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dc.contributor.authorLong, Jon Brantleyen_US
dc.date.accessioned2016-03-16T14:44:57Z
dc.date.available2016-03-16T14:44:57Z
dc.date.issued2015
dc.identifier.urihttps://hdl.handle.net/2144/15194
dc.description.abstractPoisonings account for 0.8% of emergency room visits each year. Our review of current toxicological resources revealed a gap in their ability to provide expedient calculations and recommendations, as they are broad in scope and time-consuming to read. Time is crucial in a toxicologic emergency. Delay in first dose can lead to life-threatening sequelae. To bridge the gap, we developed the Antidote Application (AA), a computational system that automatically provides patient-specific antidote treatment recommendation(s) and individualized dose calculation(s). We implemented 27 algorithms that describe FDA approved use and evidence-based practices found in primary literature for the treatment of common toxin exposure. The AA covers 29 antidotes recommended by Poison Control and toxicology experts, 31 toxins from 19 toxin classes, and over 200 toxic entities. We implemented the AA in two formats: a standalone downloadable application for offline use and an online web application. The AA represents a unique educational resource for the study of toxicology with the potential of being adopted for point of care decision support. The system also provides guidance for reporting toxic exposures regionally and nationally as required by accrediting bodies and some states. The AA system has the potential for reducing initial dose delays and medication errors. To the best of our knowledge, the AA is the first educational and decision support system in toxicology that provides patient-specific treatment recommendations and drug dose calculations. The downloadable and online Antidote Applications are publically available at http://www.met-hilab.org/files/antidote/antidote_application.jar and http://projects.met-hilab.org/antidote/ respectively.en_US
dc.language.isoen_US
dc.subjectInformation technologyen_US
dc.subjectOverdoseen_US
dc.subjectToxicologyen_US
dc.subjectClinical decision support systemen_US
dc.titleA clinical decision support system for the treatment of common toxin overdoseen_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-03-12T07:15:10Z
etd.degree.nameMaster of Scienceen_US
etd.degree.levelmastersen_US
etd.degree.disciplineComputer Information Systemsen_US
etd.degree.grantorBoston Universityen_US


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