AZEBRA (Almost Zero Error Basepair-based Record Alert): A genomic clinical decision support system
dc.contributor.author | Kulanthaivel, Anand | |
dc.contributor.author | Kshirsagar, Madhura M. | |
dc.contributor.author | Alarifi, Mohammad | |
dc.contributor.author | Oki, Mark N. | |
dc.contributor.author | Jones, Josette F. | |
dc.date.accessioned | 2022-04-22T21:12:18Z | |
dc.date.available | 2022-04-22T21:12:18Z | |
dc.date.issued | 2017 | |
dc.description.abstract | The idea of the United States's Precision Medicine Initiative (PMI) was to allow providers (and patients) to leverage large amounts of information (including patient genomic data) in order to create actionable knowledge that increases patient well-being. To this end, we propose a system called AZEBRA; the acronym stands for Almost Zero Error Basepair-based Record Alerts. Zebra, in addition to being a well-known wild animal, is a common medical slang term for the clinician's fallacy of mistakenly corning to a rare and sometimes dire diagnosis (the rare zebra diagnosis) due to having missed more common causes of patient symptoms (the common horse diagnosis); conversely, patients with rare conditions would be better thought of as zebras and not horses. AZEBRA is intended to leverage the principles of genetically-enhanced precision medicine in order to alert clinicians to the presence of patients with five (four rare, one common) genetic pathologies that are ordinarily sources of unnecessary morbidity and mortality in clinical settings. | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/28744 | |
dc.title | AZEBRA (Almost Zero Error Basepair-based Record Alert): A genomic clinical decision support system | en_US |
dc.type | Poster | en_US |