Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper
Dennis L Shung1, John K Lin2, Loren Laine1,3
1Section of Digestive Diseases, Department of Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
Introduction:
We estimate the economic impact of applying risk assessment tools to identify very low-risk patients with upper gastrointestinal bleeding who can be safely discharged from the emergency department using a cost minimization analysis.
Methods:
We compare triage strategies (Glasgow-Blatchford score = 0/0-1 or validated machine learning model) with usual care using a Markov chain model from a US health care payer perspective.
Results:
Over 5 years, the Glasgow-Blatchford score triage strategy produced national cumulative savings over usual care of more than $2.7 billion and the machine learning strategy of more than $3.4 billion.
Discussion:
Implementing risk assessment models for upper gastrointestinal bleeding reduces costs, thereby increasing value.
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