b'The power of AI is not just to make one prediction or inform one decision. More importantly, AI starts a process of continual learning, testing, and optimization.Machine learning can help an organization learn, evaluate, and optimize all the time, not just at an annual strategic planning session or a single board meeting. It elevates your organizations ability to respond proactively to events as they unfold and is the strategic equivalent of moving from analog to digital, from Blockbuster to Netflix.In this new, AI-enabled world, previous dashboards and spreadsheets become the brick-and-mortar equivalent: suitable for a boutique operation but incapable of competing with powerful new models.The Use Case: MatchingPatient Volume to Operations What do the aviation industry and U.S. health care have in common?Complexity. High fixed and high labor costs. Variable and unpredictable demand. Difficulty in adopting technology. However, in the use case described below, aviation has been well ahead of health care. In fact, the airlines use AI to predict the predictable, optimize operations, and balance cost against customer satisfaction. Aviation is an excellent analog for understanding the challenges many hospitals face today.The Aviation AnalogAirlines may sometimes seem stuck in old ways, but many have been on the leading edge of artificial intelligence and machine learning for years. The comparison to a major health system is useful. Consider that each airline operates in multiple (if not hundreds) of sites spread across the country. At each site, there are dozens of gates. Each gate has a specific capacity to handle airplanes. Each type of airplane requires specific pilots with specific training to fly it. Each requires a certain number of flight attendants, each with specific training. And each plane can carry a certain number of passengers who may be connecting through other sites around the country, all on a set schedule.Now, a line of thunderstorms comes through to disrupt operations at one or multiple airports in a particular region. The airline responds by making use of the tools of machine learning to determine, in real-time, what to do: how many flights to cancel vs. delay, where to re-route planes with the least disruption, andmost importantlyhow to balance the cost of cancellations or delays with the costs of customer dissatisfaction.Humans do not make these decisions with spreadsheets and gut feel. A suite of technologies enables airlines to automate decision-making in real-time. Similar technologies are now becoming essential to solving a parallel set of problems in health care.The Emergency DepartmentFor decades, patient volume and acuity in the nations ERs were predictable. Each year, volumes went up with demographic trends. There were seasonal flu patterns and variations and differences in case mix depending on the size and location, but volumes were predictable.In the last five years, this began to change. And, with the COVID pandemic, it transformed. Volume is no longer as predictable. Volatility is up. And it is not simply different strains of the COVID virus, or other contagious diseases, impacting different regions at various times. In the last three years, wholesale changes in society have made predicting volumes in the ED more complex than ever before.Work from home, changing travel patterns, misinformation, rising high-deductible health plans, millions of people changing jobs, and a range of psychological challengesall these factors mean that Americans are making different judgments about when and where to go for care. These dynamics dramatically impact the emergency room and by extension, the hospital. And these judgments change month to month and year to year. Together, we healThis volatility is the new normal.Together, we heal 3Together, we heal SCP HEALTHIINVESTING IN AI: PROVEN STRATEGIES TO FUEL TRANSFORMATION IN HOSPITAL OPERATIONS'