Web-Based Self-Triage of Influenza-Like Illness During the 2009 H1N1 Influenza Pandemic
Identifieur interne : 000F01 ( PascalFrancis/Checkpoint ); précédent : 000F00; suivant : 000F02Web-Based Self-Triage of Influenza-Like Illness During the 2009 H1N1 Influenza Pandemic
Auteurs : Arthur L. Kellermann [États-Unis] ; Alexander P. Isakov [États-Unis] ; Ruth Parker [États-Unis] ; Michael T. Handrigan [États-Unis] ; Seth Foldy [États-Unis]Source :
- Annals of emergency medicine [ 0196-0644 ] ; 2010.
Descripteurs français
- Pascal (Inist)
English descriptors
Abstract
The sudden emergence of 2009 H1N1 influenza in the spring of that year sparked a surge in visits to emergency departments in New York City and other communities. A larger, second wave of cases was anticipated the following autumn. To reduce a potential surge of health system utilization without denying needed care, we enlisted the input of experts from medicine, public health, nursing, information technology, and other disciplines to design, test, and deploy clinical algorithms to help minimally trained health care workers and laypeople make informed decisions about care-seeking for influenza-like illness. The product of this collaboration, named Strategy for Off-Site Rapid Triage (SORT) was disseminated in 2 forms. Static algorithms, posted on the Centers for Disease Control and Prevention's Web site, offered guidance to clinicians and telephone call centers on how to manage adults and children with influenza-like illness. In addition, 2 interactive Web sites, http://www.Flu.gov and http://www. H1N1ResponseCenter.com, were created to help adults self-assess their condition and make an informed decision about their need for treatment. Although SORT was anchored in a previously validated clinical decision rule, incorporated the input of expert clinicians, and was subject to small-scale formative evaluations during rapid standup, prospective evaluation is lacking. If its utility and safety are confirmed, SORT may prove to be a useful tool to blunt health system surge and rapidly collect epidemiologic data on future disease outbreaks.
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