Evidence map›Paper›PMID 38576888›Full record

ArticlePediatric quality & safety

Identifying Autism Spectrum Disorder in a High-risk Follow-up Program through Quality Improvement Methodology.

Christine M Raches, Elesia N Hines, Abbey C Hines, Emily K Scott

Abstract read
In one paragraph

Article in Pediatric quality & safety. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Christine M RachesFrom the Division of Developmental Medicine, Department of Pediatrics,Indiana University School of Medicine.
Elesia N HinesFrom the Division of Developmental Medicine, Department of Pediatrics,Indiana University School of Medicine.
Abbey C HinesDivision of Neonatology, Department of Pediatrics, Indiana University School of Medicine.
Emily K ScottDivision of Neonatology, Department of Pediatrics, Indiana University School of Medicine.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Children born prematurely are at increased risk for autism spectrum disorder (ASD). ASD can be diagnosed between 18 and 24 months of age, but access barriers and medical complexity can delay diagnosis. ASD screening was implemented in a high-risk infant follow-up program using QI methodology. The project aimed to screen 60% of children and refer 90% of those with positive screens. Methods: The team developed a standardized workflow to administer the M-CHAT-R/F to HRIF patients between the ages of 16-22 months. Telehealth ASD assessment, using the TELE-ASD-PEDS, was conducted for those who screened positive. Monthly team meetings were held to implement change cycles and review the impact of the previous month's change. Results: Within 7 months of program implementation, ASD screening exceeded the 60% aim. The program referred 72% of patients who screened as medium/high risk on the M-CHAT-R/F. The remaining patients were not referred per provider discretion. Twenty-seven percent of patients who received an autism evaluation received an ASD diagnosis. The average age at diagnosis was 22.5 months. Conclusions: An ASD screening protocol was implemented for patients enrolled in a high-risk infant follow-up program. Patients identified as at risk for ASD received an expedited telehealth ASD evaluation. The screening protocol was maintained for 13 months and is now part of the standard workflow. Screening has been expanded to other HRIF clinics, and evaluation appointments have been added to meet access needs. QI methodology is an effective tool for implementing ASD screening and referral in multidisciplinary HRIF programs.

Identifiers

PMID38576888
PMCPMC10990309

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.