SingaporeHealthcare and life sciences2023 – ongoingNational Healthcare Group
PreSAGE bed-exit prediction at Tan Tock Seng Hospital
Tan Tock Seng Hospital reported a 34% lower falls rate during PreSAGE evaluation
Problem
Patients needed help before leaving their beds, while nurses faced heavy workloads. Falls prevention required timely intervention.
Approach
The hospital used PreSAGE to predict bed exits from thermal images using machine learning. Alerts gave staff a 15-second lead time before a patient stood. The system expanded to all single-bed and isolation rooms, covering more than 100 beds.
Results
Takeaway
The hospital reported evaluation results without specifying the evaluation duration or comparison group.
Categories
- Size
- Large enterprise
- Industry
- Healthcare and life sciences
- Business function
- Production and quality
- Innovation type
- Process innovation
- AI technology
- Computer visionPredictive analytics and ML
- Adoption
- Vendor solution
- Evidence
- Company's own claim
Sources
Source summaries
The hospital described PreSAGE's prediction of bed exits and subsequent expansion. Evaluation produced a reported 34% reduction in falls rate and 67% saving in manpower hours.
Full details are in the original sources.
