📊 Using Big Data to Inform ABA Hour Recommendations | Dr. David Cox on Cooperant Podcast Description: How can big data improve clinical decision-making in behavior analysis? In this episode of the Cooperant Podcast, host Kyle Steury welcomes Dr. David Cox—PhD behavior analyst, researcher at Endicott College, and expert in AI and clinical modeling—to discuss data-driven approaches to recommending ABA service hours.

🎙 What You’ll Learn in This Episode: Why current hour recommendations in ABA may be more guesswork than science How Dr. Cox’s team analyzed data from nearly 40,000 cases The role of dose-response curves and cluster analysis in identifying optimal therapy levels How data indicate that more isn’t always better when it comes to ABA hours Implications for adult services, funding decisions, and the ethics of using AI in clinical care How real-time data could one day guide session-level decisions for RBTs and BCBAs

🔍 Key Concepts: Behavior analysis | Artificial intelligence | ABA therapy | Data science | Clinical modeling | Ethical decision-making | Big data | Cluster analysis | Autism services | Service authorization | Individualized care

🧠 Guest Bio – Dr. David Cox: Dr. Cox holds a PhD in behavior analysis, completed postdoctoral work at Johns Hopkins and Insight Data Science, and currently researches big data and AI applications in clinical decision-making and behavior analytic practice at Endicott College. His work explores how advanced data models can support ethical, individualized, and efficient behavioral healthcare.

💡 Interested in how behavioral science and machine learning can work together to improve client outcomes? This episode is for you.

Cooperant Learning is a project of Sparks Behavioral Services. Learn more about Sparks at https://www.sparksbehavioralservices.com.