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 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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