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SURF: identifying and allocating resources during Out-of-Hospital Cardiac Arrest
Gaurav Rao1, Salimur Choudhury2, Pawan Lingras1
1Department of Mathematics and Computing Science, Saint Mary's University, Halifax, CA, USA.
BMC Medical Informatics and Decision Making
|December 31, 2020
Summary
A new Preprocessed Integer Linear Programming algorithm rapidly matches users with automated external defibrillators (AEDs) during out-of-hospital cardiac arrests (OHCA). This faster matching can reduce resuscitation times for patients.
Area of Science:
- Emergency medical services
- Computer science applications in healthcare
- Public health technology
Background:
- Out-of-hospital cardiac arrest (OHCA) incidents require rapid emergency medical services (EMS) response.
- Responder Network Systems (RNS) alert nearby registered users via mobile devices to cardiac emergencies.
- Matching available users with automated external defibrillators (AEDs) is crucial for timely patient support.
Purpose of the Study:
- To develop an efficient algorithm for real-time matching of users with AEDs during cardiac emergencies.
- To improve upon existing matching methods for faster and more effective emergency response.
Main Methods:
- Comparison of Bipartite Matching and Integer Linear Programming (ILP) for user-AED matching.
- Introduction of a novel Preprocessed Integer Linear Programming (Preprocessed ILP) method for accelerated matching.
Main Results:
- Bipartite Matching averaged 1850 seconds processing time.
- Integer Linear Programming (ILP) averaged 32 seconds processing time.
- Preprocessed ILP achieved an average processing time of just 2 seconds, significantly outperforming previous methods.
Conclusions:
- The proposed Preprocessed ILP algorithm enables efficient, real-time user-AED matching during cardiac emergencies.
- Implementation of this system has the potential to significantly reduce patient resuscitation times.
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