Crowdsourcing Knowledge Discovery and Innovations in Medicine

Read our publication in JMIR, “Crowdsourcing Knowledge Discovery and Innovations in Medicine

Leo Anthony Celi1,2*, MD, MPH, MS; Andrea Ippolito3*, MS, MEng; Robert A Montgomery4*, MD; Christopher Moses5*, BS; David J Stone6*, MD

1Institute for Medical Engineering and Science, Laboratory of Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States
2Beth Israel Deaconess Medical Center, Division of Pulmonary, Critical Care and Sleep Medicine, Harvard Medical School, Boston, MA, United States
3Engineering Systems Division, Massachusetts Institute of Technology, Cambridge, MA, United States
4Beth Israel Deaconess Medical Center, Department of Medicine, Harvard Medical School, Boston, MA, United States
5Smart Scheduling, Inc., Cambridge, MA, United States
6UVA Center for Wireless Health, Departments of Anesthesiology and Neurosurgery, University of Virginia School of Medicine, Charlottesville, VA, United States
*all authors contributed equally

ABSTRACT
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.