Is technostress in hospitals undermining care quality and AI adoption?

Researchers have raised fresh concerns about how digital transformation in healthcare is affecting frontline workers, with new research showing that increased reliance on digital systems and artificial intelligence may be contributing to higher stress levels among nurses, more missed patient care, and reduced willingness to adopt AI-supported tools.

The study, titled "How Digital Stress and eHealth Literacy Relate to Missed Nursing Care and Willingness to Use AI Decision Support," published in Healthcare, examines how technostress, digital literacy, burnout, and workplace conditions interact to shape both care quality and readiness for artificial intelligence adoption in clinical settings.

The research is based on data from 239 registered nurses working in a tertiary-care hospital in Romania, offering one of the most integrated analyses to date of how digital strain and workforce capability influence both patient outcomes and the future of AI in nursing.

Rising technostress linked to burnout and missed patient care

The study finds a strong and consistent relationship between technostress and deteriorating conditions in nursing practice, with higher levels of digital strain directly associated with increased emotional exhaustion and more frequent instances of missed nursing care.

Technostress, defined as the strain caused by navigating complex digital systems, constant alerts, documentation burdens, and workflow disruptions, emerged as a central driver of negative outcomes. Nurses experiencing higher levels of this strain were significantly more likely to report incomplete or delayed care tasks, indicating that digital overload is not just a workforce issue but a patient safety concern.

Data from the study shows that technostress is closely tied to emotional exhaustion, a key dimension of burnout, and both factors independently contribute to care omissions. The findings suggest that digital tools, while designed to improve efficiency, may be adding cognitive and operational pressure that interferes with the completion of essential clinical tasks.

The relationship is not marginal. As technostress increases, missed care rises in a clear pattern, reinforcing the idea that digital burden and care quality are deeply interconnected. Nurses working in high-intensity environments such as critical care units reported even higher levels of missed care, reflecting the compounded pressure of both clinical complexity and digital demands.

The study notes that burnout is not just a parallel outcome but part of the mechanism linking digital strain to care failures. Emotional exhaustion alone accounts for a significant portion of the relationship between technostress and missed care, suggesting that workforce well-being is a critical mediator of patient safety outcomes.

Digital transformation in healthcare is not neutral. Without careful design and support, it can reshape workload dynamics in ways that increase risk at the bedside.

eHealth Literacy Emerges as Key Factor in AI Readiness

While technostress drives negative outcomes, the study identifies eHealth literacy as a critical protective factor that can improve both workforce resilience and openness to artificial intelligence.

eHealth literacy, defined as the ability to find, understand, and apply digital health information, was strongly associated with lower technostress and higher acceptance of AI-driven clinical decision support systems. Nurses with stronger digital skills were better able to navigate complex systems, interpret outputs, and engage with technology in a more confident and effective way.

The findings show that higher levels of eHealth literacy significantly reduce the likelihood of resistance to AI tools. Nurses who scored higher in digital competence were more likely to trust AI-generated recommendations and express willingness to use such systems in clinical practice.

This relationship highlights a crucial shift in how AI adoption is understood. Acceptance is not driven solely by the perceived usefulness of the technology but by the user's ability to interact with it effectively. In environments where AI tools are still emerging and not fully embedded into workflows, this perception-based readiness becomes even more important.

The study also reveals that digital literacy is unevenly distributed across the workforce, creating distinct risk profiles. Nurses with lower digital skills and higher technostress form a vulnerable group characterized by lower AI acceptance, higher burnout, and greater levels of missed care. This divide suggests that digital transformation may be amplifying existing inequalities within healthcare teams. Without targeted training and support, the benefits of AI may remain concentrated among more digitally confident staff, while others face increasing strain.

The research also underscores that AI readiness is not simply a matter of introducing new tools. It is closely tied to training, usability, and the broader work environment, reinforcing the need for a system-level approach to digital adoption.

Workplace conditions shape AI adoption and patient safety

The study further draws focus to the critical role of workplace conditions, particularly safety climate and teamwork, in shaping both care quality and AI adoption.

A stronger safety climate, defined by shared perceptions of teamwork, communication, and organizational priorities, was associated with lower levels of missed care and higher acceptance of AI tools. This suggests that organizational culture plays a key role in how digital systems are experienced and used in practice.

In environments where teamwork is strong and safety is prioritized, nurses are better equipped to manage digital demands and integrate new technologies into their workflow. Conversely, weaker safety climates amplify the negative effects of technostress, increasing both care omissions and resistance to AI.

The study identifies three distinct workflow profiles among nurses, ranging from low-strain, high-literacy groups with strong outcomes to high-strain, low-literacy groups facing the most challenges. The latter group shows the highest levels of missed care, the lowest acceptance of AI, and the greatest intention to leave the profession.

This clustering highlights that the impact of digital transformation is not uniform. Instead, it creates distinct operational environments within the same institution, each with different risks and outcomes.

Another key finding is the link between technostress, burnout, and workforce retention. The combination of high digital strain, emotional exhaustion, and missed care was found to strongly predict nurses' intention to leave their jobs, pointing to a potential long-term workforce crisis if these issues are not addressed.

According to the study, current levels of AI acceptance reflect perception rather than actual use, as many hospitals are still in early stages of AI deployment. This means that trust, readiness, and willingness to adopt AI are being shaped before widespread implementation, making early interventions critical.

Overall, the findings suggest that successful AI integration in healthcare depends less on the technology itself and more on the conditions in which it is introduced. Digital systems that increase workload without improving usability or support may undermine both staff well-being and patient care.