Hostname: page-component-8448b6f56d-sxzjt Total loading time: 0 Render date: 2024-04-24T18:00:05.883Z Has data issue: false hasContentIssue false

“MedTRIS” (Medical Triage and Registration Informatics System): A Web-based Client Server System for the Registration of Patients Being Treated in First Aid Posts at Public Events and Mass Gatherings

Published online by Cambridge University Press:  08 August 2016

Stefan Gogaert
Affiliation:
Belgian Red Cross-Flanders, Mechelen, Belgium
Axel Vande veegaete*
Affiliation:
Belgian Red Cross-Flanders, Mechelen, Belgium
Annelies Scholliers
Affiliation:
Belgian Red Cross-Flanders, Mechelen, Belgium Department of Anaesthesiology and Perioperative Medicine, University Hospital, Free University of Brussels, Brussels, Belgium
Philippe Vandekerckhove
Affiliation:
Belgian Red Cross-Flanders, Mechelen, Belgium Faculty of Medicine, University of Ghent, Ghent, Belgium Department of Public Health and Primary Care, Faculty of Medicine, Catholic University of Leuven, Leuven, Belgium
*
Correspondence: Axel Vande veegaete, MS Motstraat 40 B-2800 Mechelen, Belgium E-mail: Axel.vandeveegaete@rodekruis.be

Abstract

First aid (FA) services are provisioned on-site as a preventive measure at most public events. In Flanders, Belgium, the Belgian Red Cross-Flanders (BRCF) is the major provider of these FA services with volunteers being deployed at approximately 10,000 public events annually. The BRCF has systematically registered information on the patients being treated in FA posts at major events and mass gatherings during the last 10 years. This information has been collected in a web-based client server system called “MedTRIS” (Medical Triage and Registration Informatics System). MedTRIS contains data on more than 200,000 patients at 335 mass events. This report describes the MedTRIS architecture, the data collected, and how the system operates in the field. This database consolidates different types of information with regards to FA interventions in a standardized way for a variety of public events. MedTRIS allows close monitoring in “real time” of the situation at mass gatherings and immediate intervention, when necessary; allows more accurate prediction of resources needed; allows to validate conceptual and predictive models for medical resources at (mass) public events; and can contribute to the definition of a standardized minimum data set (MDS) for mass-gathering health research and evaluation.

GogaertS, Vande veegaeteA, ScholliersA, VandekerckhoveP. “MedTRIS” (Medical Triage and Registration Informatics System): A Web-based Client Server System for the Registration of Patients Being Treated in First Aid Posts at Public Events and Mass Gatherings. Prehosp Disaster Med. 2016;31(5):557–562.

Type
Special Reports
Copyright
© World Association for Disaster and Emergency Medicine 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Arbon, P, Cusack, L, Verdonck, N. Mass gathering public health and emergency medicine literature review: levels of evidence. Australasian Journal of Paramedicine. 2013;10(1).CrossRefGoogle Scholar
2. Arbon, P. Mass-gathering medicine: a review of the evidence and future directions for research. Prehosp Disaster Med. 2007;22(2):131-135.CrossRefGoogle ScholarPubMed
3. Gogaert, S. Main reasons for transfer of patients to a hospital during mass gatherings. Abstract presented at: 19th World Congress on Disaster and Emergency Medicine; April 21-24, 2015; Cape Town, South Africa.Google Scholar
4. Gogaert, S. Type of mass gathering event determines/influences the duration of stay in, and therefore the size needed of, a first aid post. Abstract presented at: 19th World Congress on Disaster and Emergency Medicine; April 21-24, 2015; Cape Town, South Africa.Google Scholar
5. Arbon, P, Bridgewater, F, Smith, C. Mass gathering medicine: a predictive model for patient presentation and transport rates. Prehosp Disaster Med. 2001;16(3):150-158.Google Scholar
6. Serwylo, P, Arbon, P, Rumantir, G. Predicting Patient Presentation Rates at Mass Gatherings using Machine Learning. 2011. Conference Proceedings; 8th International ISCRAM Conference; Lisbon, Portugal.Google Scholar
7. Zeitz, KM, Schneider, DP, Jarrett, D, et al. Mass-gathering events: retrospective analysis of patient presentations over seven years. Prehosp Disaster Med. 2002;17(3):147-150.Google Scholar
8. Hartman, N, Williamson, A, Sojka, B, et al. Predicting resource use at mass gatherings using a simplified stratification scoring model. Am J Emerg Med. 2009;27(3):337-343.Google Scholar
9. Moore, R, Williamson, K, Sochor, M, et al. Large-event medicine–event characteristics impacting medical need. Am J Emerg Med. 2011;29(9):1217-1221.Google Scholar
10. Ranse, J, Hutton, A, Turris, SA, et al. Enhancing the minimum data set for mass-gathering research and evaluation: an integrative literature review. Prehosp Disaster Med. 2014;29(3):280-289.Google Scholar
11. Alquthami, AH, Pines, JM. A systematic review of non-communicable health issues in mass gatherings. Prehosp Disaster Med. 2014;29(2):167-175.Google Scholar
12. Ranse, J, Hutton, A. Minimum data set for mass-gathering health research and evaluation: a discussion paper. Prehosp Disaster Med. 2012;27(6):543-550.Google Scholar