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Implicit bias is a natural survival instinct inherent in human beings. However, when implicit bias is not addressed in a healthcare setting, health disparities, gaps in care, and discrimination can occur in clinical settings. Algorithmic bias in healthcare still exists today, and as new technologies and digital innovations are introduced into the care continuum, leaders must acknowledge that many of the foundations the tools are built upon contain damaging bias that negatively impacts patient care. Taking deliberate steps to mitigate these biases can reduce health disparities and allow the promise of artificial intelligence to be fully realized in improving patient outcomes
Dr. Tania M. Martin-Mercado
Dr. Tania M. Martin-Mercado is Chief Clinical Researcher and Global Lead for Equity in Healthcare & Life Science at Microsoft. She is a clinical researcher with a focus on public health and diversity, equity and inclusion in clinical studies and healthcare. Dr. Martin-Mercado has deep expertise in designing and executing transformational scientific and technical programs and research studies to determine the safety and efficacy of care regimens, medications, and new interventions for the prevention, diagnosis, and treatment of disease. Dr. Martin-Mercado’s own research has included genetic variation and biomarker detection for patients suffering from systemic lupus erythematosus. She is passionate about genomics and its potential to forward precision medicine. Dr. Martin-Mercado is also a member of the Association for Clinical Research Professionals (ACRP) where she stays up to date on good clinical practices, diversity and ethics in clinical trials, and innovations in clinical research. She has her PhD in Biomedical Informatics, a Masters in Public Health, and additional degrees focused on health information technology and enterprise technology architecture
1. Define implicit bias and its impact on health disparities and patient care.
2. Describe ways implicit bias affects artificial intelligence and ways that bias can be mitigated.
3. Discuss strategies to mitigate the impact of bias through ethical data planning, data fairness, and accountability tools.
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