Evidence map›Paper›PMID 24155791›Full record

ArticleApplied clinical informatics2013

Development and validation of a portable platform for deploying decision-support algorithms in prehospital settings.

A T Reisner, M Y Khitrov, L Chen, A Blood, K Wilkins, W Doyle, S Wilcox, T Denison, J Reifman

Open access · bronzeAbstract readValidation Study
In one paragraph

Article in Applied clinical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. Article
  5. A Machine Learning-Based Model to Predict Acute Traumatic Coagulopathy in Trauma Patients Upon Emergency Hospitalization.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    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

9 authors at 2 institutions in 1 country.

A T Reisner
M Y Khitrov
L Chen
A Blood
K Wilkins
W Doyle
S Wilcox
T Denison
J Reifman
Telemedicine & Advanced Technology Research Center · USMassachusetts General Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAdvanced decision-support capabilities for prehospital trauma care may prove effective at improving patient care. Such functionality would be possible if an analysis platform were connected to a transport vital-signs monitor. In practice, there are technical challenges to implementing such a system. Not only must each individual component be reliable, but, in addition, the connectivity between components must be reliable.

objectiveWe describe the development, validation, and deployment of the Automated Processing of Physiologic Registry for Assessment of Injury Severity (APPRAISE) platform, intended to serve as a test bed to help evaluate the performance of decision-support algorithms in a prehospital environment.

methodsWe describe the hardware selected and the software implemented, and the procedures used for laboratory and field testing.

resultsThe APPRAISE platform met performance goals in both laboratory testing (using a vital-sign data simulator) and initial field testing. After its field testing, the platform has been in use on Boston MedFlight air ambulances since February of 2010.

conclusionThese experiences may prove informative to other technology developers and to healthcare stakeholders seeking to invest in connected electronic systems for prehospital as well as in-hospital use. Our experiences illustrate two sets of important questions: are the individual components reliable (e.g., physical integrity, power, core functionality, and end-user interaction) and is the connectivity between components reliable (e.g., communication protocols and the metadata necessary for data interpretation)? While all potential operational issues cannot be fully anticipated and eliminated during development, thoughtful design and phased testing steps can reduce, if not eliminate, technical surprises.

Indexed as

AlgorithmsDecision Support Systems, ClinicalHospitalsHumansSoftwareVital Signscombat casualty careDecision-support algorithmsdevice connectivityprehospital carevital-sign data

Identifiers

PMID24155791
PMCPMC3799209
OpenAlexW1986241684

What OpenQuestion holds

Textmetadata
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.