Community first response for cardiac arrest: comparing phased dispatch policies through Monte Carlo simulation

preprint OA: closed CC-BY-4.0
📄 Open PDF Full text JSON View at publisher
AI-generated deep summary by claude@2026-07, 2026-07-04 · read from full text

This paper studied how different phased alert dispatch policies in an Advanced Community First Responder (CFR) system affect response times, CFR workload (number of alerts), redundant CFR arrivals, and patient survival probability. Using Monte Carlo simulation, the authors bootstrapped acceptance probabilities and response delays from 29,307 rows of historical GoodSAM alert data in New Zealand (2017–2020), modeled CFR locations as uniformly random within a 1 km radius, and assigned travel speeds based on distance-dependent regression of observed responder speeds. The results show that alerting policy substantially changes the expected number of alerts and redundant arrivals, and also influences the probability of patient survival, indicating trade-offs among the metrics. A key limitation is that the CFR spatial and travel-speed assumptions and reliance on historical acceptance/delay data constrain how directly the simulated outcomes reflect future operational settings. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

Read from the paper's body, not the abstract. Not a substitute for reading the paper. No clinical advice. How this works

Abstract

Background Advanced Community First Responder (CFR) systems send so-called phased alerts: notifications with built-in time delays. The policy that defines these delays affects response times, CFR workload and the number of redundant CFR arrivals. Methods We compare policies by Monte Carlo Simulation, estimating the three metrics above. We bootstrap acceptance probabilities and response delays from 29,307 rows of historical data covering all GoodSAM alerts in New Zealand between 1-12-2017 and 30-11-2020. We simulate distances between the patient and CFRs by assuming that CFRs are located uniformly at random in a 1 km circle around the patient, for different CFR densities. Our simulated CFRs travel with a distance-dependent speed that was estimated by linear regression on observed speeds among those responders in the abovementioned data set that eventually reached the patient. Results The alerting policy has a large impact on the expected number of alerts sent, the redundant arrivals and the probability of patient survival. CFR app managers can use our results to identify a policy that displays a desirable trade-off between these performance measures.
Full text 3,061 characters · extracted from oa-doi-fallback · 3 sections · click to expand

Abstract

Background Advanced Community First Responder (CFR) systems send so-called phased alerts: notifications with built-in time delays. The policy that defines these delays affects response times, CFR workload and the number of redundant CFR arrivals.

Methods

We compare policies by Monte Carlo Simulation, estimating the three metrics above. We bootstrap acceptance probabilities and response delays from 29,307 rows of historical data covering all GoodSAM alerts in New Zealand between 1-12-2017 and 30-11-2020. We simulate distances between the patient and CFRs by assuming that CFRs are located uniformly at random in a 1 km circle around the patient, for different CFR densities. Our simulated CFRs travel with a distance-dependent speed that was estimated by linear regression on observed speeds among those responders in the abovementioned data set that eventually reached the patient.

Results

The alerting policy has a large impact on the expected number of alerts sent, the redundant arrivals and the probability of patient survival. CFR app managers can use our results to identify a policy that displays a desirable trade-off between these performance measures. Competing Interest Statement The authors have declared no competing interest. Funding Statement Pieter L. van den Berg reports financial support was provided by Nederlandse Organisatie voor Wetenschappelijk Onderzoek Utrecht and by TKI Dinalog. Caroline J. Jagtenberg reports financial support was provided by TKI Dinalog. Shane G. Henderson reports financial support was provided by National Science Foundation. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Not applicable I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Not Applicable Data Availability Data subject to third party restrictions. Data available from the authors with the permission of St John New Zealand

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00
unpaywall
last seen: 2026-05-26T02:00:01.498150+00:00
License: CC-BY-4.0