Surgical data science¹  is an emerging scientific discipline with the objective of improving the safety, quality, effectiveness, and efficiency of surgical care by means of data acquisition, modeling, and analysis. Improvement may come from understanding of processes and strategies, assisting surgeons and controlling devices before, during and after surgery as well as from improvements to training, simulation and assessment. Surgical data science builds on principles and methods from other data-intensive disciplines such as computer science, engineering, information theory, statistics, mathematics, and epidemiology, and complements other information-enabled technologies such as surgical robotics, smart operating rooms, and electronic patient records.

The goal of this workshop was to bring together researchers working on diverse topics in surgical data science in order to discuss existing challenges, potential standards and new research directions in the field. It took place at the German Cancer Research Center (DKFZ) in Heidelberg, Germany.

On the workshop day, keynote lectures by leading experts in the field were complemented by short presentations of accepted workshop papers.

¹) Acknowledgement: The title ‘surgical data science’ was suggested by Swaroop Vedula (Computational Interaction and Robotics Laboratory (Prof. Hager), Johns Hopkins University).