Description
Predict individual hospital admission rates using historical and real-time data
Benefits & ROI
0.5
Key considerations
Upside3
Linked Case Studies
Case Study
Mercy Hospital Fort Smith
Mercy Hospital Fort Smith improves patient flow and throughput in the ER to improves LWBS rates by over 30% using machine learning
Case Study
Johns Hopkins Hospital
Johns Hopkins Hospital improves patient monitoring and resource allocation in ER and critical care units in real-time using machine learning
Case Study
University College London Hospitals
UCLH plans to use machine learning to triage patients in A&E and better predict demand for the service
Case Study
MetroHealth
MetroHealth predicts patient flow to improve operational decision making using machine learning
Case Study
"The George Institute for Global Health, Oxford University"
Researchers at the George Institute for Global Health improve emergency room admittance predictive models using machine learning
Case Study
Natividad Medical Center
Natividad Medical Center reduces time to see doctor by 20% and left without being seen rates by 42% by optimising patient flow and resource allocation in ER
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Source: mckinsey.com · Editor: original