Description
Optimise staffing and labour resource allocation to reduce healthcare bottlenecks
Benefits & ROI
0.7
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
Gold Coast Health
Gold Coast Health saves $3m per annum in operational costs by forecasting patient arrival rates at emergency care using machine learning
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Source: mckinsey.com · Editor: original