Why Patients and Doctors Refuse New Health Tech: The Science of Adoption Gap

Medicine is hurtling through one of the most extraordinary technological transformations in its history. Mobile health applications now sit in billions of pockets, artificial intelligence chatbots answer patient questions around the clock, intelligent customer service systems triage pharmacy inquiries, and real-time motion capture tools can quantify the biomechanical forces acting on an athlete’s spine with millimeter precision. Yet a striking paradox defines the field: the pace of innovation in medical technology dramatically outstrips the pace at which those technologies are actually adopted and used. A brilliant app that no pharmacist opens, an AI service that patients actively avoid, or a telemedicine platform that older adults never log into delivers no clinical benefit whatsoever. The potential of digital health to improve outcomes, expand access, and reduce systemic burdens can only be realized when technologies are genuinely accepted by the patients, providers, and caregivers for whom they are designed.

That adoption gap is the focus of a Research Topic published in Frontiers in Medical Engineering titled Bridging the Gap: Understanding Adoption and Acceptance of Emerging Healthcare Technologies, introduced by an editorial from Xianghui Peng of Penn State Erie and Victor Prybutok of the University of North Texas. Six studies, spanning Kuwait, China, and the United States and covering everything from pharmacist-facing mHealth applications to Facebook support groups for chronic lung disease, collectively map the behavioral, psychological, organizational, and social determinants that decide whether a healthcare technology lives or dies in the real world. Their combined message is sobering and empowering at once: acceptance is not a switch that flips when a product launches, but a multidimensional process shaped by trust, anxiety, cultural stigma, cognitive load, and social context.

The theoretical scaffolding for most of this work descends from the Technology Acceptance Model, originally formulated by F. D. Davis in 1989, and its successors the Unified Theory of Acceptance and Use of Technology and the consumer-oriented UTAUT2. These frameworks model adoption as a function of constructs such as performance expectancy, the belief that a technology will help the user accomplish something valuable, and effort expectancy, the perceived ease of use. What distinguishes the new research is how often those standard models had to be augmented with context-specific constructs drawn from complementary theories, including the Health Belief Model, Social Exchange Theory, and the Stressor-Strain-Outcome framework. Methodologically, the studies lean on quantitative survey research analyzed with structural equation modeling and its partial least squares variant, a statistical technique that tests networks of hypothesized causal pathways between latent psychological constructs simultaneously rather than one relationship at a time.

On the provider side, AlGhannam and colleagues examined pharmacists’ acceptance of mobile health applications within Kuwait’s government health sector using the mobile-specific extension of the Technology Acceptance Model. Their cross-sectional survey found that compatibility with existing workflows, performance expectancy, personal innovativeness, and effort expectancy all significantly predicted behavioral intention to use mHealth tools. Crucially, performance expectancy acted as a critical mediator, a statistical pathway through which compatibility and ease of use exerted their influence, meaning pharmacists embraced apps primarily when those apps demonstrably made their professional work better. The team also uncovered a chain of mediating effects linking job-related mental demands, professional identity, and perceived social support to adoption intention, underscoring that a clinician’s decision to embrace a new tool is embedded in psychological and social circumstances far beyond the app’s interface. For ministries of health attempting to scale mHealth in resource-constrained environments, these findings suggest deployment strategies must attend to workload and professional identity, not merely technical training.

Patient-facing studies reveal equally complex dynamics. Huang and colleagues investigated older adults’ adoption of online medical services in China using a modified UTAUT2 model enriched with technology anxiety, trust, and perceived risk. Drawing on 379 valid questionnaires analyzed through structural equation modeling, they found that performance expectancy, effort expectancy, social influence, and trust were positive drivers of adoption, while technology anxiety and perceived risk acted as significant barriers. Perhaps most surprising, facilitating conditions, meaning the availability of support and resources, and price value showed no significant effect at all, challenging assumptions routinely imported from mainstream consumer technology research where cost and convenience loom large. Moderating analyses showed that age, education level, and health status all reshaped these relationships, a reminder that older adults are not a monolithic bloc. As populations age worldwide, designing digital health systems that genuinely serve this heterogeneous group may require abandoning one-size-fits-all assumptions entirely.

