Crowdsourcing Knowledge Discovery and Innovations in Medicine

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.

The hackathon model to spur innovation around global mHealth

“…[1] The challenge of providing quality care to underserved populations in remote rural settings has, in particular, attracted a lot of interest among engineers seeking to provide societal impact through their work. Sana is an organisation hosted at the Institute for Medical Engineering and Science at the Massachusetts Institute of Technology (MIT) that was established out of this interest.…”

The Unintended Consequences of Social Media in Healthcare: New Problems and New Solutions

“…The process is collaborative and involves people from different disciplines, which allows for innovation [40]. Despite these promises, our search of the evidence did not find research claims justifying the accuracy of the diagnoses and treatments established when using online crowdsourcing systems.…”

Pulling It All Together: Envisioning a Data-Driven, Ideal Care System

“…While we realize that individual clinicians function brilliantly in spite of the technical and systems-level obstacles and inefficiencies with which they are faced, we have reached a point of necessity, one recognized by the Institute of Medicine threatening the quality and safety of healthcare, requiring the development of digital tools that facilitate necessary data input and decisions as well as tools that can interact with and incorporate other features of an integrated digitally-based ICS [17]. This will require close interactions and collaborations among health care workers, engineers including software and hardware experts, as well as patients, regulators, policymakers, vendors and hospital business and technical administrators [5].…”

“…Clearly, this will require an overall monitoring and information system that is interoperable, interactive both with its own components and its users, and actively but selectively informative. Future generations of clinicians will receive their education in an environment in which these systems are ubiquitous, selectively modifiable based on inputs such as crowdsourcing, and intrinsic to the tasks at hand, in contrast to the siloed and apparently arbitrarily imposed applications current clinicians may resist and resent [5,8].…”

“…We have previously written on potential components of such a system-e.g. dynamic clinical data mining, closing the loop on ICU data, optimizing the data system itself, crowdsourcing, etc., and will attempt to 'pull it all together' in this chapter, which we hope will inspire and encourage others to think about and move to create such a system [1][2][3][4][5][6][7][8][9][10]. Such a system, in theory, would support clinical workflow by [1] leveraging data to provide both accurate personalized, or 'precision,' care for individuals while ensuring optimal care at a population level; [2] providing coordination and communication among the users of the system; and [3] defining, tracking, and enhancing safety and quality.…”