RECORDED WEBINAR: From Prediction to Practice: Rethinking Chronic Disease Management in Value-Based Care

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 Download Slides As healthcare organizations take on greater accountability for outcomes and cost, managing chronic disease requires more than retrospective reporting. This session examines how earlier risk identification, sustained patient engagement, and coordinated care delivery can work together to improve outcomes and reduce avoidable utilization—particularly in high-burden chronic conditions. Speakers: Reynaldo Villar, Digital Architect…

RECORDED WEBINAR: AI Pitfalls That Value-Based Care Providers Need to Avoid

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 Download Slides Join us to learn how to harness AI for value-based outcomes—without falling into dangerous traps! As AI rapidly enters healthcare, value-based care (VBC) organizations face unique risks when adopting these technologies. This webinar highlights the most common AI pitfalls—including privacy violations, inaccurate outputs, biased or incomplete data, workflow disruption, and low clinician…

RECORDED WEBINAR: AI Triage in Action: Using Predictive Intelligence to Personalize VBC Pathways at Scale

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 Download Slides (will be available soon) Value-based care providers face mounting pressure to deliver better outcomes while lowering costs. But with limited time, staffing and resources, how can providers identify which patients need the most attention—and what interventions will make the greatest impact? This webinar explores how VBC providers can combine AI-driven predictive analytics…

RECORDED WEBINAR: Transforming Care Management with AI – Risk Stratification & Predictive Modeling for Better Health Outcomes

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 Download Slides Exploring how AI-driven analytics can transform care management through focus on Population Health Risk Stratification and Predictive Modeling. We will discuss how advanced data science techniques using Electronic Health Records (EHR) and claims data provides us an opportunity to perform risk stratification to help identify high-risk populations, predict adverse health events, and…

How Can EHRs Support the Shift to Value-Based Care and Reduce Clinician Burnout?

Garrett Schmitt,

A webinar sponsored by Elation Health highlighted some of the challenges primary care practices face and how a more thoughtful, nuanced approach to health tech could transform healthcare practices. MedCity News Editor-in-Chief Arundhati Parmar moderated a conversation with Elation Health executives Dr. Sara Pastoor, head of primary care advancement, and Meaghan Sullivan, senior sales engineer….

The Vibe at ViVE: Health Tech Leaders Share Insights from AI to Value-based Care

Garrett Schmitt,

Executives from TailorCare, Altera Digital Health, and WiPro shared insights at the ViVE 2025 conference in Nashville. John Petito, TailorCare Chief Strategy Officer, talked about what sets the company apart, particularly its multi-pronged approach to specialty value-based care. TailorCare is focused on using PT to improve outcomes for patients with joint, back, and muscle conditions,…

Generative AI IDs social determinants in EHR notes: Mass General Brigham

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Generative artificial intelligence can identify social determinants of health within EHR notes, accurately and without bias, determining which patients might need extra support, according to Somerville, Mass.-based Mass General Brigham. The health system’s researchers fine-tuned large language models to automatically extract information about social determinants — factors such as housing, employment and family status that…

How NorthShore Uses AI, NLP to Tackle SDOH in the Emergency Department

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Identifying and tackling social determinants of health (SDOH) are crucial aspects of any health equity strategy. But establishing how best to capture patients’ SDOH needs and develop interventions in the clinical setting remains a significant challenge for many healthcare organizations. The United States Department of Health and Human Services (HHS) defines SDOH as “the conditions in the…

Using Social Determinants of Health Data in Machine Learning Reduced Bias

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Published in Circulation: Heart Failure, a recent study conducted by Northwestern Medicine researchers found that adding social determinants of health (SDOH) data into machine learning models led to less bias when predicting outcomes for heart failure patients. Healthcare bias can lead to detrimental outcomes for patients. For example, in April, a study published in PLOS digital health found that digital health…