WEBINAR: March 27, 2024, 12pm

Advanced Analytics and Patient Outcomes in ABA
Speakers: David J. Cox, Ph.D., M.S.B., BCBA-D

TPatients, 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.

LEARNING OBJECTIVES

1. Describe the three types of quality measures and provide examples from ABA.
2. Describe three methods for identifying patient profiles to account for clinical severity in analytics of patient outcomes.
3. 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.

Note: 1 CEU will be offered after completion of survey.