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AI-Based Triage Decision Support: Multisite Economic Evaluation in the United States
Scott Levin1, Ben Steinhart1, Rohit Sangal2
1Danaher Diagnostics, Danaher (United States), 2200 Pennsylvania Avenue NW, Washington, DC, 20037, United States, 1 301-404-7742.
Journal of Medical Internet Research
|June 3, 2026
Summary
Artificial intelligence (AI) in emergency departments (EDs) can increase revenue and efficiency. However, the financial impact varies significantly based on the cost-modeling approach used, highlighting the need for adaptable economic frameworks.
Area of Science:
- Health Economics
- Artificial Intelligence in Healthcare
- Emergency Medicine
Background:
- Rising emergency department (ED) visits strain healthcare resources, leading to crowding.
- ED crowding negatively impacts patient care, staff well-being, and hospital finances.
- Digital tools, including AI, show potential for improving ED efficiency, but economic evaluation frameworks are lacking.
Purpose of the Study:
- To develop and apply a generalizable economic model for assessing the financial impact of AI-driven efficiency in emergency care.
- To compare hospital management and public policy cost-modeling approaches for AI tools in EDs.
Main Methods:
- An economic model was applied to data from 3 EDs before and after implementing an AI triage system.
- Operational and financial data, including revenue, costs, and operating margins, were analyzed.
- Two cost-modeling frameworks (hospital management and public policy) were used, with sensitivity and break-even analyses.
Main Results:
- AI implementation increased revenue by $15.4 million and ED visit volume by 9.6%.
- The hospital management perspective showed a $12.6 million operating margin gain, while the public policy perspective showed only $0.9 million.
- Break-even costs for AI tools differed drastically: $66.02/visit (hospital management) vs. $4.69/visit (public policy) for a 5% efficiency gain.
Conclusions:
- The financial impact of AI-driven efficiency tools in EDs is highly dependent on the cost-modeling framework.
- Public policy cost-modeling approaches may underestimate the value of AI tools from a hospital management perspective.
- A generalized, adaptable economic model is crucial for evaluating AI in EDs and facilitating adoption.
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