Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis.
- Resource Type
- Article
- Authors
- Shung, Dennis L.; Lin, John K.; Laine, Loren
- Source
- American Journal of Gastroenterology (Lippincott Williams & Wilkins). Feb2024, Vol. 119 Issue 2, p371-373. 3p.
- Subject
- *MACHINE learning
*GASTROINTESTINAL hemorrhage
*DISEASE risk factors
*COST analysis
*ECONOMIC impact
- Language
- ISSN
- 0002-9270
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. [ABSTRACT FROM AUTHOR]