Journal of general internal medicine
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Misinformation about reproductive health threatens to harm health outcomes, compromise medical trust, and enable misinformed policy restrictions. In recent years, reproductive health misinformation has proliferated online due to ideological campaigns and limited content moderation for reproductive health topics. Developing evidence-based practices to counter reproductive health misinformation requires an understanding of the content that women are exposed to online, which is currently lacking. ⋯ Fourteen percent promoted alternative medicine. Smaller numbers of claims and narratives exaggerated risks of medical interventions, discouraged evidence-based interventions, directly undermined medical trust, and proposed inaccurate biological mechanisms. Healthcare professionals can proactively promote evidence-based medical decision-making by increasing their awareness of prominent misleading claims and narratives.
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Editorial
Decoding Homelessness: Z-Codes and the Recognition of Homelessness as a Comorbid Condition.
There are an estimated 653,100 people across the United States experiencing homelessness. Homelessness is an important social determinant of health associated with increased morbidity and mortality. ⋯ Here, we review the historical purpose and utilization of codes to identify SDOH ("Z-codes"); describe how the recent CMS policy change elevates the importance of homelessness within medical care and impacts reimbursement; analyze the potential risks and benefits of this change to patients, clinicians, and health systems; and assess barriers to implementation. We conclude by calling for health systems to move beyond simply documenting homelessness to meaningfully addressing health inequities in PEH.
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Diagnostic errors, often due to biases in clinical reasoning, significantly affect patient care. While artificial intelligence chatbots like ChatGPT could help mitigate such biases, their potential susceptibility to biases is unknown. ⋯ It seems that, while ChatGPT is not sensitive to bias when biasing information is situational, it is sensitive to bias when the biasing information is part of the patient's disease history. Its utility in diagnostic support has potential, but caution is advised. Future research should enhance AI's bias detection and mitigation to make it truly useful for diagnostic support.