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With the recent emergence of ChatGPT and other AI-powered tools, the focus on using these tools in healthcare has increased. That’s not a problem if you’re a data scientist, but where do you start if you’re unfamiliar with how artificial intelligence and machine learning might impact your work? This post aims to provide a very brief, high-level overview of AI, with examples of how they might help in healthcare.
We know that hospitals rely on operating rooms to deliver the majority of their revenues and margin and that OR time sitting idle represents a significant opportunity cost. As such, hospitals use tools like Copient Health to optimize the use of valuable OR time. Do ASCs share a similar dynamic? Might an ASC benefit from a tool to optimize the use of OR time?
For most hospitals, operating rooms contribute more to funding the mission than any other service. They typically represent the majority of a hospital's revenue and its biggest source of profit. As such, how can you make the best use of this scarce resource?
With the financial pressures most hospitals have recently experienced, you’ve likely considered different approaches to increase case volume and operational efficiency in your OR. In that consideration, you’ve probably encountered a variety of claims related to either cost savings or increased revenue from internal project teams, consultants, and software vendors. These success metrics can be misleading in different ways:
It's a recurring theme we hear at nearly every hospital. Surgeons distrust - or actively refute the accuracy of - reported utilization statistics. Do they have good reason for this doubt? Regardless, hospitals continue to rely heavily on utilization statistics when making decisions about allocating block time. Is this a sensible thing for hospitals to do? What are the implications?
