Using Artificial Intelligence to Improve Patient Outcomes in ABA matters because it changes what a BCBA notices when decisions have to hold up in clinic sessions and day-to-day service delivery. In Using Artificial Intelligence to Improve Patient Outcomes in ABA, for this course, the practical stakes show up in faster workflow without clinical drift, privacy loss, or weak oversight, not in abstract discussion alone.
Provider: BehaviorLive — via BABAT
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Join Free →For many, artificial intelligence (AI) had its coming out party in 2023. Now it's time for AI to live up to the hype. For those who already feel behind, fortunately, example AI use cases within and outside of ABA show that AI can be used to improve patient outcomes. In this presentation, we review one way that ABA providers 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 expected levels so that the appropriate action can be taken. Further, as outcome (and other quality) measures gain adoption, advanced analytics and AI present many opportunities for improving ABA service delivery such as: ABA hours / dosage recommender systems, patient-provider matching, treatment pathway analysis, and dynamic treatment recommender systems. As the saying goes, "AI is already here, it's just not evenly distributed." Audience members will come away with tractable ways they can move toward incorporating AI into their clinical processes to separate themselves from their peers on overall impact to care quality and cost. And, perhaps most importantly, how to do so ethically and responsibly.
| Certification Body | Credits | Type |
|---|---|---|
| BACB® | 1 | General |
Dr. David Cox can formally lay claim to being a bioethicist (master's degree from Union Graduate College), a board-certified behavior analyst at the doctoral level (PhD in behavior analysis from the University of Florida), a behavioral economist (post-doc training at the Behavioral Pharmacology Research Unit at Johns Hopkins University School of Medicine), and a data scientist (post-doc training through an Insight! Data Science Fellowship). He has worked in behavior analysis for 20 years as a clinician, academic researcher, scholar, technologist, and all-around behavior science junky. From his work and collaborations, David has published over 70 peer-reviewed articles, book chapters, and books. And, has had the fortune to serve as Editor in Chief for The Experimental Analysis of Human Behavior Bulletin and Associate or Guest Editor for Perspectives on Behavior Science, Behavior Analysis in Practice, Journal of Applied Behavior Analysis, Psychological Record, Education and Treatment of Children, Toward Data Science, and Behavior and Social Issues. When he's not doing research or building quantitative models of behavior-environment relations, he enjoys spending time with his wife, two beagles, and two kittens around St. John's, FL.
Dig into the research behind this topic — plain-English summaries written for BCBAs.
279 research articles with practitioner takeaways
258 research articles with practitioner takeaways
256 research articles with practitioner takeaways
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All behavior-analytic intervention is individualized. The information on this page is for educational purposes and does not constitute clinical advice. Treatment decisions should be informed by the best available published research, individualized assessment, and obtained with the informed consent of the client or their legal guardian. Behavior analysts are responsible for practicing within the boundaries of their competence and adhering to the BACB Ethics Code for Behavior Analysts.