197. Optimizing Healthcare Resources through AI-Enhanced Diabetic Foot Screening: A Long-Term Cost-Effectiveness Analysis
Invited abstract in session MC-1: Simulation 1, stream Sessions.
Monday, 11:00-12:30Room: NTNU, Realfagbygget R5
Authors (first author is the speaker)
| 1. | YAN SUN
|
| Health Services and Outcomes Research, National Healthcare Group | |
| 2. | Gary Ang
|
| National Healthcare Group | |
| 3. | Lixia Ge
|
| National Healthcare Group | |
| 4. | Zhiwen Lo
|
| Woodlands Hospital | |
| 5. | huiling Liew
|
| Tan Tock Seng Hospital | |
| 6. | Donna Tan
|
| National Healthcare Group Polyclinics | |
| 7. | Daniel Chew
|
| Tan Tock Seng Hospital | |
| 8. | John Abisheganaden
|
| National Healthcare Group |
Abstract
Background: Diabetic foot ulcers (DFUs) are a serious complication of diabetes mellitus requiring substantial healthcare resources. While regular foot screenings prevent lower extremity amputations (LEAs), conventional annual screening approaches are resource-intensive. This study evaluates how AI can help optimize screening resource allocation while maintaining care quality.
Method: We developed a Markov state-transition model simulating disease progression across five health states: diabetes, DFU, minor & major LEA, and death. The model leverages national disease registry data for transition probabilities and costs. We compared AI-enhanced screening approach against routine annual screening for 500,000 low-risk diabetic patients over 40 years. The AI model recommends screening interval tailored to individual risk profile. Effectiveness was measured in quality-adjusted life years (QALYs). Monte Carlo micro-simulation was performed for probability sensitivity analysis (PSA).
Results: The AI-enhanced strategy demonstrated significant resource optimization, eliminating 6.8 million unnecessary screenings and saving healthcare costs by S$657.5 million over 40 years with minimal QLAY loss (ICER: S$174,572/QLAY, SD:13,296), proving its cost-effectiveness.
Conclusion: AI-enhanced screening AI-enhanced DFU screening optimizes healthcare resources through personalized risk assessment, significantly reducing unnecessary screenings while maintaining clinical outcomes.
Keywords
- Cost effectiveness and health economics
- Artificial Intelligence
- Screening and prevention
Status: accepted
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