The Evolution and Rise of Robotic Health Assistants: The New Human-Machine Frontier of Geriatric Home Care

Abstract

The aim of this chapter is to identify and conceptualize functional elements necessary for successful adoption of robotic technology within geriatric healthcare environments. Using data collected from a series of focus groups conducted with nurse case managers, social service caseworkers, and rural aging outreach providers, this chapter reviews and further proposes human-machine elements that will guide future integration and use of robotic health assistants within geriatric care settings. Identified functional elements were conceptualized within a technology care-based theoretical model addressing three primary tiers of design: (1) core design features and machine intelligence, (2) interactive human-machine capabilities and functions, and (3) required user skills, education, and training. A core principle is the attention placed on addressing user-based technological problems and solutions surrounding human-machine interactions likely to impact the future of work within geriatric care settings. Limiting factors for integrating robotic technology are addressed, including (1) protecting user privacy, (2) generating positive human-machine interactions, and (3) stimulating human-to-machine and machine-to-human learning through user-friendly interface designs. Robotic functions pertaining to artificial intelligence or smart active and passive monitoring are explored in terms of interventions to reduce biopsychosocial risks, including falls, social isolation, and cognitive management, within geriatric care settings. Key to adoption and proper operation of robotic health assistants is a system-wide plan for continuing education, in-service training, and on-the-job skills training of the long-term care workforces. Such education efforts must focus on strategies that support caregiver literacy via the translation, interpretation, and timely use of real-time data. Although these features are key for improving efficiency of care, the quality of care delivery will necessitate sustainable robot-patient interactions while promoting medical adherence and keeping both human and machine errors to a minimum. Future directions and implications concerning the evolving human-machine frontier within geriatric care and adoption of smart robotic health assistants will be provided.