowing whether these data are missing completely at random, our study findings related to ethnicity should be interpreted cautiously.

Overall, the findings of this study suggest that telehealth has not been a panacea for healthcare disparities. Clearly, some of the barriers to in-person care that certain patients experience are not effectively addressed by telehealth. The telehealth modality does, however, seem to be useful for certain clinical and demographic groups, such as patients with less complex ADHD, older children with ADHD, and patients who live farther from the clinic. Additionally, in the context of technological supports to minimize barriers, the telehealth modality seems to be equally useful regardless of SES. Going forward, providers and institutions must determine how to encourage telehealth use among diverse patient populations. This might be accomplished in a wide variety of different ways. To encourage uptake of telehealth among Black patients, telehealth advocates might partner with Black community leaders to bring awareness to and address community concerns around telehealth use. To allow younger children to be evaluated remotely more often, providers might develop tools (e.g., YouTube videos) to train caregivers, and patients themselves, in attention and behavior management strategies prior to the appointment. To encourage clinicians to consider using telehealth for more diagnostically complex patients, professional development around topics such as safety planning via telehealth might be helpful. In addition, the advent of remotely administered measures that are normed in this context will go a long way toward addressing clinician concerns about remote evaluation of skills such as written expression. Finally, published policies around acceptance of telehealth evaluations from schools and social service agencies will be critical in assuaging clinicians’ potential concerns about performing “high stakes” evaluations remotely.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Eunice Kennedy Shriver National Institutes of Child Health and Human Development (NICHD) award P50 HD103538.

ORCID iDs

Alison E. Pritchard

Rebecca Lieb

Lisa A. Jacobson

Author Biographies

Alison E. Pritchard, PhD, ABPP is a board-certified clinical child and adolescent psychologist and co-director of the Neuropsychology Department at the Kennedy Krieger Institute. She also holds an appointment as Assistant Professor in the Johns Hopkins University School of Medicine’s Department of Psychiatry & Behavioral Sciences.

Rachel A. Northrup, BA, is a research assistant in the Neuropsychology Department at the Kennedy Krieger Institute.

Rachel Peterson, PhD, is a neuropsychologist in the Neuropsychology Department at the Kennedy Krieger Institute. She also holds an appointment as Assistant Professor in the Johns Hopkins University School of Medicine’s Department of Psychiatry & Behavioral Sciences.

Rebecca Lieb, PhD, ABPP, is a board-certified clinical child and adolescent psychologist in the Neuropsychology Department at the Kennedy Krieger Institute. She also holds an appointment as Assistant Professor in the Johns Hopkins University School of Medicine’s Department of Psychiatry & Behavioral Sciences.

Danielle Wexler, Ph.D., is a child and adolescent clinical psychologist in the Neuropsychology Department at the Kennedy Krieger Institute.

Rowena Ng, Ph.D., is a neuropsychologist in the Neuropsychology Department at the Kennedy Krieger Institute. She also holds an appointment as Assistant Professor in the Johns Hopkins University School of Medicine’s Department of Psychiatry & Behavioral Sciences.

Luke Kalb, Ph.D., is an epidemiologist who serves as Director of Informatics at the Kennedy Krieger Institute’s Center for Autism and Related Disorders. He also holds an appointment as Assistant Professor in the Department of Mental Health at the Johns Hopkins Bloomberg School of Public Health.

Natasha Ludwig, Ph.D., is a neuropsychologist in the Neuropsychology Department at the Kennedy Krieger Institute. She also holds an appointment as Assistant Professor in the Johns Hopkins University School of Medicine’s Department of Psychiatry & Behavioral Sciences.

Lisa A. Jacobson, Ph.D., ABPP, is a board-certified neuropsychologist and Director of Research in the Neuropsychology Department at the Kennedy Krieger Institute. She also holds an appointment as Associate Professor in the Johns Hopkins University School of Medicine’s Department of Psychiatry & Behavioral Sciences.

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