ArticleJMIR mHealth and uHealth2018
Mobile Decision Support Tool for Emergency Departments and Mass Casualty Incidents (EDIT): Initial Study.
Article in JMIR mHealth and uHealth, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Mobile Triage Applications: A Systematic Review in Literature and Play Store.Journal of medical systems · 2021Pooled it
- Mobile triage units for abdominal trauma in sub-Saharan Africa: current evidence, implementation barriers, and strategies for scale-up.Annals of medicine and surgery (2012) · 2026Review
- The Role of Telemedicine in Prehospital Traumatic Hand Injury Evaluation.Diagnostics (Basel, Switzerland) · 2023Review
- The use of kiosks to improve triage efficiency in the emergency department.NPJ digital medicine · 2023Review
- The Role of Health Kiosks: Scoping Review.JMIR medical informatics · 2022Article
- Patient triage system for supporting the operation of dispatch centres and rescue teams.BMC medical informatics and decision making · 2021Article
- Designing and executing a functional exercise to test a novel informatics tool for mass casualty triage.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- A Guide to Mass Casualty Incidents for Radiology Residents: Strategies, Ethics, Directions.Current problems in diagnostic radiologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundChemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system.
objectiveThis study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident.
methodsOver 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system.
resultsOf the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app's decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action.
conclusionsThe app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.
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Registered trials
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.