Researchers at the University of Massachusetts Amherst have developed a machine learning-driven approach to optimize surgeon scheduling, addressing systemic inefficiencies in hospital operations. Published in the Journal of the American Medical Informatics Association, the study analyzed nearly 86,500 surgical records from Baystate Medical Center in Springfield to identify operational bottlenecks that drive costs and contribute to provider burnout. With the American Association of Medical Colleges projecting a national surgeon shortfall of 10,000 to nearly 20,000 by 2036, healthcare systems face mounting pressure to maximize existing surgical workforces. Traditional scheduling models rely on fixed time blocks, a method ill-suited to the highly variable nature of surgical procedures. This rigidity frequently leaves operating rooms idle or overextended, failing to account for critical transition periods between cases. To resolve this, the research team, led by industrial engineering assistant professor Muge Capan and doctoral candidate Jonathan Akhagbosu, shifted focus from operating room logistics to the surgeon workflow. The team quantified the inter-procedural interval, termed gap time, and deployed machine learning models to predict its duration based on specific clinical and operational variables. Analysis revealed that gap duration is significantly influenced by case sequencing, including whether adjacent procedures are emergencies, the anatomical focus such as thoracic or cardiac operations, and the physical and cognitive load imposed on the surgeon. To standardize workload assessment, the researchers introduced a new metric called surgical case demand, categorizing procedures into three tiers. Tier 1 encompasses low-severity, elective interventions. Tier 2 includes moderately demanding operations like joint replacements. Tier 3 covers high-acuity, off-hour procedures including emergency neurosurgery. The data indicated that ophthalmic and orthopedic cases consistently resulted in shorter transition intervals. The practical application centers on identifying collectible time, or scheduling windows substantial enough to absorb additional procedures without compromising care quality. By accurately forecasting transition durations, hospitals can dynamically adjust operating room utilization, reducing wasted capacity and mitigating surgeon fatigue. This predictive framework aligns with industrial engineering principles focused on minimizing variation and eliminating operational waste. The study underscores a growing intersection between computational methods and clinical logistics. As healthcare systems confront resource constraints and staffing deficits, algorithmic scheduling tools offer a scalable pathway to enhance throughput, lower procedural costs, and improve patient outcomes. Integrating engineering methodologies into hospital administration can transform unpredictable surgical workflows into optimized, data-driven operations.