Browsing by Author "Alarifi, Mohammad"

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    AZEBRA (Almost Zero Error Basepair-based Record Alert): A genomic clinical decision support system
    (2017) Kulanthaivel, Anand; Kshirsagar, Madhura M.; Alarifi, Mohammad; Oki, Mark N.; Jones, Josette F.
    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.
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    Using cognitive fit theory to evaluate patient understanding of medical images
    (IEEE, 2017) Gichoya, Judy Wawira; Alarifi, Mohammad; Bhaduri, Ria; Tahir, Bilal; Purkayastha, Saptarshi; Radiology and Imaging Sciences, School of Medicine
    Patients are increasingly presented with their health data through patient portals in an attempt to engage patients in their own care. Due to the large amounts of data generated during a patient visit, the medical information when shared with patients can be overwhelming and cause anxiety due to lack of understanding. Health care organizations are attempting to improve transparency by providing patients with access to visit information. In this paper, we present our findings from a research study to evaluate patient understanding of medical images. We used cognitive fit theory to evaluate existing tools and images that are shared with patients and analyzed the relevance of such sharing. We discover that medical images need a lot of customization before they can be shared with patients. We suggest that new tools for medical imaging should be developed to fit the cognitive abilities of patients.