Many AI projects in hospitals never make it past proof of concept. The technology performs in a demo and then stalls before it reaches a care team.
Success depends on more than the model’s sophistication. It rests on the foundation underneath: connected data, infrastructure that scales, oversight someone owns and workflows that match how frontline teams actually deliver care.
In particular, one element receives less attention than it deserves: patient monitors and other bedside devices can capture continuous physiological changes that periodic EHR documentation may miss. Using this data in real time requires interoperability, standardization, storage and clinical context.
Adam Alkhato of Stanford Health Care and Bobby Greer of The Hospital for Sick Children will discuss what organizations should address before deploying AI built on bedside data: which use cases to pursue, how to judge readiness, who holds oversight and what changes when a model goes live.
Learnings include:
- How to identify a care or operating problem worth addressing with AI
- How continuous physiological data differs from EHR documentation and why the two are more useful when connected
- What infrastructure and organizational capability it takes to capture, standardize and use bedside data in real time
- How governance, validation and clinician input can help AI fit everyday workflows