Editorial: Bridging the gap: understanding adoption and acceptance of emerging healthcare technologies

Introduction

The healthcare sector is undergoing a profound technological transformation. From mobile health (mHealth) applications and artificial intelligence (AI)-powered chatbots to intelligent customer service systems and real-time motion capture tools, the pace of innovation in medical technology far outstrips the pace of its adoption. Yet the potential of these advances to improve patient outcomes, expand access to care, and reduce systemic burdens can only be realized when technologies are genuinely accepted and used by those for whom they are designed—patients, providers, and caregivers alike. Understanding the behavioral, psychological, organizational, and social determinants of healthcare technology adoption has thus become a critical imperative for researchers, practitioners, and policymakers (Davis, 1989; Venkatesh et al., 2003).

This Research Topic, “Bridging the Gap: Understanding Adoption and Acceptance of Emerging Healthcare Technologies,” was launched in response to that imperative. The six published articles represent a rich and methodologically diverse body of work spanning multiple technology types, user populations, geographic and cultural contexts, and theoretical frameworks. Collectively, they advance understanding of the multidimensional nature of healthcare technology adoption and generate actionable insights for the design, deployment, and governance of digital health tools.

Theoretical foundations and methodological approaches

The Technology Acceptance Model (TAM; Davis, 1989) and its successors including the Unified Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003) and UTAUT2 (Venkatesh et al., 2012), provide the dominant theoretical scaffolding across this Research Topic. Several contributions extend or adapt these frameworks by integrating complementary theories. The Health Belief Model (Rosenstock, 1974), Social Exchange Theory, and the Stressor-Strain-Outcome (SSO) model are each employed to capture context-specific adoption dynamics that standard acceptance frameworks may not fully address. Methodologically, the Research Topic is anchored in quantitative survey research analyzed using structural equation modeling (SEM), partial least squares SEM (PLS-SEM), and mixed methods, reflecting both rigor and breadth in the study of technology acceptance.

Provider-facing acceptance: mHealth in pharmaceutical care

Adoption by healthcare providers represents one of the most consequential yet underexplored frontiers of health technology research. AlGhannam et al. address this gap by examining pharmacists’ acceptance of mHealth applications within Kuwait’s government health sector using the mobile TAM (m-TAM). Their cross-sectional survey reveals that Compatibility, Performance Expectancy, Personal Innovativeness, and Effort Expectancy are statistically significant predictors of behavioral intention, with Performance Expectancy serving as a critical mediator linking both Compatibility and Effort Expectancy to adoption intention. Importantly, the authors uncover a chain of mediating effects connecting job-related mental demands, professional identity, and perceived social support to behavioral intention, underscoring the multifaceted nature of provider acceptance. These findings have direct implications for app developers and health ministry policymakers seeking to scale mHealth deployment in resource-constrained environments.

Patient and user-facing adoption: older adults and online communities

Two articles illuminate technology adoption among patients and the general public, demonstrating how health status, demographic characteristics, and social context shape digital health engagement. Huang et al. investigate older adults’ adoption of online medical services (OMS) in China using a modified UTAUT2 model enriched with technology anxiety, trust, and perceived risk constructs. Drawing on 379 valid questionnaires analyzed via SEM, they find that performance expectancy, effort expectancy, social influence, and trust are positive drivers of OMS adoption, while technology anxiety and perceived risk are significant barriers. Notably, facilitating conditions and price value show no significant effect, challenging assumptions frequently imported from mainstream consumer technology research. Moderating effects of age, education level, and health status further underscore heterogeneity within the older adult population, with significant implications for health system design as global populations age.

Boyce et al. extend the adoption lens to online health communities by examining information-seeking behavior among patients with Chronic Obstructive Pulmonary Disease (COPD) using Facebook groups. Integrating the Health Belief Model, Social Exchange Theory, and TAM within a PLS-SEM framework (N = 155), the study demonstrates that ease of internet use and perceived utility are significant determinants of active participation, while emotional support and self-worth perceptions play meaningful complementary roles. Their findings affirm the value of online peer communities as a distinct and underappreciated modality of health technology adoption—one with practical implications for both platform design and clinical communication.

