Damon Centola
Damon Centola is the Elihu Katz Professor of Communication, Sociology and Engineering at the University of Pennsylvania, where he is Director of the Network Dynamics Group and Senior Fellow at the Leonard Davis Institute of Health Economics. Before coming to Penn, Damon was a professor at MIT, and prior to that he was a fellow at Harvard University.
Damon is one of the world's leading scholars on social networks and behavior change. His work has received numerous scientific awards, including the Goodman Prize for Outstanding Contributions to Sociological Methodology in 2011; the James Coleman Award for Outstanding Research in Rationality and Society in 2017; and the Harrison White Award for Outstanding Scholarly Book in 2019. He was a developer of the NetLogo agent based modeling environment, and was awarded a U.S. Patent for inventing a method to promote diffusion in online networks. Damon is a fellow of the Center for Advanced Study in the Behavioral Sciences at Stanford University.
Popular accounts of Damon’s work have appeared in The New York Times, The Washington Post, The Wall Street Journal, Wired, TIME, The Atlantic, Scientific American and CNN, among other outlets. His research has been funded by the National Science Foundation, the National Institutes of Health, the Robert Wood Johnson Foundation, the James S. McDonnell Foundation, the Templeton Foundation, the Hewlett Foundation, and Facebook. Damon’s speaking and consulting clients include Amazon, Apple, Cigna, General Motors, Microsoft, Ben & Jerry’s, the U.S. Army, and the NBA, among others. He is a series editor for Princeton University Press and the author of How Behavior Spreads: The Science of Complex Contagions (Princeton 2018), and Change: How to Make Big Things Happen (Little Brown 2021).
The reduction of race and gender bias in clinical treatment recommendations using clinician peer networks in an experimental setting
Bias in clinical practice, in particular in relation to race and gender, is a persistent cause of healthcare disparities. We investigated the potential of a peer-network approach to reduce bias in medical treatment decisions within an experimental setting. We created “egalitarian” information exchange networks among practicing clinicians who provided recommendations for the clinical management of patient scenarios, presented via standardized patient videos of actors portraying patients with cardiac chest pain. The videos, which were standardized for relevant clinical factors, presented either a white male actor or Black female actor of similar age, wearing the same attire and in the same clinical setting, portraying a patient with clinically significant chest pain symptoms. We found significant disparities in the treatment recommendations given to the white male patient-actor and Black female patient-actor, which when translated into real clinical scenarios would result in the Black female patient being significantly more likely to receive unsafe under treatment, rather than the guideline-recommended treatment. In the experimental control group, clinicians who were asked to independently reflect on the standardized patient videos did not show any significant reduction in bias. However, clinicians who exchanged real-time information in structured peer networks significantly improved their clinical accuracy and showed no bias in their final recommendations. The findings indicate that clinician network interventions might be used in healthcare settings to reduce significant disparities in patient treatment.