Crowdsourcing knowledge discovery and innovations in medicine

• Published in: Journal of medical Internet research Vol. 16; no. 9; p. e216
• Main Authors: Celi, Leo Anthony, Ippolito, Andrea, Montgomery, Robert A, Moses, Christopher, Stone, David J
• Format: Journal Article
• Language: English
• Published: Canada Journal of Medical Internet Research 01.09.2014; Gunther Eysenbach MD MPH, Associate Professor; JMIR Publications Inc; JMIR Publications
• Subjects: Adult; Ambiguity; Biomedical Research - organization & administration; Biomedical Research - trends; Clinical decision making; Clinical medicine; Cooperative Behavior; Crowdsourcing; Crowdsourcing - trends; Decision making; Defense contracts; Digitization; Discovery; Engineers; Humans; Innovations; Inventions; Knowledge; Knowledge discovery; Medical research; Medicine; Open data; Patients; R&D; Research & development; Software; Tuberculosis; Viewpoint; innovation; hackathon; knowledge discovery; crowdsourcing
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/jmir.3761

Clinicians face difficult treatment decisions in contexts that are not well addressed by available evidence as formulated based on research. The digitization of medicine provides an opportunity for clinicians to collaborate with researchers and data scientists on solutions to previously ambiguous and seemingly insolvable questions. But these groups tend to work in isolated environments, and do not communicate or interact effectively. Clinicians are typically buried in the weeds and exigencies of daily practice such that they do not recognize or act on ways to improve knowledge discovery. Researchers may not be able to identify the gaps in clinical knowledge. For data scientists, the main challenge is discerning what is relevant in a domain that is both unfamiliar and complex. Each type of domain expert can contribute skills unavailable to the other groups. "Health hackathons" and "data marathons", in which diverse participants work together, can leverage the current ready availability of digital data to discover new knowledge. Utilizing the complementary skills and expertise of these talented, but functionally divided groups, innovations are formulated at the systems level. As a result, the knowledge discovery process is simultaneously democratized and improved, real problems are solved, cross-disciplinary collaboration is supported, and innovations are enabled.