Emerging AI and intelligent systems: ambivalence, overload, and avoidance

Two articles tackle AI-driven healthcare technologies from complementary angles—one examining public perception, the other examining system-side failures. Russell and Bode employ a mixed-methods analysis of high-engagement public discourse on X (formerly Twitter) from early 2025 (N = 496 posts, English and Spanish) to examine sentiment toward both traditional mental health providers and AI alternatives. Applying a pioneering UTAUT lens, they identify a central paradox: public discourse reflects deep frustration with traditional care, primarily citing prohibitive costs and perceived inefficacy, while AI is valued not as a therapeutic equivalent but as a pragmatic tool offering accessibility, anonymity, and scalability. Yet AI is simultaneously critiqued for its inability to replicate human empathy. Spanish-language discourse particularly highlights how AI’s anonymity functions as a countermeasure to cultural stigma surrounding mental health. The authors argue that AI in mental health is being adopted to patch systemic failures rather than driven by technological merit—a finding with significant ethical and policy implications for the design of hybrid human-AI care systems.

Jia et al. approach AI acceptance from the service-delivery side by examining user avoidance behavior toward Intelligent Customer Service (ICS) in pharmaceutical e-commerce through a Stressor-Strain-Outcome framework. Using SmartPLS with 418 valid responses, they demonstrate that system overload, information overload, and service overload amplify user emotional stress, which in turn drives avoidance behavior. With strong model explanatory power (R² = 0.450–0.586), the study reveals a mechanism often overlooked in adoption research: that poorly designed intelligent systems do not merely fail to attract users—they actively repel them—a dynamic with downstream implications for medication adherence and patient safety in digital pharmacy environments.

Novel technology for clinical and performance assessment

Rounding out the Research Topic, Ji et al. present a pioneering application of real-time motion-tracking and ergonomic analysis tools to assess injury risk in competitive archery, marking the first such utilization of this technology in archery research. Thirteen participants performed tasks with varying bow draw weights and target positions, yielding precise recordings of human movement that revealed gender-differentiated postural patterns and quantified spinal force exposure. While distinct in its clinical engineering orientation, this study is thematically aligned with the Research Topic’s core concern: the role of emerging technologies in improving healthcare-relevant outcomes beyond traditional clinical settings, and the value of non-invasive, real-time sensing as a scalable approach to injury prevention and occupational health.

Cross-cutting themes and future directions

Across these six contributions, several cross-cutting themes emerge. First, context shapes adoption profoundly: drivers and barriers vary substantially by population (providers vs. patients; younger vs. older adults), culture (Kuwait, China, United States), and technology type. Second, negative adoption behaviors, including avoidance, resistance, and ambivalence, deserve as much scholarly attention as positive adoption intentions; the field’s historical emphasis on behavioral intention to use risks undertheorizing the mechanisms of non-adoption and technology rejection. Third, standard acceptance frameworks, while powerful, require contextual augmentation; trust, anxiety, cognitive overload, stigma, and emotional support are recurring constructs that enrich models originally developed for productivity-oriented, non-clinical settings. Finally, design implications consistently surface: from reducing cognitive burdens in intelligent customer service to building intuitive platforms for chronic disease communities, the evidence points toward user-centered design as a critical lever for closing the adoption gap.

Future research should continue to examine adoption dynamics across the full healthcare ecosystem, spanning patients, caregivers, clinicians, administrators, and policymakers. Longitudinal studies capturing actual use behavior rather than intention alone, cross-cultural comparative designs, and hybrid methodologies triangulating quantitative models with qualitative insights will be especially valuable. As generative AI, wearable biosensors, ambient care platforms, and intelligent clinical decision support tools continue to proliferate, the questions posed by this Research Topic will only grow more urgent.

Conclusion

The articles assembled in this Research Topic collectively advance a nuanced, evidence-based understanding of what it takes to bridge the gap between the availability of healthcare technology and its genuine adoption. We are grateful to the contributing authors, peer reviewers, and the editorial team at Frontiers for their commitment to this Research Topic. We hope these works serve both as a scholarly resource and as a call to action for healthcare technology designers, system implementers, and policy architects working to ensure that innovation translates into meaningful, equitable, and continuous improvements in care.

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Footnotes

Edited and reviewed by: Chiara Tonda-Turo, Polytechnic University of Turin, Italy