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Improving hospitalwide patient flow requires an appreciation of the hospital as an interconnected, interdependent system of care. Michael Thompson explores how Cedars-Sinai Medical Center used supervised machine learning to create predictive models for length of stay, emergency department (ED) arrivals, ED admissions, aggregate discharges, and total bed census and leveraged these models to reduce patient wait times and staff overtime and improve patient outcomes and patient and clinician satisfaction.
Learn more about the following topics:
• How to engage leaders up front with the goal of operationalizing analytics.
• What types of machine learning methods best support operationalizing analytics.
• How to operationalize machine learning-driven results to improve patient flow.