Towards Effective Patient Simulators

In this paper we give an overview of the field of patient simulators and provide qualitative and quantitative comparison of different modeling and simulation approaches. Simulators can be used to train human caregivers but also to develop and optimize algorithms for clinical decision support applications and test and validate interventions. In this paper we introduce three novel patient simulators with different levels of representational accuracy: HeartPole, a simplistic transparent rule-based system, GraphSim, a graph-based model trained on intensive care data, and Auto-ALS—an adjusted version of an educational software package used for training junior healthcare professionals. We provide a qualitative and quantitative comparison of the previously existing as well as proposed simulators.

Physical patient simulators for surgical training: a review

The purpose of this article is to review the fabrication process of physical patient simulators for surgical training and describes current research areas. Medical image acquisition and analysis are tools to reproduce human anatomy in 3D models. Data acquisition techniques include CT scans, MRI, and ultrasound. Postprocessing of this data is necessary to obtain a file for 3D printing. Two available fabrication methods are direct 3D printing of an organ model and 3D printing a mould to cast an organ replica. Direct 3D printing presents several limitations. Therefore, casting techniques with silicones and hydrogels are better suited for the fabrication of softer tissue models. Surgeons qualitatively evaluate the simulators and their ability to train students. It is also possible to make a quantitative evaluation to compare the properties of the simulators to the physical properties of organs. Different methods exist to measure the physical properties of soft tissues, mainly to find the Young modulus of the soft tissue. The tests can be in vivo, in situ or in vitro. Researchers perform tests on human tissues or animal tissues. The use of surgical simulators has shown satisfactory results in surgical training. Nonetheless, limitations remain, simulators lack realism and are not available for some pathologies. Future work in this area could be of benefit to surgical training.

A posthuman ecology of simulated human patients: Eidolons, empathy and fidelity in the uncanny embodiment of nursing practice

The reproduction of the human form has been a universal practice amongst human ecologies for millennia. Over the past 200 years, popular culture has considered the imaginary consequences of the danger to humanity and human-ness of replicating the autonomous human form too faithfully. Today, the seductive allure of technologically advanced simulated human bodies and advances in robotics and artificial intelligence has brought us closer to facing this possibility. Alongside the simultaneous aversion and fascination of the possibility that autonomous simulated human forms may become indistinguishable from human beings is the deep-rooted uncanniness of the automaton in its strange familiarity – not only to ourselves but to our pleasant childhood imaginings of playing with dolls. As such, simulated human bodies are often enrolled in medical and nursing education models with the assumption that making the simulation teaching spaces seem as close to clinical spaces as possible will allow students to practise potentially harmful clinical skills without causing any harm to human patients. However similar the simulated human bodies may appear to a living, breathing human, a tension between the embodiment of particularly human attributes and their replication persists. How can computerized human patient simulators be enrolled to teach people to develop the necessary attributes of compassion and empathy when caring for human beings? This article explores the uncanny ecologies of simulated human patients in nursing education by presenting a posthuman analysis of the practices of nurse educators as they enrol these digital objects in their teaching. Guided by a selection of heuristics offered as a mode of interviewing digital objects, the analysis enrolled ‘Gathering Anecdotes’ and ‘Unravelling Translations’ to attune to the ways in which these uncanny posthuman assemblages become powerful modes of knowing to mobilize learning about human attributes within uncanny posthuman ecologies.

Abstract TP304: Mimicking Acute Stroke Findings With a Digital Avatar

Introduction: It is now well-accepted that simulation-based learning (SBL) is an important component of medical education. At our institution, we have a state-of-the-art simulation center, and SBL is already incorporated into many medical subspecialties. However, commercially available patient simulators have static faces and lack the realistic depiction of non-verbal facial cues important for rapid diagnosis of neurological emergencies such as stroke. This multidisciplinary project addresses the urgent need for expressive patient simulators by developing acute stroke avatars for use in simulated healthcare training. Methods: Using a previously published and validated method, we developed techniques to display stroke pathologies on a virtual patient simulator (VPS). After obtaining patient or surrogate consent, we obtained source videos from patients admitted to an academic medical center who had experienced acute ischemic stroke resulting in neurological findings such as facial droop, eyelid apraxia, dysarthria, and coma. We then extracted facial features using shape-based modeling techniques, leaving anonymous feature points. Next, we applied a novel algorithm to use these feature points to build accurate computational models (masks) representing the facial characteristics of stroke. We then overlaid these prebuilt masks onto a live facial video stream to generate asymmetric expressions on a virtual avatar. This project was IRB approved. Results: More than 21 videos of stroke patients were made. Once the feature points were extracted from these videos, we were able to develop VPSs capable of expressing realistic asymmetric facial expressions. These avatars were then validated amongst neurologists with clinical experience in diagnosing acute ischemic stroke. Conclusions: This multidisciplinary effort using patient-inspired facial expressions resulted in a tool that aids the stroke education community by making virtual and robotic simulators of acute stroke more varied, interactive, and realistic.