Challenging the Gold Standard: Tufts Professor Reframes Medical Evidence in New Book

Tufts University School of Medicine’s Olaf Dammann, MD, PhD,—physician, perinatal epidemiologist, and philosopher of medicine—is out with a new book that challenges some of the most entrenched assumptions in medical research and practice. In Uncertainty and Explanation in Medicine and the Health Sciences, Dr. Dammann urges readers to rethink how we define effectiveness in medicine and how we use evidence to guide health decisions.

“There was no lightning bolt moment,” he says of the book’s origin. “It’s a continuation—part of a long evolution in my thinking,” building on the foundation laid in his earlier works Causation in Population Health Informatics and Etiological Explanations.

In this latest book, Dr. Dammann pushes back against a growing school of thought known as “medical nihilism,” popularized by philosopher Jacob Stegenga. The idea: that medicine is largely ineffective. Dr. Dammann disagrees—not because he believes medicine always works, but because, he argues, we’re asking the wrong questions.

“The problem isn’t that medicine fails,” he explains. “It’s that we judge it by whether it cures. But medicine’s goal is to help.”

One major focus of the book is our over-reliance on randomized controlled trials (RCTs), often labeled the gold standard in medical evidence. While acknowledging their value, Dr. Dammann critiques their dominance, arguing they fail to capture the full picture. “RCTs aren’t perfect,” he says. “And pretending they are keeps us from seeing—and using—other valuable sources of evidence.”

Instead of throwing out RCTs, Dr. Dammann proposes an expansion: a structured framework for understanding cause and prognosis in health science, centered on what he calls etio-prognostic explanations. The approach culminates in an “evidence map,” introduced in Chapter 10, which synthesizes different kinds of data to support decisions in clinical and public health contexts.

Uncertainty—both natural (aleatoric) and cognitive (epistemic)—is a central theme. “There is no perfect research result,” he explains in his video interview. “There’s always room for error—what we call statistical variability.”

These ideas also shape how Dr. Dammann teaches in the Tufts MPH and MD/MPH programs. “Our students are trained to think critically,” he says. “To detect the faults in evidence, and to use the best available information in ways that actually help people.”

Ultimately, the book argues that good medicine and sound public health policy require more than just high-powered statistics—they require flexible, thoughtful reasoning. “Public health is inherently interdisciplinary,” he says. “And we need to embrace that in how we assess what works.”

Asked what readers should take away from Uncertainty and Explanation in Medicine and the Health Sciences, Dr. Dammann is clear: “Don’t rely on just one kind of evidence. Use every tool available—especially those that help explain the why and how of disease.”

Because in a world full of uncertainty, a better understanding of the evidence just might lead to better health for all.

Watch