Achieving Value by Risk Stratification With Machine Learning Model or Clinical Risk Score in Acute Upper Gastrointestinal Bleeding: A Cost Minimization Analysis

Am J Gastroenterol. 2024 Feb 1;119(2):371-373. doi: 10.14309/ajg.0000000000002520. Epub 2023 Nov 23.


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.

MeSH terms

  • Acute Disease
  • Costs and Cost Analysis
  • Gastrointestinal Hemorrhage* / diagnosis
  • Gastrointestinal Hemorrhage* / therapy
  • Humans
  • Machine Learning*
  • Risk Assessment
  • Risk Factors
  • Severity of Illness Index