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Updated: Jun 23, 2025
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Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
Published on: January 15, 2017
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Translational artificial intelligence-led optimization and realization of estimated discharge with a supportive
Brandon Stretton1,2,3,4, Andrew E C Booth2,4, Shrirajh Satheakeerthy2,4
1Lyell McEwin Hospital, Elizabeth Vale, SA, 5112, Australia.
Internal and Emergency Medicine
|June 22, 2024
Summary
Artificial intelligence (AI) enhanced weekend discharge planning increased patient discharges on weekends. This AI-driven approach proved cost-effective, improving hospital efficiency and patient flow.
Area of Science:
- Healthcare Management
- Artificial Intelligence in Medicine
- Hospital Operations
Background:
- Weekend discharges are less frequent than weekday discharges, leading to hospital congestion.
- Predictive artificial intelligence (AI) algorithms can identify patients nearing discharge from ward notes.
Purpose of the Study:
- To implement and evaluate an AI algorithm combined with a multidisciplinary discharge facilitation team for weekend discharges.
- To assess the impact of this intervention on the rate and number of weekend discharges and its cost-effectiveness.
Main Methods:
- An AI algorithm predicting discharge readiness was integrated with a weekend discharge facilitation team in a tertiary hospital.
- A historical cohort from the previous year served as the control group for comparison.
- 3990 patients were included in the study, comparing intervention and control periods.
Main Results:
- The intervention group showed a significant increase in weekend discharges (median 18% vs. 14%, P=0.031).
- A higher absolute number of weekend discharges occurred during the intervention period (P=0.025).
- Economic analyses indicated the approach is cost-effective.
Conclusions:
- The AI-assisted discharge facilitation significantly increased weekend discharges.
- This intervention is a cost-effective strategy to improve hospital efficiency.
- Further research is needed to confirm generalizability across different healthcare settings.
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