Iris: A Low-Cost Telemedicine Robot to Support Healthcare Safety and Equity During a Pandemic

Matsumoto, S., Moharana, S., Devanagondi, N., Oyama, L., and Riek, L.D., Proceedings of the 15th Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth 2021) (2021).
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Abstract

The COVID-19 pandemic exacerbated problems of already overwhelmed healthcare ecosystems. The pandemic worsened long-standing health dispari- ties and increased stress and risk of infection for frontline healthcare workers (HCWs). Telemedical robots offer great potential to both improve HCW safety and patient access to high-quality care, however, most of these systems are pro- hibitively expensive for under-resourced healthcare organizations, and difficult to use. In this paper, we introduce Iris, a low-cost, open hardware/open software telemedical robot platform. We co-designed Iris with front-line HCWs to be usable, accessible, robust, and well-situated within the emergency medicine (EM) ecosystem. We tested Iris with 15 EM physicians, who reported high usability, and provided detailed feedback critical to situating the robot within a range of EM care delivery contexts, including under-resourced ones. Based on these find- ings, we present a series of concrete design suggestions for those interested in building and deploying similar systems. We hope this will inspire future work both in the current pandemic and beyond.