In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed enormous inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g. -image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging strategies for mitigating bias via decentralized learning, disentanglement, and model explainability.
Objectives/Hypothesis Follow‐up care in head and neck cancers (HNC) is critical in managing patient health. However, social determinants of health (SDOH) can create difficulties in maintaining follow‐up care. The study goal is to explore how SDOH impacts maintenance of HNC follow‐up care appointments. Methods A systematic retrospective chart review of 877 HNC patients diagnosed in the past 10 years a safety‐net tertiary care hospital with systems to help reduce care disparities. Cohort groups were identified and compared against protocols for follow‐up. Data were analyzed using analysis of variance, chi‐square tests, Fisher's exact tests, two‐sample t‐tests, and simple linear regression. Results The average length of follow‐up time in months and average total number of follow‐ups over 5 years were 32.96 (34.60) and 9.24 (7.87), respectively. There was no significant difference in follow‐up care between United States (US) versus non‐US born and English versus non‐English speaking patients. Race/ethnicity, county median household income, insurance status, and county educational attainment were not associated with differences in follow‐up. However, living a greater distance from the hospital was associated with lower follow‐up length and less frequency in follow‐up (P < .0001). Conclusion While income, primary language, country of birth, race/ethnicity, insurance status, and markers of educational attainment do not appear to impact HNC follow‐up at our safety‐net, tertiary care institution, and distance from hospital remains an important contributor to disparities in care. This study shows that many barriers to care can be addressed in a model that addresses SDOH, but there are barriers that still require additional systems and resources. Laryngoscope, 132:1022–1028, 2022
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