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.
Learning Healthcare System Podcast
Claim This Podcastby James Green
4 episodes
Updated Bi-weekly
Accepts GuestsHas Sponsors
30
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality61
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Podcast Overview
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.
Language
🇺🇲
Publishing Since
12/9/2024
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30
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality61
Social0
YouTube0
Engagement0
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excellent
Episode Length
27 minutes
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Recent Episodes
February 4, 2025
Clinical Trial Matching
<p>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</p>
February 4, 2025
The Learning Health System Maturity Model (S1, E2)
<p>In this episode, we discuss what it takes to build a Learning Health System. Dr. Parsa Mirhaji has devised an eight level maturity model that we analyze in depth.Join us as we dive deep into the transformative power of advanced data and AI technologies in healthcare. This engaging conversation covers key topics such as: Master entity indexing and data lineage Real-time data ingestion and natural language processing (NLP) Leveraging large language models (LLMs) for healthcare innovation Ensuring data quality, privacy, and compliance Scalable, equitable access to data for a data-driven culture Responsible, explainable AI/ML and generative AI Integration of EHR and translation of research into practice The HIMSS Healthcare Analytics Maturity Model Pragmatic clinical trials, patient-reported outcomes, and precision medicine Explore how these innovations create a continuous learning and quality-improving health system, driving patient-centered and personalized medicine. Don’t miss this insightful discussion on the future of healthcare! tags: #HealthcareAI, #HealthcareAnalytics, #HIMSSMaturityModel, #DataQualityInHealthcare, #NLPHealthcare, #GenerativeAIHealthcare, #ExplainableAI, #PrecisionMedicine, #PersonalizedMedicine, #EHRIntegration, #PatientCenteredCare, #DataPrivacyHealthcare, #ComplianceEnforcementAI, #PragmaticClinicalTrials, #PopulationGenetics, #EquitableDataAccess, #PatientReportedOutcomes, #ResponsibleAI, #ScalableDataSystems, #HealthcareInnovation, #DrParsaMirhaji, #CognomeJamesGreen, #RemoteHealthcare, #RealTimeDataIngestion, #DataLineageHealthcare, #TrustableAI, #AIInMedicine, #HealthcareResearchTranslation, #KnowledgeSystems, #ScalableAISolutions Chapters: 0:00 Introduction 3:20 Maturity Level 1: The Foundational Layer 9:34 Comparison with HIMSS Analytic Maturity Model 11:08 Maturity Level 2: Open Science & Collaborative Research 15:05 Maturity Level 3: Equitable scalable access to and training 19:00 Maturity Level 4: Machine Learning & AI 20:34 Maturity Level 5: Translation to Practice 24:47 Maturity Level 6: Patient Empowerment 25:55 Maturity Level 7: Precision Medicine 27:45 Maturity Level 8: Learning Health System</p>
February 4, 2025
Going Beyond Atlas: Building a Learning Health System with Dr. Parsa Mirhaji (S1,E3)
<p>In this episode of our podcast, Dr. Parsa Mirhaji and James Green, CEO of Cognome, discuss the evolving landscape of healthcare data management. From real-world evidence generation to patient empowerment, they cover: Why OHDSI's Atlas falls short for learning health systems. Challenges with identified, de-identified, and limited-identified datasets. The implications of reidentification, IRB regulations, and compliance. Building a single-source dataset for better integration. Interoperable data ecosystems and frameworks for healthcare. The role of multimodal datasets: Bio repositories, PACS, Notes, and EMR. Self-service data extraction and auto-generated OHDSI OMOP datamarts. Patient-centered consent, privacy, and transparency in research. This episode is packed with insights on data quality, ontology management, and the future of healthcare interoperability. #real-world-evidence, #OHDSI, #Atlas-limitations, #learning-health-systems, #healthcare-data, #Dr-Parsa-Mirhaji, #James-Green, #Cognome, #IRB-regulations, #compliance, #data-privacy, #interoperability, #integration-frameworks, #multimodal-datasets, #patient-consent, #patient-empowerment, #Bio-repository, #PACS, #Notes, #EMR, #data-enclaves, #OHDSI-OMOP-datamarts, #audit-trails, #data-transparency, #data-quality, #ontology-management, #single-source-dataset, #healthcare-innovation</p>
4 total episodes available
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Frequently asked questions
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- What is Learning Healthcare System Podcast?
- How often does this podcast release new episodes?
This podcast updates bi-weekly.
- Where can I listen to this podcast?
This podcast is available on 7 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Yes, this podcast regularly features guests.
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