How Data Analytics and AI Can Reduce Clinician Burnout in Healthcare Systems with Lori Runion Resultant TRANSCRIPT
Release Date: 03/30/2026
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Next-Generation Imaging Catheter for Enhanced Coronary Interventions with Tom Looby Conavi
Empowered Patient Podcast
Tom Looby, CEO of Conavi, is focused on developing hybrid intravascular imaging technology that combines two established modalities into a single imaging catheter to provide a comprehensive view of the coronary arteries. This eliminates blind spots when using either intravascular ultrasound (IVUS) or optical coherence tomography (OCT) alone and is driving a shift away from relying solely on traditional angiography toward the use of advanced intravascular imaging to guide coronary procedures. Using AI to analyze dual-stream co-registered data allows interventional cardiologists to more...
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Empowered Patient Podcast
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Providing Current Information and Global Support for Parents of Children with Autism with Theresa Lyons Navigating AWEtism
Empowered Patient Podcast
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How Data Analytics and AI Can Reduce Clinician Burnout in Healthcare Systems with Lori Runion Resultant TRANSCRIPT
Empowered Patient Podcast
Lori Runion, a director at Resultant, identifies inadequate scheduling and related staffing unpredictability as a central cause of clinician burnout. Healthcare organizations traditionally rely on historical averages for scheduling, often resulting in a mismatch between patient demand and clinician capacity. Breaking down data silos and using analytics and AI to create predictive staffing models can help forecast demand, anticipate seasonal spikes, and enable proactive staffing to reduce clinician burnout. Lori explains, " From my perspective, burnout is driven at the operational level. To say...
info_outline
How Data Analytics and AI Can Reduce Clinician Burnout in Healthcare Systems with Lori Runion Resultant
Empowered Patient Podcast
Lori Runion, a director at Resultant, identifies inadequate scheduling and related staffing unpredictability as a central cause of clinician burnout. Healthcare organizations traditionally rely on historical averages for scheduling, often resulting in a mismatch between patient demand and clinician capacity. Breaking down data silos and using analytics and AI to create predictive staffing models can help forecast demand, anticipate seasonal spikes, and enable proactive staffing to reduce clinician burnout. Lori explains, " From my perspective, burnout is driven at the operational level. To say...
info_outline
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Empowered Patient Podcast
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info_outline
Temporary Medical Staffing Addresses Critical Provider Shortages with Bill Heller CHG Healthcare
Empowered Patient Podcast
Bill Heller, Chief Operating Officer at CHG Healthcare, is focused on the significant and growing demand for physicians across numerous specialties and on providing a flexible solution for healthcare facilities to maintain services with temporary physician staffing. Rural healthcare facilities are especially dependent on temporary staffing to overcome challenges in attracting and keeping permanent medical professionals. Physicians at all career stages are drawn to locum tenens work, and it has evolved from a niche practice to a mainstream strategy for healthcare facilities and...
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Empowered Patient Podcast
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info_outline
Building Foundational AI Infrastructure for Holistic View of Biology with Jean-Philippe Vert Bioptimus
Empowered Patient Podcast
Jean-Philippe Vert, the Co-Founder and CEO of Bioptimus, is building a foundational AI model for biology to solve the problem of siloed biomedical research. Key goals are to bridge the translational gaps between drug discovery and development, and between clinical research and real-world patient outcomes, and to redesign clinical trials for greater efficiency and improved results. Creating digital twins of patients is a way to simulate treatment outcomes and create synthetic control arms for clinical trials, ultimately lowering the risk and cost of drug development and enabling the creation...
info_outlineLori Runion, a director at Resultant, identifies inadequate scheduling and related staffing unpredictability as a central cause of clinician burnout. Healthcare organizations traditionally rely on historical averages for scheduling, often resulting in a mismatch between patient demand and clinician capacity. Breaking down data silos and using analytics and AI to create predictive staffing models can help forecast demand, anticipate seasonal spikes, and enable proactive staffing to reduce clinician burnout.
Lori explains, " From my perspective, burnout is driven at the operational level. To say it most simply, I think that burnout is driven by unpredictability, specifically, what I want to talk a little bit about, predictive staffing. And so, when we think about staffing, the unpredictability and misalignment between patient demand and staffing capacity are really what's driving it. So I don't think it's a lack of resilience. I don't think it's necessarily that there are gaps in care, but there are constant coverage gaps and volatility in the workload. And so I think it's ultimately driven by that mismatch when patient demand and clinician capacity are misaligned. I think that healthcare is traditionally staffed based on historical averages rather than dynamic demand or patterns, and that's what creates the unpredictable shifts and last-minute schedule changes that lead to overextension and exhaustion, which drive burnout."
"So, for example, you think about your EHR, which includes your demand, your patient medical record, and you have a scheduling system that shows available capacity, and you may have claims data that shows utilization patterns or other things. So when they are only looking at one system, they have some blind spots. And so I think that if they're looking at connected systems and pulling all that data together to identify patterns and really see the full picture, that's where they can align patient demand with staffing capacity."
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