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Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis
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Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis
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Journal Article
Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis
2024
Overview
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.
Publisher
Wolters Kluwer,Wolters Kluwer Health Medical Research, Lippincott Williams & Wilkins
