Going Vertical: A Smarter Approach to Emergency Department Crowding

Published: September 6, 2026

If you’ve ever visited a hospital emergency department, chances are you’ve experienced one of its most persistent challenges: waiting.

For hospitals, reducing those wait times can seem like a capacity problem. More patients require more beds, more staff or more space, right?

But what if one of the solutions is simply making better decisions about how the resources already available are used?

Joining me today is Agni Orfanoudaki, Oxford University, to share new research on a data-driven approach to determining which patients actually need a traditional emergency department bed and who can be treated in alternate locations. Ultimately, this reduced emergency room stays by 11 minutes, all without adding staff, beds or technology, and without compromising patient safety.

Interviewed this episode:

Agni Orfanoudaki

Oxford University

Agni Orfanoudaki is an Associate Professor of Operations Management at the Saïd Business School of Oxford University. She is also a Management Studies Fellow at Exeter College and leads the Data-Driven Decisions Lab (3DL), conducting theoretical and empirical research with machine learning, optimization, and stochastic processes with applications to healthcare and insurance. She was previously a visiting scholar at the Harvard Kennedy School as a Harvard Data Science Initiative Fellow. Prior to joining Oxford, Agni received a PhD in Operations Research from the Massachusetts Institute of Technology. She has collaborated with numerous institutions, including a major medical society, three international insurance companies, and more than eight healthcare systems in the US and Europe.