A second patient-centered study by Boyce and colleagues widened the lens to online health communities, examining information-seeking behavior among patients with Chronic Obstructive Pulmonary Disease who use Facebook groups. Integrating the Health Belief Model, Social Exchange Theory, and the Technology Acceptance Model within a partial least squares structural equation modeling framework with 155 participants, the study demonstrated that ease of internet use and perceived utility were significant determinants of active participation, while emotional support and perceptions of self-worth played meaningful complementary roles. This positions online peer communities as a distinct and historically underappreciated modality of health technology adoption, one whose value flows as much from human connection as from information exchange. The implications cut both ways: platform designers can engineer for lower friction and richer reciprocity, while clinicians can treat these communities as legitimate extensions of the care ecosystem rather than digital curiosities.

Artificial intelligence introduces its own ambivalence. Russell and Bode conducted a mixed-methods analysis of high-engagement public discourse on X, formerly Twitter, from early 2025, analyzing 496 posts in English and Spanish through a pioneering application of the UTAUT lens. They identified a central paradox: public discourse reflects deep frustration with traditional mental health care, 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. Simultaneously, AI is critiqued for its inability to replicate human empathy. Spanish-language discourse particularly highlighted how AI’s anonymity functions as a countermeasure to cultural stigma surrounding mental health, suggesting acceptance mechanisms can be deeply culture-specific. The authors’ most provocative conclusion is that AI in mental health is being adopted to patch systemic failures rather than because of its technological merit, an insight with profound ethical and policy weight for anyone designing hybrid human-AI care systems.

On the system side, Jia and colleagues studied user avoidance of intelligent customer service in pharmaceutical e-commerce through the Stressor-Strain-Outcome framework, using SmartPLS to analyze 418 valid responses. Their model showed that system overload, information overload, and service overload amplify users’ emotional stress, which in turn drives avoidance behavior, with strong explanatory power indicated by R-squared values ranging from 0.450 to 0.586. The finding exposes a mechanism often overlooked in adoption research: poorly designed intelligent systems do not merely fail to attract users, they actively repel them. In a digital pharmacy context, where an avoided chatbot may mean an unanswered question about dosing or drug interactions, this dynamic carries downstream consequences for medication adherence and patient safety. Adoption research, in other words, needs a science of rejection as rigorous as its science of intention.

Rounding out the collection, Ji and colleagues applied real-time motion-tracking and ergonomic analysis to competitive archery, the first such use of the technology in that sport. Thirteen participants performed tasks with varying bow draw weights and target positions, generating precise recordings of human movement that revealed gender-differentiated postural patterns and quantified spinal force exposure. Though distinct in its clinical engineering orientation, the study aligns thematically with the collection’s core concern: emerging technologies improving health-relevant outcomes beyond traditional clinical settings, and the promise of non-invasive, real-time sensing as a scalable approach to injury prevention and occupational health.

Across all six contributions, cross-cutting lessons crystallize. Context shapes adoption profoundly, with drivers and barriers varying by population, culture, and technology type. Negative behaviors, including avoidance, resistance, and ambivalence, deserve as much scholarly attention as positive intentions, since the field’s historical emphasis on behavioral intention risks undertheorizing non-adoption. Standard acceptance frameworks remain powerful but demand contextual augmentation with constructs like trust, anxiety, cognitive overload, stigma, and emotional support that were absent from the productivity-oriented settings where those models were born. And consistently, the evidence points toward user-centered design as the critical lever for closing the gap, whether by reducing cognitive burdens in intelligent customer service or building intuitive platforms for chronic disease communities. The editors call for longitudinal studies capturing actual use rather than stated intention, cross-cultural comparisons, and hybrid methodologies, urging that as generative AI, wearable biosensors, and ambient care platforms proliferate, the questions raised here will only grow more urgent for designers, implementers, and policymakers alike.

Subject of Research: Adoption and acceptance of emerging healthcare technologies including mHealth, AI systems, and online health communities

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

Article References: Peng, X., & Prybutok, V. (2026). Editorial: Bridging the gap: understanding adoption and acceptance of emerging healthcare technologies. Frontiers in Medical Engineering, 4, Article 1898286. https://doi.org/10.3389/fmede.2026.1898286

Image Credits: AI Generated

DOI: 10.3389/fmede.2026.1898286

Keywords: healthcare technology adoption, technology acceptance model, UTAUT, mHealth, artificial intelligence, online health communities, digital health, technology anxiety, user avoidance, pharmacists, older adults, mental health