Technology can be used to assist physicians in diagnosis, decision-making, and treatment personalization, as well as help individuals make informed decisions and stick to their plans.

Domain Statement

Technology affects health outcomes in several ways. Patient diagnosis can be improved using artificial intelligence. Information systems can be used to help physicians track information, access research, and remember to adhere to protocols ranging from treatment decisions to handwashing. Individual treatment plans can be personalized. Digital decision tools can help guide individuals to make more informed decisions about their own health. Technology can be used to help remind and nudge patients to take drugs, exercise, or adhere to treatment regimes.

Project Abstracts

Read about some of the health projects that the lab is currently working on.

Do Commonly Prescribed Drugs Prevent COVID-19 Symptoms?

A variety of commonly prescribed drugs have the potential to prevent or treat COVID-19 symptoms. Using historical health claims data and methods combining machine learning and causal inference, this project identifies if these commonly prescribed drugs improved the outcome of patients hospitalized during the COVID-19 crisis. The analysis compares the patient outcomes of those who were already taking these drugs to those who were not at the time of hospitalization and uses a technique that pools information from different hospital systems from around the world to enable a larger sample to be considered together.

AI & Machine Learning

Evaluating the Benefits of Commonly Prescribed Drugs to Prevent Poor Outcomes from Respiratory Distress

Applying recently developed machine learning and causal inference methods to historical health claims data allows the evaluation of the impact of certain medications on outcomes for patients hospitalized with respiratory conditions. The methods incorporate new techniques to estimate causal effects across distinct, proprietary data sources without merging the datasets. The results can be used to suggest candidates for clinical trials in the fight against COVID-19.

AI & Machine Learning

Accelerating Health Technology Innovation to Address COVID-19

Accelerating the development of health technology such as treatments, diagnostics, and vaccines is crucial to ending the COVID-19 crisis as quickly as possible. This project evaluates alternative methods for speeding up the process, including the design of incentives for development and manufacturing, as well as approaches for optimizing clinical trials using shared control groups and adaptive experiments.

Incentive Design

Adaptive Experiments to Help Patients Make Informed Choices About Contraception

Lab researchers are working with Yaounde Gynecology, Obstetrics and Pediatrics Hospital in Cameroon to help women make informed choices about contraceptives. Adaptive experiments identify effective and efficient strategies, including price subsidies, for providing information through a tablet application. The experiment accelerates learning in an environment of many potential alternatives and improves expected patient outcomes by allocating more and more patients to the tablet design that works best.

Adaptive & Iterative Experimentation, Social Sciences & Behavioral Nudges, AI & Machine Learning


Learn firsthand from health researchers and practitioners in interviews conducted by the lab.

Michael Kremer: “Copays” to Shape the Vaccine Market to Reduce Global Poverty

Michael Kremer, Nobelist and Gates Professor of Developing Societies at Harvard, was lead economist in an effort by the global health community to overcome the private sector’s underinvestment in vaccines for diseases of the poor.

Marta Milkowska: Technology Meets Behavioral Science for the Social Good

Stanford MBA student Marta Milkowska has followed an unusual path that established her credentials as one of her generation’s leading innovators in economic development.