Accepted for/Published in: JMIR Formative Research
Date Submitted: Oct 23, 2025
Date Accepted: Jul 23, 2026
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Multi-Level Evaluation of ECHO Autism Programs: Protocol for the Development of a Standardized Minimum Dataset
ABSTRACT
Background:
The rising prevalence of autism highlights the challenges faced by healthcare professionals, educators, and guardians in managing autism care. ECHO Autism Communities was established to train professionals in best practice autism care and diagnosis through regular virtual case-based learning sessions. The programmatic offerings and global expansion of the ECHO Autism model led to a need for standardized outcome measures to evaluate effectiveness.
Objective:
In this study, a review of existing measures was conducted, and a minimum dataset (MDS) was created.
Methods:
The study team collected 42 surveys from existing programs, organized all survey metrics, and refined them into a standardized set of metrics. The MDS was designed to measure priority information, set consistent scales and anchors, use inclusive language, standardize response choices, and determine units of analysis to transform data into actionable information. An early version of the MDS was translated into Spanish and piloted in programs in the United States, Argentina, and Chile.
Results:
Pilot data provided actionable insight so that adjustments were made before ongoing evaluation. The first two years of MDS implementation showed consistency in evaluation and significant improvements in self-efficacy across eight programs.
Conclusions:
As a result of this study, ECHO Autism leaders can assess and compare program effectiveness, participant characteristics, and demonstrate the program’s impact on autism care. This study highlights the importance of consistent outcome measures for evaluation, enabling data-informed decisions, and continuous improvement of autism care.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.