A “datathon” model to support cross-disciplinary collaboration

By J. Aboab, Leo Anthony Celi, P. Charlton, Mengling Feng, Marzyeh Ghassemi, D. Marshall, Louis Mayaud, Tristan Naumann, N. Mccague, Kenneth E Paik and 4 more. 2016

80 citations recorded by the source corpus.

A “datathon” model combines complementary knowledge and skills to formulate inquiries and drive research that addresses information gaps faced by clinicians. In recent years, there has been a growing focus on the unreliability of published biomedical and clinical research. To introduce effective new scientific contributors to the culture of health care, we propose a “datathon” or “hackathon” model in which participants with disparate, but potentially synergistic and complementary, knowledge and skills effectively combine to address questions faced by clinicians. The continuous peer review intrinsically provided by follow-up datathons, which take up prior uncompleted projects, might produce more reliable research, either by providing a different perspective on the study design and methodology or by replication of prior analyses.

Subfields: Biomedical Engineering, Health Informatics, Information Systems and Management