HCM 340 Module 7 Final Project Research Paper example

Closing the Diagnostic Wait: A Delivery Systems Analysis of Autism Evaluation for Young Children and a Combined Model for Faster Diagnosis

[Student Name]

Southern New Hampshire University

HCM 340: Healthcare Delivery Systems

Module Seven Final Project

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Closing the Diagnostic Wait: A Delivery Systems Analysis of Autism Evaluation for Young Children and a Combined Model for Faster Diagnosis

Introduction

Autism spectrum disorder is now identified in about 1 in 31 eight-year-olds in the national surveillance network, and professional guidelines call for every toddler to be screened at 18 and 24 months (Hyman et al., 2020; Shaw et al., 2025). Screening is widely delivered in primary care. Diagnosis is not. After a positive screen, most families are referred to a small number of specialty centers and join a waiting list that can run for many months. This paper analyzes that wait as a gap in the healthcare delivery system, identifies who bears it, compares four initiatives designed to shorten it and recommends a combined model for a composite Midwestern state.

The Gap

The gap is the interval between the point at which a young child is flagged for possible autism and the point at which the child receives a diagnostic evaluation. It is measurable at the population level. In 2022, the median age at which children in the surveillance network were first diagnosed was 47 months, with a spread of 33.5 months between the fastest and slowest sites (Shaw et al., 2025). Because experienced clinicians can make the diagnosis by about age two, most of that time is delay rather than biology. A review of diagnostic models attributed a roughly two-year lag between first signs and diagnosis to waiting lists, long evaluations, cost and a shortage of clinicians (Gordon-Lipkin et al., 2016).

The delay matters because diagnosis is the key that opens services. Insurance approval for intensive behavioral therapy and eligibility for many educational and state programs depend on it. Early, intensive intervention improves outcomes: in a randomized trial of toddlers diagnosed between 18 and 30 months, children who received two years of a comprehensive developmental behavioral intervention gained an average of 17.6 standard score points in IQ, compared with 7.0 points for children referred to community services (Dawson et al., 2010). Every month a child spends on a waiting list is a month taken from the period in which treatment does the most good.

The Population Affected

The population is children roughly 18 to 48 months old with a positive screen or a developmental concern, together with their families. The wait is longest for groups the delivery system already serves poorly. Families with Medicaid face fewer participating specialists. Rural families face travel of several hours to reach a regional center, often more than once. Parents who speak a language other than English meet scarce bilingual evaluators and more difficult scheduling. The wide variation in age at diagnosis across surveillance sites suggests that geography, and therefore the structure of local services, shapes a child's chances of timely diagnosis. The burden extends to parents, who spend months aware of a problem without a name for it or a plan, and to early intervention and school systems that receive children later than they could have.

Causes Within the Delivery System

The gap has four structural causes. Diagnosis is concentrated in specialty care, so demand from a whole region converges on one or two clinics. Traditional evaluations are long and multidisciplinary, which limits each clinic's throughput. Primary care clinicians, who see young children most often, have rarely been trained or paid to diagnose autism, and the share of diagnoses made in primary care declined each year between 2004 and 2019 even though primary care diagnoses came about a year earlier (Smith et al., 2024). Finally, rising prevalence has increased demand faster than the specialist workforce has grown. In the composite state used in this paper, one hospital-based developmental clinic completes about 700 evaluations a year against about 1,400 referrals, and a newly referred family waits about 13 months for its first visit.

Existing Initiatives Compared

Four initiatives address the gap from different directions, as summarized in Table 1.

Table 1

Comparison of Initiatives to Shorten the Wait for Autism Diagnosis

Note. Ratings are the author's judgments based on the cited studies.

The first is the primary care hub model. Indiana's Early Autism Evaluation Hub system trains community primary care clinicians to diagnose autism with ongoing support from specialists. In a blinded comparison of 126 children, hub clinicians and experts reached the same diagnosis for 82 percent, and a positive hub diagnosis was confirmed 92.6 percent of the time; most disagreements involved children with global developmental delay (McNally Keehn et al., 2023). Caregivers reported high satisfaction regardless of the child's diagnosis (Martin et al., 2024).

The second is diagnosis within a single pediatric medical home. In one practice where the pediatrician served as primary diagnostician, families waited an average of 3.5 months between referral and diagnosis, and half the children were diagnosed by 34.5 months of age, although the published data covered only eight children (Nasir et al., 2024).

The third is caregiver-mediated tele-assessment. Using the TELE-ASD-PEDS, a psychologist observes by video while a parent leads the child through structured activities at home; a center that adopted the approach during the pandemic reported that it kept evaluations going and that clinicians found it acceptable (Wagner et al., 2021).

The fourth redesigns access within specialty centers. Two autism centers used systems analysis to find sources of delay and change scheduling and workflow; one cut its follow-up waiting list from 99 patients to 6, and the other reduced the third next available appointment for new physician visits for children aged 3 to 5 by 94 percent (Austin et al., 2016).

Recommendation: A Combined Model

No single initiative closes the gap alone, but their strengths are complementary. For the composite state, this paper recommends a combined model with four parts. First, establish primary care diagnostic hubs in each region, starting with practices in counties more than an hour from the specialty center, and train their clinicians through the academic center. Second, give hubs direct access to tele-assessment by specialists for children whose presentation is unclear, especially those with global developmental delay, the group where hub accuracy was weakest. Third, apply access redesign at the specialty center so that the specialists freed from routine evaluations can see complex cases quickly. Fourth, add a family navigator at each hub who explains the diagnosis and links the family to early intervention, therapy and school services within weeks, which answers the caregivers' request for clearer guidance after diagnosis.

