How YOU Can Manage Clinical Quality in ABA Autism Service Settings
Jon Bailey and Bryant Silbaugh
This webinar will offer two perspectives on the issue of managing high-quality ABA therapy in autism service settings. The first presenter will define quality in terms of specifically making measurable changes in targeted behaviors as spelled out in the BIP. RBTs shoulder great responsibility in implementing interventions as planned, and their supervisors and clinical director are accountable for associated outcomes. The fundamental role of supervision is
reconsidered in light of a systems approach to ensuring high-quality ABA services and outcomes. The second presenter will present a model of managing behavioral intervention quality using a highly efficient estimation approach to treatment integrity assessment. The model will be followed by data from the real-world implementation of the model in an in-home and
community-based ABA autism service organization.
- Participants will be able to describe the downside of using the billable hour as a unit of measurement for service delivery.
- Participants will be able to describe managing for quality in terms of the “Juran trilogy” in relation to quality control.
- Participants will be able to describe a process for embedding quality control into routine
ABA service delivery.
February 28th, 12PM
As of now, we are planning on having Advocacy Day in person. It could be switched to virtual depending on the legislature’s schedule. We will keep you informed.
Heritage Hall – 100 N Capitol Ave, Lansing, MI 48933
Our lobbyist (Kelley Cawthorne) will arrange all appointments with legislators for individual members of the association and will contact you with all of the information.
Please note that legislative visits are sometimes difficult to secure, and all visits are coordinated to the best of the staff’s ability.
Registration closes February 26, 2024.
March 27th, 12PM
Advanced Analytics and Patient Outcomes in ABA
David J. Cox, Ph.D., M.S.B., BCBA-D
Patients, their caregivers, and payers often want to know exactly what they will get when receiving ABA and for how long it will last. They also often want to know how they can identify ABA providers who are better at providing ABA services compared to other providers. However, the complexity of ABA service delivery and idiosyncratic intervention and goal design make answering questions around patient outcomes challenging. In this presentation, we review categories of quality measurement stakeholders often seek and how advanced analytics (e.g., statistical modeling, artificial intelligence [AI]) allow us to answer questions about patient outcomes. Specifically, we show one way that ABA providers and payers can use AI to model and predict patient outcomes as a function of each patient’s AI-informed unique clinical profile. From there, all stakeholders can identify which patients are making progress above, at, or below expectations so that relevant action can be taken accordingly. Further, as outcome measures gain adoption, advanced analytics and AI present many opportunities for improving ABA such as: ABA hours dosage recommender systems, patient-provider matching, treatment pathway analysis, and dynamic treatment recommender systems to optimize patient outcomes.
- Describe the three types of quality measures and provide examples from ABA.
- Describe three methods for identifying patient profiles to account for clinical severity in analytics of patient outcomes.
- Describe how advanced analytics allow for predicting patient outcomes in ABA that can inform conversations around quality measurement, provider comparisons, and improved clinical decision making.