Crowdsourcing health discoveries : from anecdotes to aggregated self-experiments

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 a 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, formally validates the effectiveness of an intervention on a single person. Aggregated Personal Experiments enables user communities to translate anecdotal correlations into repeatable trials that can validate efficacy in the context of their daily lives. Aggregating the outcomes of multiple trials improves the efficiency of future trials and enables users to prioritize trials for a given condition. Successful outcomes from many patients provide evidence to motivate future clinical research. The model, and the design principles that support it were evaluated through a set of focused user studies, secondary data analyses, and experience with real-world deployments.