AI LITERACY
BMF Crew Patient Photo: Lyn Burks
AI literacy is the work most programs are doing right now, and it is the right starting point. As Atwood has explained, “the issue is a lot of people don’t know what it’s capable of or where the capabilities currently exist.” AI is much broader than the chat assistants most people imagine. It includes a range of methods, such as large language models, computer vision, and predictive analytics, with use cases spanning from ambient AI in clinical documentation to dispatch decision support and demand forecasting.
Once teams see the full scope of the field, their thinking shifts. Atwood has seen this change firsthand: “People went from really not knowing anything besides ‘ChatGPT helps me with my email’ to exploring all kinds of use cases.” She’s been successful in treating AI literacy as another educational opportunity for staff at all levels.
Before choosing any tools, the team needs a shared understanding of what AI is and isn’t, and the leadership team must agree on the direction. Kolbet said, “The challenge right now is helping everybody see that vision.”
With literacy established and leadership aligned, the next step is to focus on a problem AI is well-equipped to solve.
Photo: Dianne Bond, Airbus LEAD WITH THE PROBLEM
BMF Patient Load Photo: Lyn Burks
Before choosing any AI tool, it’s important to clarify the problem we want to solve. Whether it’s improving dispatch reliability, monitoring clinical guideline adherence, or identifying new hotspots in patient demand, the best use cases come from clearly defined problems. As Atwood said, “It’s always helpful to define a problem to solve that most people can rally around.” A shared problem creates momentum.
Instead of beginning with the technology and asking where the team fits, start with the work the team already does and determine where AI fits within it.
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