Human-Robot Collaboration for Healthcare: Sociotechnical Insights on Development and Integration

Abstract

This thesis examines human–robot collaboration (HRC) in healthcare through a sociotechnical lens. It positions robots not as isolated technologies, but as compo nents of complex clinical systems shaped by human actors, work routines, infras tructures, organisational arrangements, and regulatory contexts. Although HRC is often framed as a response to workforce shortages, many deployments fail to achieve effective, acceptable, and sustained integration. To better understand why HRC deployments struggle in practice and how these challenges can be addressed, this research is structured around three interrelated questions. First, it explores how HRC technologies are developed and introduced into healthcare settings. Second, it examines the factors that influence stakeholder acceptance of HRC technologies in healthcare contexts. Third, it considers how knowledge gained from these sociotechnical processes can be translated into practical guidance for the design and integration of HRC technologies in healthcare. Guided by a pragmatic paradigm, the thesis adopts a mixed-methods approach within a Design Science Research Methodology. It combines qualitative and quan titative approaches, including interviews with experts in healthcare technology, a hospital-based quantitative acceptance study, and two in-depth qualitative case studies. Findings from the first two studies are synthesised into the STEER-H framework (Sociotechnical Engineering for Effective Robotics in Healthcare). The framework is then demonstrated and refined through two contrasting case studies: a prospective HRC design for a hospital rapid response system and a retrospective examination of an established rehabilitation robot in routine clinical use. The expert interviews show that introducing HRC into care settings is shaped by a tightly interwoven stakeholder ecosystem, the need for context-aware design that fits work-as-done, and broader constraints including regulation, funding, and pro fessional norms. The quantitative acceptance study indicates that acceptance is influenced by contextual factors such as trust, perceived risk, and ethical concerns, reflecting the high-stakes and accountability demands of clinical work. Overall, the findings suggest that effective HRC depends on alignment across multiple di mensions identified in STEER-H — stakeholder, institutional, and ecosystem. The framework provides an analytic lens for anticipating integration challenges, structur ing design and implementation work, and identifying where targeted interventions are required. The thesis contributes the STEER-H sociotechnical framework, grounded in health care robotics literature, expert perspectives, and empirically identified acceptance determinants; an empirically tested acceptance model that estimates the effects of trust, perceived risk, and ethical concerns alongside functional and workflow-related factors; and an in situ evidence base spanning two distinct clinical contexts and stages of adoption. In sum, this thesis advances a sociotechnical foundation for embedding HRC within complex healthcare systems.