• Evaluation of CYP2C19 Phenotype Impact on Sertraline Treatment of Major Depressive Disorder

  Kashyap, Eshaan; Rivas, Salvador; Hashemzadeh, Mehrtash; The University of Arizona College of Medicine - Phoenix; Heise, William (The University of Arizona., 2024)

  Treatment for Major Depressive Disorder (MDD) has classically involved selective-serotonin reuptake inhibitors (SSRIs), but the appropriate selection of a specific pharmacologic has remained elusive. Clinical response to an SSRI can be predicted by clinical improvement within weeks of treatment initiation, but for many patients, clinical improvement is never identified. Recent research demonstrates an association between pharmacokinetics of SSRIs and the metabolizer phenotype of their associated cytochromes. In this retrospective chart review, we investigated 94 adult patients who were diagnosed with a depressive disorder and prescribed sertraline. Each patient’s clinical response to sertraline, as determined by clinician judgment, was recorded and evaluated with respect to the patient’s CYP2C19 metabolizer phenotype. We hypothesized that adult patients who are CYP2C19 ultra-rapid metabolizers and rapid metabolizers (UM+RM) did not experience response with sertraline when compared to patients who are CYP2C19 normal metabolizers (NM). We found no significant difference in clinical response between UM+RM and NM patients, and therefore patients may not benefit from preemptive pharmacogenomic testing for their CYP2C19 metabolism phenotype.
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  What Do Librarians Need to Know about Quantitative Methods in Digital Humanities?

  Froehlich, Heather; University of Arizona Libraries (Association of College & Research Libraries (ACRL), 2024)
• Informed consent for artificial intelligence in emergency medicine: A practical guide

  Iserson, Kenneth V; Department of Emergency Medicine, The University of Arizona (Elsevier, 2023-11-25)

  As artificial intelligence (AI) expands its presence in healthcare, particularly within emergency medicine (EM), there is growing urgency to explore the ethical and practical considerations surrounding its adoption. AI holds the potential to revolutionize how emergency physicians (EPs) make clinical decisions, but AI's complexity often surpasses EPs' capacity to provide patients with informed consent regarding its use. This article underscores the crucial need to address the ethical pitfalls of AI in EM. Patient autonomy necessitates that EPs engage in conversations with patients about whether to use AI in their evaluation and treatment. As clinical AI integration expands, this discussion should become an integral part of the informed consent process, aligning with ethical and legal requirements. The rapid availability of AI programs, fueled by vast electronic health record (EHR) datasets, has led to increased pressure on hospitals and clinicians to embrace clinical AI without comprehensive system evaluation. However, the evolving landscape of AI technology outpaces our ability to anticipate its impact on medical practice and patient care. The central question arises: Are EPs equipped with the necessary knowledge to offer well-informed consent regarding clinical AI? Collaborative efforts between EPs, bioethicists, AI researchers, and healthcare administrators are essential for the development and implementation of optimal AI practices in EM. To facilitate informed consent about AI, EPs should understand at least seven key areas: (1) how AI systems operate; (2) whether AI systems are understandable and trustworthy; (3) the limitations of and errors AI systems make; (4) how disagreements between the EP and AI are resolved; (5) whether the patient's personally identifiable information (PII) and the AI computer systems will be secure; (6) if the AI system functions reliably (has been validated); and (7) if the AI program exhibits bias. This article addresses each of these critical issues, aiming to empower EPs with the knowledge required to navigate the intersection of AI and informed consent in EM.
• Magical thinking: Its effect on emergency medical care

  Iserson, Kenneth V; Department of Emergency Medicine, The University of Arizona (Elsevier, 2023-07-26)

  Magical thinking is a cognitive process characterized by beliefs in supernatural causality and the power of rituals. Grounded in personal convictions rather than objective reality, it involves subjective beliefs rather than magic tricks. Magical thinking's effects range from potentially positive, such as bringing hope and comfort, to negative consequences, including delays in seeking appropriate medical care and refusing evidence-based treatments. This article provides an overview of magical thinking, including its prevalence, diverse forms, and influence on patients, families, and emergency physicians (EPs). This article offers guidelines for recognizing signs of magical thinking and emphasizes the importance of respectful and empathetic interactions with patients and their families. Highlighting both the benefits and detriments of magical thinking in Emergency Medical (EM) care, the article discusses the knowledge and tools needed to optimize patient outcomes. It acknowledges the varying belief systems and cultural practices that contribute to the prevalence of magical thinking. For physicians and other EM professionals, addressing magical thinking requires cultural competence and empathetic engagement. Active listening and shared decision-making are essential to promote positive patient outcomes. By recognizing and understanding magical thinking and fostering effective communication, EPs can navigate the delicate balance of addressing patients' beliefs while delivering evidence-based care.
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  Orchestrating the critical: Library instruction programs and our labor (BTAA Keynote)

  Pagowsky, Nicole; University of Arizona Libraries (2024-04-18)

  Conceptualizing and incorporating critical information literacy into our instruction programs at the intersections of pedagogy, campus dynamics, relationships with faculty, threats to higher education, and burnout in our labor of primarily one-shot instruction models. Discussions for more sustainable library instruction programs are brought forward with an example of UArizona Libraries' critical information literacy tutorials that engage a Teach the Teacher approach.

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