Publications

Risa Wolf and colleagues discuss how autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations, in NPJ Digital Medicine.

Alicia Arbaje, Daniel Polsky, and colleagues explore whether end-of-life (EOL) care differs for Medicare beneficiaries in Medicare Advantage (MA) compared to traditional Medicare (TM) in JAMA Health Forum

Tinglong Dai and colleagues write about supply-chain-aware ESG measures in responsible and sustainable operations, as part of the Springer Series in Supply Chain Management.

Catherine K Ettman, Elena Badillo-Goicoechea, and Elizabeth A Stuart in The Journal of Epidemiology and Community Health

Enthusiasm about the promise of artificial intelligence and machine learning in health care must be accompanied by oversight and remediation of any potential adverse effects on health equity goals that these technologies may create. We describe five equity imperatives for the use of AI/ML in health care that require attention from health care professionals, developers, and policymakers.

Telemedicine has emerged as a vital healthcare delivery model, especially pronounced during the COVID-19 pandemic. Our study uniquely focuses on an institutional lens, examining US hospitals to offer targeted policy implications.

Gerard Anderson with Richard Frank recently commented on draft guidance by the Centers for Medicare and Medicaid Services (CMS) on the Medicare Drug Price Negotiation Program

Diagnostic excellence refers to the optimal process to attain an accurate and precise explanation about a patient's condition and incorporates the perspectives of patients and their care partners. We aimed to craft a set of roadmaps illustrating goals and guiding the development of PRMs for diagnostic excellence ("Roadmaps").

As people lose Medicaid because of the end of the COVID-19 public health emergency, many states will route former Medicaid managed care enrollees into Affordable Care Act Marketplace coverage with the same carrier.

Care transitions among high-intensity units caring for patients with complex needs are a critical yet undeveloped area of patient safety research. This study introduces and tests the Multi-Team Shared Expectations Tool (MT-SET), an exercise that aims to engage health care teams in eliciting needs and establishing agreed-upon expectations teams and individuals within a multi-team system have of one another.