A revolution is imminent in American healthcare, and “the revolution will not be televised” for passive observation. Value-based care transformation, like any other important movement, requires the active participation of all leaders on the frontlines. However, for these leaders to make the right decisions, they need to embrace innovation in order to realize the fullest potential of generative AI and predictive analytics. Through the reengineering of care delivery, we can achieve a more personalized, proactive, and efficient outcomes-based model that can ultimately transform population health.
As we navigate this transformative journey, data will play a pivotal role in reshaping the landscape of care delivery. And no one knows this better than Nassib Chamoun, Founder President & CEO of Health Data Analytics Institute (HDAI), our guest this week on Race to Value. In this episode, you will hear from a leader and primary inventor of a broad-based population health data analytics platform, enabling healthcare providers to make informed decisions based on real-time information. Tune in to an informative conversation covering such topics as data aggregation, predictive analytics, digital twinning, network management, generative AI in clinical care, and future advancements in technology-enabled value-based care.
01:30 The Imminent “Big Data” Revolution in Value-Based Care
02:00 Introduction to Nassib Chamoun of Health Data Analytics Institute
03:00 As a teenager living in Beirut, Nassib experienced the horror of a civil war.
04:00 The inventor of Bispectral Index monitoring – a technology standard in operating rooms around the world.
05:00 Nassib discusses the pivotal moments in his life that shaped a passion for data analytics in healthcare.
07:00 80% of health information in EHRs is unstructured and entirely unusable unless converted to discrete data.
07:45 CMS provided HDAI a highly coveted Innovator’s License that allows the company access to data on 100 million Medicare beneficiaries.
09:00 How Big Data drives powerful AI algorithms and predictive models in healthcare.
10:00 “If you can’t measure something, you can’t improve it.”
11:00 Understanding the intersection between cost, outcomes, and utilization.
11:30 Making data actionable in order to effectuate change in care delivery.
11:45 Data overload can actually lead to clinical inefficiencies if it isn’t curated appropriately.
12:30 The artful curation of data to drive operational improvements at point-of-care.
14:00 The limitations of claims data in making timely clinical decisions and treatment interventions.
15:00 Interpretation of unstructured EHR data to extract potential new conditions and HCC coding opportunities.
16:00 The importance of clinical judgement in augmenting AI-based recommendations in value-based care.
17:00 Combining behavioral, psychosocial, and biometric data with the existing sciences of epidemiology and clinical medicine.
18:00 Generalized clinical use cases of AI at the point-of-care to improve costs, outcomes, and utilization.
19:00 “To be successful in value-based care, you must operationalize two separate goals: Prevention and Avoidance of Complications.”
20:30 “The goal of AI is to very simply do what a clinician does, but do it repeatedly and do it continuously for every patient in their cohort.”
21:00 How staffing limitations and an aging populations necessitates a more optimal use of technology in VBC.
22:00 In 2032, U.S. healthcare spending will reach $8 trillion (ahead of the economy of Japan) making it the third largest economy in the world!
22:45 Leveraging predictive models to drive more effective care coordination and interdisciplinary team-based care.
24:30 Patient engagement as one of the more challenging aspects of value-based care.
26:30 The integration of predictive analytics and digital twinning for individualized patient care.
28:45 Using multiple predictors to serve every comp...