Student understandings of evidence-based management : ways of doing and being

This paper advances the literature on Evidence Based Management (EBMgt) by exploring how students understand EBMgt. We conduct a qualitative inductive study of undergraduate students who were introduced to EBMgt and applied evidence-based processes as part of an introductory management course. Our findings identify four qualitatively different student understandings of EBMgt: (1) EBMgt as an unrealistic way of doing management; (2) EBMgt as a way of doing management in particular situations; (3) EBMgt as a generally useful way of doing management; and (4) EBMgt as an ideal way of being a manager. We find that variations in student understanding are based upon perceptions of the utility of evidence-based processes, the stance taken towards scientific evidence as a form of knowledge, and the focus of reflection about the practice of EBMgt. By opening up insight into the how undergraduate students understand and make sense of EBMgt as ways of doing and being, we contribute to the theoretical literature on EBMgt and to the practice of EBMgt teaching and learning and offer new paths for future research.PostprintPeer reviewe

Being a professional: Three perspectives on design thinking, acting, and being

The purpose of this paper is to present three perspectives for interpreting design thinking: (1) an alternative framework on learning to become a professional, and (2) two interpretations of this framework that speak broadly to a topic of “design thinking”. The first perspective draws on a framework for “an embodied understanding of professional practice” that focuses on the ways professionals form and organize their knowledge and skills into a particular “professional-way-of-being”. The second and third perspectives provide examples of using this framework as a lens for interpreting existing results from phenomenographic studies on ways of experiencing design and ways of experiencing cross-disciplinary practice. We conclude with a discussion of how these three perspectives contribute to conceptualizing a working synthesis of design thinking

Challenges in multi-centric generalization: phase and step recognition in Roux-en-Y gastric bypass surgery.

PURPOSE Most studies on surgical activity recognition utilizing artificial intelligence (AI) have focused mainly on recognizing one type of activity from small and mono-centric surgical video datasets. It remains speculative whether those models would generalize to other centers. METHODS In this work, we introduce a large multi-centric multi-activity dataset consisting of 140 surgical videos (MultiBypass140) of laparoscopic Roux-en-Y gastric bypass (LRYGB) surgeries performed at two medical centers, i.e., the University Hospital of Strasbourg, France (StrasBypass70) and Inselspital, Bern University Hospital, Switzerland (BernBypass70). The dataset has been fully annotated with phases and steps by two board-certified surgeons. Furthermore, we assess the generalizability and benchmark different deep learning models for the task of phase and step recognition in 7 experimental studies: (1) Training and evaluation on BernBypass70; (2) Training and evaluation on StrasBypass70; (3) Training and evaluation on the joint MultiBypass140 dataset; (4) Training on BernBypass70, evaluation on StrasBypass70; (5) Training on StrasBypass70, evaluation on BernBypass70; Training on MultiBypass140, (6) evaluation on BernBypass70 and (7) evaluation on StrasBypass70. RESULTS The model's performance is markedly influenced by the training data. The worst results were obtained in experiments (4) and (5) confirming the limited generalization capabilities of models trained on mono-centric data. The use of multi-centric training data, experiments (6) and (7), improves the generalization capabilities of the models, bringing them beyond the level of independent mono-centric training and validation (experiments (1) and (2)). CONCLUSION MultiBypass140 shows considerable variation in surgical technique and workflow of LRYGB procedures between centers. Therefore, generalization experiments demonstrate a remarkable difference in model performance. These results highlight the importance of multi-centric datasets for AI model generalization to account for variance in surgical technique and workflows. The dataset and code are publicly available at https://github.com/CAMMA-public/MultiBypass140

Autonomous Needle Manipulation for Robotic Surgical Suturing Based on Skills Learned from Demonstration

In the future, surgical robots will grant the option of executing surgical tasks autonomously, supervised by the surgeon. We propose a simple framework for learning surgical action primitives that can be used as building blocks for composing more elaborate surgical tasks. Our method is based on Learning from Demonstration (LfD) as this allows us to exploit existing expert knowledge from recordings of surgical procedures. We demonstrate that we can learn needle manipulation actions from human demonstration, constructing an action library which is used to autonomously execute part of a surgical suturing task. Actions are learned from single demonstrations and we use Dynamic Movement Primitives (DMPs) to encode low-level Cartesian space trajectories. Our method is experimentally validated in a non-clinical setting, where we show that learned actions can be generalized to previously unseen conditions. Experiments show a 81 % task success rate for moderate variations from the initial conditions of the demonstration with a mean needle insertion error of 3.8 mm

A phantom study for the validation of a Surgical Navigation System Based on Real-Time Segmentation and Registration Methods

Purpose The surgical treatment of abdominal tumors (i.e. pancreas, liver, kidney) relies on precise localization of the lesions and detailed knowledge of the patient-specific anatomy especially when a minimally invasive technique is performed. Such procedures may be improved by using a navigation system that helps the physician to identify and visualize these anatomical structures. Methods In-vitro studies were performed on custom developed phantoms that mimic both the geometric properties of the human organs, that is shape and internal structures, and the radiologic properties of the human tissue under two different imaging systems: ultrasound (US) and computed tomography (CT). Real-time US tracked, segmented and 3D reconstructed data are mapped to the pre-operative 3D model, through a fast registration procedure, assuming a locally rigid and temporarily static scenario. The segmentation process identifies the external surface of the organ and its internal structures such as duct and cysts as unstructured point clouds. By registering with the pre-operative data we have obtained from one side the deletion of the outliers in the US image and, on the other side, the update of the position, orientation and dimension of the internal structures in the CT data. Since the input data for the registration is unstructured, we have chosen a principal axes alignment algorithm based on the statistical distribution of the 3D points modeled as a mass distribution. The segmentation and registration algorithms have a straightforward interpretation, they are fast and fully automatic. Results In the validation procedure we have investigated if the accuracy and the processing time are suitable with the requirements of a navigation system for the surgical room. Ground truth was obtained using the realistic phantoms. The real-time processing of the intra-operative data was obtained by implementing the segmentation algorithm using the GPU for parallel computation. The lesions, which are the target regions in most of the clinical cases, are usually of small dimensions, compact and most of the time symmetric. Given the characteristics of these structures, the principal axes registration algorithm is not applicable, therefore the registration was used only on the external surface of the organ. We have measured the registration error both globally, considering only the external surface, and locally, on the target areas. Conclusion The use of realistic phantoms offers the possibility to identify and tune the best methods of segmentation and registration for operating room applications as well as the possibility to validate these methods. The presented framework proved to be versatile and may be applicable to a wide range of procedures. The use of registration between pre-operative and interventional data could lead to precise delineation of the tumor and therefore could increase the chances of recovery after surgery with a reduced number of side effects. The emphasis was on the real-time acquisition and processing. One limit of almost all of these procedures available in literature was the incompatibility with the real-time constraint imposed by our setup. Another limits of all the multi-modal registration solutions is the difficulty of finding a distance measure and the risk of getting stuck in a local minimum in the optimization process. The real-time solution we have chosen, based on the principal axes alignment, makes the assumption that the deformations are small because the distribution of the surface points is similar between two subsequent real-time acquisitions