Learning Healthcare System Podcast
Learning Healthcare System Podcast
Podcast Description
This is the pan-informatics Learning Healthcare System Podcast. We dive into details of what a Learning Heathcare System is. The show is targeted to people working in healthcare who are trying to improve care, outcomes and operations using the latest technolgies. We cover the importance of Artificial Intelligence in all it's guises (Predictive Models, Large Language Models and more) as well as what it takes to gather data in todays world, and the importance of a privacy preserving model.
Podcast Insights
Content Themes
The podcast centers on the principles of Learning Healthcare Systems, emphasizing themes like Artificial Intelligence, clinical trial matching, data privacy, and healthcare innovation, with episodes exploring specific challenges like managing data silos in matching patients for clinical trials and discussing the maturity model for building effective healthcare systems.
This is the pan-informatics Learning Healthcare System Podcast. We dive into details of what a Learning Heathcare System is. The show is targeted to people working in healthcare who are trying to improve care, outcomes and operations using the latest technolgies. We cover the importance of Artificial Intelligence in all it’s guises (Predictive Models, Large Language Models and more) as well as what it takes to gather data in todays world, and the importance of a privacy preserving model.
How do we find the right patients for the right clinical trials? In this episode, James Green (CEO, Cognome) and Dr. Parsa Mirhaji (Albert Einstein College of Medicine) discuss the complexities of clinical trial matching and how AI-driven learning health systems can transform patient recruitment.
They explore:
✅ The role of Agentic AI in understanding trial criteria
✅ Challenges of data silos, redundancy, and quality in hospitals
✅ oTESSA, an AI-powered tool enhancing trial matching with justification & transparency
✅ How soft criteria can improve trial eligibility over time
✅ The impact of reinforcement learning in making trial matching more effective
From oncology to breast cancer, this conversation dives deep into how AI, domain knowledge, and institutional context shape the future of clinical research.
Tags: #ClinicalTrialMatching, #AIinHealthcare, #LearningHealthSystem, #AgenticAI, #ClinicalTrialRecruitment, #OncologyTrials, #BreastCancerResearch, #HealthcareData, #PatientEligibility, #ReinforcementLearning, #AITransparency, #TESSA, #MedicalAI, #HealthcareInnovation, #ClinicalResearch, #AIforGood, #PatientMatching, #DataSilos, #AIinMedicine, #HealthTech
Chapters:
Why Clinical Trial Matching is So Complex
The Role of AI in Identifying the Right Patients
Understanding Inclusion & Exclusion Criteria with AI
Tackling Data Silos, Redundancy & Quality Issues
Transparency, Justification & Eliminating AI Hallucination
Soft vs. Hard Criteria: Preparing Patients for Future Trials
The Future of AI in Healthcare & Just-in-Time Matching
Closing Thoughts & Next Steps for Clinical Trial AI
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