Literature DB >> 22317163

Radiological emergency response for community agencies with cognitive task analysis, risk analysis, and decision support framework.

Travis S Meyer1, Joseph Z Muething, Gustavo Amoras Souza Lima, Breno Raemy Rangel Torres, Trystyn Keia del Rosario, José Orlando Gomes, James H Lambert.   

Abstract

Radiological nuclear emergency responders must be able to coordinate evacuation and relief efforts following the release of radioactive material into populated areas. In order to respond quickly and effectively to a nuclear emergency, high-level coordination is needed between a number of large, independent organizations, including police, military, hazmat, and transportation authorities. Given the complexity, scale, time-pressure, and potential negative consequences inherent in radiological emergency responses, tracking and communicating information that will assist decision makers during a crisis is crucial. The emergency response team at the Angra dos Reis nuclear power facility, located outside of Rio de Janeiro, Brazil, presently conducts emergency response simulations once every two years to prepare organizational leaders for real-life emergency situations. However, current exercises are conducted without the aid of electronic or software tools, resulting in possible cognitive overload and delays in decision-making. This paper describes the development of a decision support system employing systems methodologies, including cognitive task analysis and human-machine interface design. The decision support system can aid the coordination team by automating cognitive functions and improving information sharing. A prototype of the design will be evaluated by plant officials in Brazil and incorporated to a future trial run of a response simulation.

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Year:  2012        PMID: 22317163     DOI: 10.3233/WOR-2012-0659-2925

Source DB:  PubMed          Journal:  Work        ISSN: 1051-9815


  2 in total

1.  Understanding complex clinical reasoning in infectious diseases for improving clinical decision support design.

Authors:  Roosan Islam; Charlene R Weir; Makoto Jones; Guilherme Del Fiol; Matthew H Samore
Journal:  BMC Med Inform Decis Mak       Date:  2015-11-30       Impact factor: 2.796

2.  Understanding Clinician Macrocognition to Inform the Design of a Congenital Heart Disease Clinical Decision Support System.

Authors:  Azadeh Assadi; Peter C Laussen; Gabrielle Freire; Patricia Trbovich
Journal:  Front Cardiovasc Med       Date:  2022-02-03
  2 in total

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