Policy & Administration

As a busy surgeon who has worked in many of the best hospitals in the nation, he can testify to the amazing power of modern medicine to cure. But he's also been a witness to a medical culture that routinely leaves surgical sponges inside patients, amputates the wrong limbs, and overdoses children because of sloppy handwriting. Over the last ten years, neither error rates nor costs have come down, despite scientific progress and efforts to curb expenses. To patients, the healthcare system is a black box.

In Why Are Health Disparities Everyone's Problem? Dr. Lisa Cooper shows how we can work together to eliminate the injustices that plague our health care system and society. The book follows Cooper's journey from her childhood in Liberia, West Africa, to her thirty-year career working first as a clinician and then as a health equity researcher at Johns Hopkins University. Drawing on her experiences, it explores how differences in communication and the quality of relationships affect health outcomes.

Medical professionals are among the highest-sued people in the most litigious country in the world, which means if that string of letters behind your name includes a “D,” you’ve got a big target on your back. It’s usually not a matter of if you’ll find yourself at the wrong end of a lawsuit, but when. What’s worse, although for everyone else, a settlement means the end of the matter, that’s not so for you.

A healthcare AI expert examines how artificial intelligence can either undermine or enhance the doctor-patient relationship, offering frameworks to keep medicine human-centered. Subjects to Sovereigns explores what happens when the most powerful AI systems in medicine make decisions about patients who are never really heard? For most of modern medicine, patients have been treated as data points inside someone else’s system. Artificial intelligence risks locking that pattern in forever.

Organizations have invested $30–40 billion in AI. According to MIT (2025), 95% produced no measurable return. Only 5% made it into daily workflows. If you're reading this, you're trying not to become that statistic—or you already are. The problem isn't bad technology. Most AI failures don't begin at scale. They begin during pilots, when early success builds confidence without revealing risks that surface once AI enters real workflows and decisions under pressure.