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The authors comment on an article by L. Shavelson and colleagues which identifies a key ethical concern associated with medical aid-in-dying (MAiD) laws in the U.S. jurisdictions where the practice is legal. Topics discussed include implications of neuroscience for volition and consent, challenges faced by physicians caring for patients undergoing MAiD, and enabling equity in expressions of volition.

Advances in medical care and technology have dramatically improved outcomes in patients undergoing neurosurgical intervention; however, certain patient subgroups (e.g., older adults) may encounter greater rates of morbidity and mortality in the perioperative period. The objective of this study was to determine the effects of patient and hospital characteristics, including age, on in-hospital mortality, and complication rates of 3 routine neurosurgical operations: subdural hematoma evacuation, brain tumor resection, and degenerative spine procedures. A retrospective multivariable analysis of the 2014 National Inpatient Sample was performed. The setting was a national sample of hospitalized inpatient stays occurring in 2014 in the United States. Patients (N = 48,963) included those undergoing subdural hematoma evacuation, brain tumor resection, or degenerative spine procedures, stratified according to age group (<65, 65–74, 75–84, 85+ years). Mortality and complication rate were measured. Age ≥85 years was found to increase the odds of mortality (odds ratio 11.32) and complications (odds ratio 2.64) in patients undergoing degenerative spine procedures, whereas age had no significant effect on mortality and complication rate in subdural hematoma evacuation and brain tumor resection. Multiple comorbidities and nonelective status were predictors of increased mortality and complication rate in all procedure groups. Overall, our data would suggest that increased age does not universally predict worse outcome and that, for many procedures, surgical decision-making in older patients should instead consider other pertinent factors, such as comorbidities and elective status. [ABSTRACT FROM AUTHOR]

Estimates in a 1989 study indicated that physicians in the United States were unable to reach a diagnosis that accounted for their patient's symptoms in up to 90% of outpatient patient encounters. Many proponents of artificial intelligence (AI) see the current process of moving from clinical data gathering to medical diagnosis as being limited by human analytic capability and expect AI to be a valuable tool to refine this process. The use of AI fundamentally calls into question the extent to which uncertainty in medical decision making is tolerated. Uncertainty is perceived by some as fundamentally undesirable and thus, for them, optimal decision making should be based on minimizing uncertainty. However, uncertainty cannot be reduced to zero; thus, relative uncertainty can be used as a metric to weigh the likelihood of various diagnoses being correct and the appropriateness of treatments. Here, the authors make the argument, using as examples the experiences of 2 AI systems, IBM Watson on Jeopardy and Watson for Oncology, that medical decision making based on relative uncertainty provides a better lens for understanding the application of AI to medicine than one that minimizes uncertainty. This approach to uncertainty has significant implications for how health care leaders consider the benefits and trade-offs of AI-assisted and AI-driven decision tools and ultimately integrate AI into medical practice., (Copyright © 2020 by the Association of American Medical Colleges.)