Payment is the enabling condition. The state Medicaid program should pay hub clinicians for the extended time an evaluation requires and pay for tele-assessment on the same terms as in-person evaluation. Without payment that covers the work, primary care practices will not sustain the role.

Stakeholders and Barriers

Key stakeholders are families, primary care practices, the academic specialty center, the state Medicaid agency, the early intervention program, school districts and commercial insurers. Each faces barriers. Primary care practices may resist a time-intensive service in already crowded schedules. Specialists may worry about diagnostic accuracy. Insurers may question diagnoses made outside specialty settings when approving costly therapy. Families may doubt a diagnosis from their pediatrician rather than a specialist. The model addresses these concerns with specialist support and consultation, published accuracy data, clear criteria for referring complex cases and the option of specialist review by video.

Measuring Success

The model should be judged by measures tied to the gap. The primary outcome is the median time from positive screen to diagnostic decision, reported by region, payer and primary language, with a target of under 90 days. Secondary measures include median age at diagnosis, the share of evaluations completed in primary care, agreement between hub and specialist diagnoses on audited cases, the time from diagnosis to the start of services and caregiver satisfaction. Reporting by subgroup is essential, because an average improvement could hide continued delays for rural, Medicaid-covered or non-English-speaking families.

Conclusion

The wait for autism diagnosis is a delivery system problem: a needed service concentrated where too few clinicians can provide it. Evidence shows that trained primary care clinicians can diagnose most young children accurately, that tele-assessment can extend specialist reach and that specialty centers can shorten their own queues. Combining these approaches, backed by payment and navigation, offers a realistic path to diagnosis within months rather than years, and with it, earlier treatment during the period when it matters most.

References

Austin, J., Manning-Courtney, P., Johnson, M. L., Weber, R., Johnson, H., Murray, D., Ratliff-Schaub, K., Tadlock, A. M., & Murray, M. (2016). Improving access to care at autism treatment centers: A system analysis approach. Pediatrics, 137(Suppl. 2), S149-S157. https://doi.org/10.1542/peds.2015-2851M

Dawson, G., Rogers, S., Munson, J., Smith, M., Winter, J., Greenson, J., Donaldson, A., & Varley, J. (2010). Randomized, controlled trial of an intervention for toddlers with autism: The Early Start Denver Model. Pediatrics, 125(1), e17-e23. https://doi.org/10.1542/peds.2009-0958

Gordon-Lipkin, E., Foster, J., & Peacock, G. (2016). Whittling down the wait time: Exploring models to minimize the delay from initial concern to diagnosis and treatment of autism spectrum disorder. Pediatric Clinics of North America, 63(5), 851-859. https://doi.org/10.1016/j.pcl.2016.06.007

Hyman, S. L., Levy, S. E., Myers, S. M., & Council on Children With Disabilities, Section on Developmental and Behavioral Pediatrics. (2020). Identification, evaluation, and management of children with autism spectrum disorder. Pediatrics, 145(1), e20193447. https://doi.org/10.1542/peds.2019-3447

Martin, A. M., Huskins, J., Paxton, A., Nafiseh, A., Ciccarelli, M. R., Keehn, B., & McNally Keehn, R. (2024). Mixed methods analysis of caregiver satisfaction with the Early Autism Evaluation Hub system. Journal of Patient Experience, 11. https://doi.org/10.1177/23743735241305531

McNally Keehn, R., Swigonski, N., Enneking, B., Ryan, T., Monahan, P., Martin, A. M., Hamrick, L., Kadlaskar, G., Paxton, A., Ciccarelli, M., & Keehn, B. (2023). Diagnostic accuracy of primary care clinicians across a statewide system of autism evaluation. Pediatrics, 152(2), e2023061188. https://doi.org/10.1542/peds.2023-061188

Nasir, A. K., Strong-Bak, W., & Bernard, M. (2024). Diagnostic evaluation of autism spectrum disorder in pediatric primary care. Journal of Primary Care & Community Health, 15. https://doi.org/10.1177/21501319241247997

Shaw, K. A., Williams, S., Patrick, M. E., Valencia-Prado, M., Durkin, M. S., Howerton, E. M., Ladd-Acosta, C. M., Pas, E. T., Bakian, A. V., Bartholomew, P., Nieves-Muñoz, N., Sidwell, K., Alford, A., Bilder, D. A., DiRienzo, M., Fitzgerald, R. T., Furnier, S. M., Hudson, A. E., Pokoski, O. M., . . . Maenner, M. J. (2025). Prevalence and early identification of autism spectrum disorder among children aged 4 and 8 years: Autism and Developmental Disabilities Monitoring Network, 16 sites, United States, 2022. MMWR Surveillance Summaries, 74(2), 1-22. https://doi.org/10.15585/mmwr.ss7402a1

Smith, J. V., Menezes, M., Brunt, S., Pappagianopoulos, J., Sadikova, E., & Mazurek, M. O. (2024). Understanding autism diagnosis in primary care: Rates of diagnosis from 2004 to 2019 and child age at diagnosis. Autism, 28(10), 2637-2646. https://doi.org/10.1177/13623613241236112

Wagner, L., Corona, L. L., Weitlauf, A. S., Marsh, K. L., Berman, A. F., Broderick, N. A., Francis, S., Hine, J., Nicholson, A., Stone, C., & Warren, Z. (2021). Use of the TELE-ASD-PEDS for autism evaluations in response to COVID-19: Preliminary outcomes and clinician acceptability. Journal of Autism and Developmental Disorders, 51(9), 3063-3072. https://doi.org/10.1007/s10803-020-04767-y