A "datathon" model to support cross-disciplinary collaboration

Abstract

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.

Fig. 1. Sample snapshots

Shown are screen shots of the Jupyter Notebooks used in the Second International Critical Care datathon to facilitate sharing of queries and codes among the teams. (A) A custom Python package was developed to assist with transforming and analyzing data. (B) Notebooks simplified data exploration. (C) Data visualization.