Evidence map›Paper›PMID 40011628›Full record

ArticleScientific reports2025

Flight testing of drone-delivered automated external defibrillators for simulated out-of-hospital cardiac arrest in suburban Thailand.

Winchana Srivilaithon, Nalinas Khunkhlai, Michael Currie

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Winchana SrivilaithonDepartment of Emergency Medicine, Faculty of Medicine, Thammasat University, 99/209 Phahon Yothin Road, Klong Luang District, Pathum Thani, 12120, Thailand. winchanas@gmail.com.
Nalinas KhunkhlaiThai Resuscitation Council, Bangkok, Thailand.
Michael CurrieFlingX Co., Ltd., Bangkok, Thailand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of automated external defibrillators (AEDs) in a timely manner is critical for improving survival rates in out-of-hospital cardiac arrest (OHCA) cases. However, in developing countries, logistical and infrastructural challenges often result in delays, particularly in suburban areas. This study evaluates the feasibility and safety of using drones to deliver AEDs in suburban OHCA scenarios. A series of ninety test flights were conducted using a DJI Matrice 600 drone (DJI, China) to deliver a Philips HeartStart AED (Philips, Netherlands) across varying payloads. Bystanders in simulated OHCA situations identified their location via mobile applications, enabling the drone operator to dispatch the drone beyond the pilot's line of sight. The results showed a 97.7% success rate in AED delivery, with a median flight distance of 4042 m and a median response time of 7 min and 39 s. Despite payload variations, the drone maintained adequate speed and landing accuracy, with a mean speed of 9.17 m per second and a median landing error of 122 centimeters. The findings suggest that drones have significant potential for improving emergency medical responses in suburban areas of developing countries. Integration into emergency services could address current delays, though further research is necessary to optimize performance under varying conditions.

Indexed as

AircraftCardiopulmonary ResuscitationDefibrillatorsOut-of-Hospital Cardiac ArrestEmergency Medical ServicesFemaleHumansMaleThailandAutomated external defibrillators (AED)Drone deliveryOut-of-hospital cardiac arrest (OHCA)Response timeSuburban areas

Identifiers

PMID40011628
PMCPMC11865582

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.