Racial treatment disparities after machine learning surgical risk-adjustment

Health Equity in Clinical Research Informatics

“…Indeed, algorithmic bias appears in general as a difficult barrier for health equity to overcome [26-30, 33-37, 51, 55]. The current conditions where CRI demonstrates promotion of health equity appears admittedly to be those where CRI is used specifically to address inequity in health; not only in the evaluation of AI-based healthcare instruments ability to promote health equity [51,55], but through AI-based methods demonstrating and addressing disparities in the delivery of healthcare services [52][53][54].…”

“…In contrast to the other included sources in this scoping review, these sources address health equity by directly responding to existing health inequities. Pierson et al [54] and Hammarlund [52] both have in common the use of AI in exposing health inequity in clinical practices and providing alternative solutions, while Lu et al [53] uses AI to promote health equity in a setting known to be characterized by disparities in health and access to health. All the included sources of this scoping review address health equity in CRI with patient implications, either by exposing health inequality in AI-based solutions or by examining possibilities for AI to extract data of importance to address health equity [46,47,49,51,55].…”

“…All the included sources of this scoping review address health equity in CRI with patient implications, either by exposing health inequality in AI-based solutions or by examining possibilities for AI to extract data of importance to address health equity [46,47,49,51,55]. However, Pierson et al [53], Lu et al [53], and Hammarlund [52] all stand out in their application of AI to drive health equity. The accomplishments of these three studies appear to stem from how they approach the issue of health equity.…”

“…Building from observations that underserved populations experience a greater amount of pain in osteoarthritis, Pierson et al [54] used a deep learning approach on radiographs to predict the pain level of the individual patient, finding that the approach significantly reduced unexplained racial pain disparities compared to traditional methods. Through a ML-based method, Hammarlund [52] demonstrated disparities between black and white patients in acute myocardial infarction treatment, beyond that explained by health risk differences. As a response to how language discordances limited the contact tracing of a non-English speaking population in California, already disproportionately affected by COVID-19, Lu et al [53] used an ML-based approach to predict the language of an incoming patient and match this to the language of the contact tracer.…”

“…The accomplishments of these three studies appear to stem from how they approach the issue of health equity. Instead of illuminating health inequity present in CRI-driven solutions, such as algorithmic bias within AI-based prediction models [51,55], they use AI to promote and improve health equity in the deliverance of existing treatments and health care services [52][53][54].…”