Reducing Bias in Healthcare Artificial Intelligence

Abstract

The groundbreaking Institute of Medicine (IOM) report, “Unequal Treatment,” 1 Institute of Medicine (US) Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health CareSmedley BD Stith AY Nelson AR Unequal treatment: confronting racial and ethnic disparities in health care. National Academies Press (US), 2003 Google Scholar exposed direct evidence of racial and ethnic disparities in healthcare delivery. Bias in healthcare was not a new phenomenon, but the many examples of deeper and more systemic issues were alarming. Unfortunately, it has taken recent socio-political events and the disproportionately negative impact of the Covid virus on communities of Black, Indigenous, and People of Color (BIPOC), to bring increased attention back to these issues. The digitization of healthcare data and greater reliance on technology tools like Artificial Intelligence (AI), have been identified as a means of reducing the kinds of bias the IOM found. Unfortunately, the research on AI systems and the algorithms on which they are built are finding that in some cases, these tools may actually be making disparities worse. The perianesthesia nurse will need to be able to recognize how AI will continue to influence their practice and their role in ensuring that these kinds of technology systems equally benefit all healthcare users.