Crowdsourcing Health Discoveries: from Anecdotes to Aggregated Self-Experiments

Time & Location 

July 26th, 1:00 PM, E14-244

Thesis Supervisor

Frank Moss, Professor of the Practice of Media Arts and Sciences, MIT

Committee 

Henry Lieberman, Principle Research Scientist, MIT
Peter Szolovits, Professor of Computer Science and Engineering, MIT

Abstract

Nearly one quarter of US adults read patient-generated health information found on blogs, forums and social media; many say they use this information to influence everyday health decisions. Topics of discussion in online forums are often poorly-addressed by existing, clinical research, so patient’s reported experiences are the only  evidence. No rigorous methods exist to help patients leverage anecdotal evidence to make better decisions.

This dissertation reports on multiple prototype systems that help patients augment anecdote with data to improve individual decision making, optimize healthcare delivery, and accelerate research.  The web-based systems were developed through a multi-year collaboration with individuals, advocacy organizations, healthcare providers, and biomedical researchers. The result of this work is a new scientific model for crowdsourcing health insights: Aggregated Self-Experiments.

The self-experiment, a type of single-subject (n-of-1) trial, validates the effectiveness of an intervention on a single person. Aggregating the outcomes of multiple trials can improve the efficiency of future trials and enable users to prioritize the sequencing of trials for a given condition. Successful outcomes from many patients will yield evidence to motivate future clinical research.  Aggregated Personal Experiments enables user communities to replace anecdotes with repeatable trials that can be run in the context of their daily life.  The properties and viability of the model were evaluated through user studies, secondary data analyses, and experience with real-world deployments.

You can check it out at http://www.personalexperiments.org