Evidence map›Paper›PMID 26918631›Full record

ArticlePloS one2016

A Smartphone App and Cloud-Based Consultation System for Burn Injury Emergency Care.

Lee A Wallis, Julian Fleming, Marie Hasselberg, Lucie Laflamme, Johan Lundin

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
7.4field-weighted citation impact, top 2% of its field
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

30 citing papers in PubMed, 71 citations in OpenAlex.

  1. Trial
  2. Artificial Intelligence and Algorithmic Bias in Burn Care: A Literature Review of Ethical Challenges.Journal of burn care & research : official publication of the American Burn Association · 2026
    Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Optimizing an Outpatient mHealth Intervention for Children with Burns: A Convergent Mixed-Methods Study.Journal of burn care & research : official publication of the American Burn Association · 2023
    Article
  8. Article
  9. Impact of the Early COVID-19 Pandemic on Burn Care: A Multi-National Study.Journal of burn care & research : official publication of the American Burn Association · 2023
    Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Article
  16. Article
  17. m-Health for Burn Injury Consultations in a Low-Resource Setting: An Acceptability Study Among Health Care Providers.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2020
    Article
  18. Article
  19. Article
  20. 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

5 authors at 3 institutions in 3 countries.

Lee A WallisDivision of Emergency Medicine, Stellenbosch University, Stellenbosch, South Africa.
Julian FlemingDivision of Emergency Medicine, Stellenbosch University, Stellenbosch, South Africa.
Marie HasselbergDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
Lucie LaflammeDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
Johan LundinDepartment of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
Karolinska Institutet · SEStellenbosch University · ZAInstitute for Molecular Medicine Finland · FI

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEach year more than 10 million people worldwide are burned severely enough to require medical attention, with clinical outcomes noticeably worse in resource poor settings. Expert clinical advice on acute injuries can play a determinant role and there is a need for novel approaches that allow for timely access to advice. We developed an interactive mobile phone application that enables transfer of both patient data and pictures of a wound from the point-of-care to a remote burns expert who, in turn, provides advice back. METHODS AND

resultsThe application is an integrated clinical decision support system that includes a mobile phone application and server software running in a cloud environment. The client application is installed on a smartphone and structured patient data and photographs can be captured in a protocol driven manner. The user can indicate the specific injured body surface(s) through a touchscreen interface and an integrated calculator estimates the total body surface area that the burn injury affects. Predefined standardised care advice including total fluid requirement is provided immediately by the software and the case data are relayed to a cloud server. A text message is automatically sent to a burn expert on call who then can access the cloud server with the smartphone app or a web browser, review the case and pictures, and respond with both structured and personalized advice to the health care professional at the point-of-care.

conclusionsIn this article, we present the design of the smartphone and the server application alongside the type of structured patient data collected together with the pictures taken at point-of-care. We report on how the application will be introduced at point-of-care and how its clinical impact will be evaluated prior to roll out. Challenges, strengths and limitations of the system are identified that may help materialising or hinder the expected outcome to provide a solution for remote consultation on burns that can be integrated into routine acute clinical care and thereby promote equity in injury emergency care, a growing public health burden.

Indexed as

Emergency Medical ServicesMobile ApplicationsRemote ConsultationSmartphoneBody Surface AreaBurnsCloud ComputingHumansPoint-of-Care SystemsText Messaging

Identifiers

PMID26918631
PMCPMC4769217
OpenAlexW2273943878

What OpenQuestion holds

Textmetadata
LicenceCC BY
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 OpenQuestion graph.