Theme 4Clinical workflow, skills assessment and simulation
The workflow of procedures needs to be optimised especially in the increasingly complex operating room where technology and a variety of systems need to be used synergistically. This involves both the measurement and understanding of individual and team factors and competences as well as the real-time use of such information to assist IGT.
Aims and processes
Metrics for assessing both workflow and a range of clinical skills are required not only for training but potentially for verification and detection of adverse events at the point of treatment. Motion analysis has been explored as a method for workflow and skill analysis but this is a very simplified surrogate measure not considering the range of different expertise levels, situational factors and context of the motion. Development and integration of new sensor modalities and link between surgeon, operating theatre team, and various technical systems will be critical to developing a deeper and more meaningful means of guiding best practice and best tech transfer and utilisation.
Relation to other key themes
The theme is aligned with all other themes of the network because it is critical for understanding the optimal use of new technologies for planning, guidance, imaging and therapy delivery.
Alignment with the IGT sector
Despite the recognised importance of training, education, skills development and team management during diagnostic and surgical procedures, currently there is a little quantification of how new technologies impact these human factors. Being able to provide such metrics and information can be an early stage route to evidence for technological importance and potential impact.
- University College London (Clarkson, Hawkes, Ourselin, Stoyanov, Vercauteren)
- King’s College London (Rhode, Rezavi)
- Imperial College London (Bello, Yang, Lee)
- Sheffield University (Taylor)
- Cardiff University (Bordas)
Dr Dan Stoyanov is a Senior Lecturer (Associate Professor) at UCL and Programme Director for the MSc in Robotics and Computation. He received the BEng degree in electronics from King’s College London, UK, in 2001, and the PhD degree in medical image computing from Imperial College London, UK, in 2006.
Dr Stoyanov was a Royal Academy of Engineering Research Fellow between 2009 and 2014 during which time he joined the Centre for Medical Image Computing and the Department of Computer Science, UCL, where he leads the Surgical Robot Vision Group.