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Predictive Dispatch of Volunteer First Responders: Algorithm Development and Validation.

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Summary
This summary is machine-generated.

Predicting volunteer first responder (VFR) behavior improves emergency dispatch. Models incorporating event data, demographics, and past experience accurately forecast VFR response likelihood, optimizing emergency medical services.

Keywords:
algorithmdecision-makingdispatchdispatch algorithmsdispatch decisionsdispatch predictionemergencyemergency responsefirst respondersmHealth interventionmedical emergencymobile healthmobile phonemobile phone appsrespondersmartphonesmartphone appsmartphone-based appsvolunteer

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Area of Science:

  • Emergency medical services
  • Public health technology
  • Predictive analytics in healthcare

Background:

  • Smartphone apps increasingly dispatch volunteer first responders (VFRs) for faster medical aid.
  • VFR response rates are uncertain (17%-47%), leading to suboptimal dispatch based solely on estimated time of arrival (ETA).
  • Current algorithms may waste resources on VFRs unlikely to respond, delaying critical aid.

Purpose of the Study:

  • To enhance emergency medical services dispatch by predicting VFR response behavior.
  • To develop a novel approach for forecasting whether a VFR will respond to or ignore an alert.
  • To improve decision-making for both human dispatchers and autonomous systems.

Main Methods:

  • Developed and compared four analytical models to predict VFR response.
  • Models utilized emergency event characteristics, VFR demographics, and prior experience.
  • Tested models on 12-month data from 112 VFRs responding to 188 opioid overdose emergencies.

Main Results:

  • Models using event data, demographics, and experience achieved high accuracy in predicting ignored alerts (up to 79.1%).
  • Model 4, including a 'frequent responder' indicator, showed slightly better overall accuracy (75.4%).
  • A model excluding demographics accurately predicted ignored alerts (84.2%) but poorly predicted responded alerts (46.5%). The J48 decision tree algorithm performed best.

Conclusions:

  • Predicting VFR response probability allows for optimized candidate selection beyond just ETA.
  • Integrating response probability into dispatch algorithms represents a new generation of emergency response.
  • Findings aid VFR network administrators in improving response times and saving lives through predictive analytics.