Stanford University

Technology and Value

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Technology and Value
Advances in health-care technology have led to interventions and treatments never before possible. Our faculty study the effectiveness, costs, and value provided by these new technologies.

Perspective: Machine Learning for Health Should Consider Social Drivers of Health

In the Proceedings of Machine Learning Research, SHP's Sherri Rose and Neha Srivastha, a Stanford PhD candidate in computer science, write that clinical machine learning (ML) algorithms can exacerbate a wide range of injustices across multiple domains and levels of society. They recommend the consideration of social drivers of health throughout the pipeline of ML system development for examining algorithmic harms to health.

Stanford’s Data-Driven Fight Against Modern Slavery in Brazil

For over 25 years, Kimberly Babiarz has worked to address human trafficking, a crime she says “is everywhere.” Modern slavery — including sex and labor trafficking, forced labor, domestic servitude, debt bondage, and forced marriage — is “a big, systemic problem in need of big, systemic solutions,” says Babiarz, PhD, MA, a senior research scholar at Stanford Health Policy. That kind of solution requires diverse expertise, which Babiarz found by teaming up with longtime SHP collaborator Grant Miller and other members of the Stanford Human Trafficking Data Lab.

 

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Policy & Ethics: Governing AI in Mental Health

SHP’s Michelle Mello, JD, PhD, moderates the Policy & Ethics Panel at the 2026 Artificial Intelligence for Mental Health Symposium. The panelists distill the key ethical challenges raised by the use of mental health AI tools and companion chatbots, review regulatory approaches being explored at the state and federal levels, and discuss barriers to making effective, evidence-based policy.

AI, Insurance Denials, and the Future of Health Policy

Michelle Mello, JD, PhD, professor of health policy and law, joins The Future of Medicine for a conversation about one of the most frustrating and consequential parts of American healthcare: prior authorization. For many patients and clinicians, prior authorization is the process behind the letter saying an insurance company will not cover a medication, procedure, or service unless additional criteria are met. As Mello explains, this system is already difficult, time-consuming, and often painful for patients and care teams. Now, artificial intelligence is beginning to reshape how these decisions are made.