Mobile robotic systems in patient-facing functions: national acceptability survey, single site feasibility study and cost-effectiveness analysis
Authors: Chai Peter R , Dadabhoy Farah Z , Huang Hen-wei , Chu Jacqueline N , Feng Annie , Le Hien M , Collins Joy , Silva Marco da , Raibert Marc , Hur Chin , Boyer Edward W , Traverso Giovanni 
Published: October 2020

AbstractObjectiveTo understand the acceptability of patient-facing mobile robotic systems on a national scale, conduct a pilot feasibility study to deploy and measure satisfaction associated with clinical evaluation using a mobile telehealth robot in the emergency department (ED) and to build a decision analytic model to gauge the potential of a robotic system to prevent COVID-19 infections and conserve personal protective equipment in the ED.DesignMixed study comprising an online sampling-based survey, single-site observational clinical trial and development of a decision analytic model.SettingA quaternary care, urban, academic, emergency department in Boston, Massachusetts, USA.ParticipantsFor the acceptability survey, we recruited N=1000 individuals living in the United States participating in an online sampling from the survey provider YouGov. In the ED study, we enrolled 40 individuals over 18 years old presenting to the ED for evaluation.InterventionsIn the pilot ED study, consenting participants were exposed to a mobile robotic system facilitated triage interview controlled by an emergency medicine clinician. Afterwards, participants completed a survey to measure their satisfaction with the robotic system.Main outcome measuresAcceptability of mobile robot facilitated tasks in healthcare (national survey), satisfaction with interaction of a robotic system (ED study), number of potential SARS-CoV-2 infections avoided and cost savings (US dollars) per year per ED (decision analytic model).ResultsIn the national survey, participants rated the use of robotics for a variety of patient-facing healthcare functions useful or very useful. The perceived usefulness increased when asked to consider these functions in the context of the COVID-19 pandemic. In the ED, 40 patients completed study procedures; 92.5% (N=37) reported satisfaction with the robotic system. Most participants (82.5%, N=33) reported their experience being evaluated by a robotic system was as good as an in-person encounter. Our decision analytic model estimated that robotic evaluations could prevent 2.68 infections per ED yearly and save $1 million annually per ED by decreasing PPE and additional staffing in a triage space.ConclusionsRobotic systems were broadly acceptable across the US and their acceptance increased in the setting of COVID-19. Mobile robotic-enabled teleheath facilitated contactless evaluation of ED patients and was highly acceptable and equivalent to an in-person history. Robotic platforms may prevent healthcare-associated COVID-19 transmission to healthcare workers and have a significant cost savings if widely implemented among healthcare systems.Trial RegistrationClinicaltrials.gov,NCT04452695Summary BoxWhat is already known on the topicMobile robots can provide telemedicine services that enable remote evaluation of patients in various settings.The use of mobile systems may facilitate contactless evaluation of patients while minimizing use of personal protective equipment (PPE) during the COVID-19 pandemic.What this study addsOur study suggests that there is broad acceptability of mobile robotic systems to perform patient-facing tasks in the hospital. This sentiment is reflected in satisfaction of patients in the emergency department interacting with a mobile robotic teletriage system during our open label pilot study.Our decision analysis model suggests that adoption of robotic systems for teletriage in the emergency department may prevent up to 2.68 SARS-CoV-2 infections among healthcare workers over one year per emergency department in the United States as well as a cost savings in deploying a robotic system compared to a clinician to evaluate patients during the COVID-19 pandemic.