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The coronavirus disease 2019 (COVID-19) pandemic has strained health care systems and personal protective equipment (PPE) supplies globally. We hypothesized that a collaborative robot system could perform health care worker effector tasks inside a simulated intensive care unit (ICU) patient room, which could theoretically reduce both PPE use and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposures. We planned a prospective proof-of-concept feasibility and design pilot study to test 5 discrete medical tasks in a simulated ICU room of a COVID-19 patient using a collaborative robot: push a button on intravenous pole machine when alert occurs for downstream occlusion, adjust ventilator knob, push button on ICU monitor to silence false alerts, increase oxygen flow on wall-mounted flow meter to allow the patient to walk to the bathroom and back (dial-up and dial-down oxygen flow), and push wallmounted nurse call button. Feasibility was defined as task completion robotically. A training period of 45 minutes to 1 hour was needed to program the system de novo for each task. In less than 30 days, the team completed 5 simple effector task experiments robotically. Selected collaborative robotic effector tasks appear feasible in a simulated ICU room of the COVID-19 patient. Theoretically, this robotic approach could reduce PPE use and staff SARS-CoV-2 exposure. It requires future validation and health care worker learning similar to other ICU device training.

Introduction: Three-dimensional (3D) printing of anatomical structures is a growing method of education for students and medical trainees. These models are generally produced as static representations of gross surface anatomy. In order to create a model that provides educators with a tool for demonstration of kinematic and physiologic concepts in addition to surface anatomy, a high-resolution segmentation and 3D-printingtechnique was investigated for the creation of a dynamic educational model. Methods: An anonymized computed tomography scan of the cervical spine with a diagnosis of ossification of the posterior longitudinal ligament was acquired. Using a high-resolution thresholding technique, the individual facet and intervertebral spaces were separated, and models of the C3-7 vertebrae were 3D-printed. The models were placed on a myelography simulator and subjected to flexion and extension under fluoroscopy, and measurements of the spinal canal diameter were recorded and compared to in-vivo measurements. The flexible 3D-printed model was then compared to a static 3D-printed model to determine the educational benefit of demonstrating physiologic concepts. Results: The canal diameter changes on the flexible 3D-printed model accurately reflected in-vivo measurements during dynamic positioning. The flexible model also was also more successful in teaching the physiologic concepts of spinal canal changes during flexion and extension than the static 3D-printed model to a cohort of learners. Conclusions: Dynamic 3D-printed models can provide educators with a cost-effective and novel educational tool for not just instruction of surface anatomy, but also physiologic concepts through 3D ex-vivo modeling of case-specific physiologic and pathologic conditions.

Otoscopy is a simple, yet fundamental tool for medical practitioners of all levels to diagnose common otologic conditions. Otoscopy is traditionally performed by a handheld light with a lens. This technique has several disadvantages, especially during teaching sessions since only a first-person view is available. Video otoscopy has the ability to project the view of the scope onto a screen that can be displayed for medical or patient education. Recently, handheld video otoscopy has advanced to display compatibility with personal devices such as cell phones or tablets. In this technical report, we demonstrate components, setup, and use of video otoscopy for otologic examination that can be easily used on a personal electronic device.