Learning to Dispatch Volunteers to Out-of-Hospital Cardiac Arrests. van den Berg , P. L., Fourmentraux, O., Henderson, S. G., Jagtenberg, C. J., & Li, H. Submitted, 2024.
abstract   bibtex   
Survival for out-of-hospital cardiac arrest can be significantly improved through volunteer efforts. To shorten the time to good-quality cardiopulmonary resuscitation, some emergency call centers use mobile phone technology to rapidly locate and alert nearby trained volunteers. Some such community first responder systems use phased alerts: notifying increasingly many volunteers with built-in time delays. The policy that defines the phasing of alerts affects both response times, which have a direct relation to survival, and the burden on volunteers. We aim to optimize this policy, which involves trading off these two metrics. The policy may depend on real-time information: where the volunteers are observed in relation to the patient and how long triage took. A direct approach using dynamic programming yields some insights, but is too slow for real-time use. Our contribution lies in recasting this problem as a multi-class classification problem and solving it using empirical data from Auckland, New Zealand's community first response system. This case study shows that phasing the alerts based on real-time information provides important improvements relative to a competitive baseline that is indicative of current practice.
@article{beretal24b,
	abstract = {Survival for out-of-hospital cardiac arrest can be significantly improved through volunteer efforts. To shorten the time to good-quality cardiopulmonary resuscitation, some emergency call centers use mobile phone technology to rapidly locate and alert nearby trained volunteers. Some such community first responder systems use phased alerts: notifying increasingly many volunteers with built-in time delays. The policy that defines the phasing of alerts affects both response times, which have a direct relation to survival, and the burden on volunteers. 

We aim to optimize this policy, which involves trading off these two metrics. The policy may depend on real-time information: where the volunteers are observed in relation to the patient and how long triage took. A direct approach using dynamic programming yields some insights, but is too slow for real-time use. Our contribution lies in recasting this problem as a multi-class classification problem and solving it using empirical data from Auckland, New Zealand's community first response system. This case study shows that phasing the alerts based on real-time information provides important improvements relative to a competitive baseline that is indicative of current practice.},
	author = {Pieter L. {van den Berg} and Oc{\'e}ane Fourmentraux and Shane G. Henderson and Caroline J. Jagtenberg and Hemeng Li},
	date-added = {2024-01-29 12:10:51 +1300},
	date-modified = {2025-01-03 09:14:21 -0500},
	journal = {Submitted},
	title = {Learning to Dispatch Volunteers to Out-of-Hospital Cardiac Arrests.},
	year = {2024},
	bdsk-url-1 = {https://doi.org/10.1007/s11134-022-09752-z}}

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