Stanford Health Care’s Data-Driven Strategy to Improve Capacity

Finding cost-effective ways to increase capacity is a perennial need for high-volume healthcare facilities nationwide.  This case study provides a unique example of how an academic hospital can leverage data and analytics to increase capacity without the costly and time-consuming process of adding bricks and mortar. Stanford Health Care used data and predictive analytics to optimize processes and solve its capacity issues.  As a result of the process changes, the efficiency and capacity of the emergency department and inpatient beds has improved, and the number of canceled cases has been significantly reduced. This session will address three scenarios that include a daily dashboard, a 24/48-hour discharge prediction, and an annual patient flow model that has helped the organization develop operational countermeasures to improve capacity.

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