A new process that consolidates machine learning algorithms with unique human-based expertise promises to improve the regulatory and approval process for new medical devices by reducing the recall rate by nearly 39% and saving up to $2.9 billion a year, according to a new study by researchers at Harvard Kennedy School, Indiana University, and Emerging Health Consulting.
Background: Accounting for race and ethnicity in estimating disease risk may improve the accuracy of predictions but may also encourage a racialized view of medicine. Objective: To present a decision
This study revisits a critical juncture in the development of national health insurance (NHI) in the United States in the post-World War II era.
The principles of sovereignty, equality, and self-determination – which have long been the basis for nonintervention in the affairs of sovereign states – have more recently been widened to condemn out
Among healthcare researchers, there is increasing debate over how best to assess and ensure the fairness of algorithms used for clinical decision support and population health—particularly concerning
Harvard Kennedy School Professor Malcolm Sparrow and a global public health commission predict that gamblers could lose up to $700 billion annually by 2028, creating a serious global public health concern.
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