![]() ![]() Surgical data science for next-generation interventions. Vedula, Stefanie Speidel, Nassir Navab, Ron Kikinis, Adrian Park, Matthias Eisenmann, Hubertus Feussner, Germain Forestier, Stamatia Giannarou, Makoto Hashizume, Darko Katic, Hannes Kenngott, Michael Kranzfelder, Anand Malpani, Keno März, Thomas Neumuth, Nicolas Padoy, Carla Pugh, Nicolai Schoch, Danail Stoyanov, Russell Taylor, Martin Wagner, Gregory D. Dmitri Nepogodiev, Janet Martin, Bruce Biccard, Alex Makupe, Aneel Bhangu on behalf of the National Institute for Health Research Global Health Research Unit on Global Surgery. The results of this workshop are currently being compiled for publication. The third workshop in March 2023 focused on validation metrics and was held in Montreal, Canada. The second edition of the workshop took place in June 2019 in Rennes, France, and focused on initiatives, industrial perspectives, and success stories in surgical data science. 1) as well as two white papers that identify, present and discuss key initiatives, potential standards, new results, and challenges in the context of surgical data science. Key results of this first workshop were a common definition of the field (see Fig. The first workshop on Surgical Data Science was inspired by current open space and think tank formats and was organized in June 2016 in Heidelberg, Germany. (Reprinted by permission from Springer Nature Customer Service Centre GmbH: Springer Nature, Nature Biomedical Engineering Surgical data science for next-generation interventions. ![]() Improvement may result from understanding processes and strategies, predicting events and clinical outcome, assisting physicians in decision-making and planning execution, optimizing the ergonomics of systems, controlling devices before, during and after treatment, and from advances in prevention, training, simulation and assessment. Data may pertain to any part of the patient-care process and are analysed in the context of generic domain-specific knowledge derived from existing evidence, clinical guidelines, current practice patterns, caregiver experience and patient preferences. It encompasses all clinical disciplines in which patient care requires intervention to manipulate anatomical structures with a diagnostic, prognostic or therapeutic goal. 1: Surgical data science aims to improve the quality of interventional healthcare and its value through the capture, organization, analysis and modelling of data. Why? We believe that it’s because they’ve failed to address a huge underlying obstacle: the day-to-day routines and rituals that stifle innovation.Fig. Across industries, one survey after another has found the same thing: Businesses just aren’t getting the impact they want, despite all their spending. Yet according to a McKinsey survey, 94% of executives are dissatisfied with their firms’ innovation performance. To catalyze innovation, companies have invested billions in internal venture capital, incubators, accelerators, and field trips to Silicon Valley. Engagement scores rose 20%, and the center was named a great place to innovate. The bank DBS used this approach to unleash innovation at a tech-development center. Leadership needs to identify these innovation blockers and neutralize them with interventions called “BEANs”-behavior enablers, artifacts, and nudges. They also explain how any organization can go about creating its own BEANs by identifying the creative behaviors it wants, examining what’s getting in the way, and then brainstorming ways to bust those bad habits.Ĭompanies’ investments in innovation are stymied by the day-to-day routines and habits that stifle original thinking. In this article the authors describe a variety of BEANs that the bank DBS, the Tata Group, and other companies have devised to unleash innovation. Nudges promote it through indirect suggestion and reinforcement. Artifacts, which you can see or touch, support the new behavior. Behavior enablers are tools or processes that make it easier for people to do something differently. ![]() These include such things as poorly run meetings, no slack capacity, few opportunities to speak up, and the notion that doing things differently is inefficient and costly.įortunately, it’s possible to hack this problem, using interventions called BEANs, combinations of behavioral enablers, artifacts, and nudges that break down the innovation blockers. Why? Because firms fail to address one major obstacle: the day-to-day habits and routines that regularly stifle innovation. Yet survey after survey indicates these efforts aren’t producing results. To spur innovation, businesses have spent billions on internal venture capital, incubators, and accelerators. ![]()
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