Perceived AI-Related Risks of Technological Dependence in Healthcare Among Nurses in Poland
Abstract
Background: Artificial intelligence (AI) may improve healthcare delivery, but wider adoption raises concerns about technological dependence. As AI enters clinical practice, its public health relevance extends to workforce and organizational effects. The study’s aim was to assess perceived risks of AI-related technological dependence among nurses and identify factors associated with high perceived risk.
Methods: We analyzed data from a national online survey of nurses in Poland (N = 800). Perceived AI-related technological dependence risks were measured with four items (rated on the scale from “no threat” to “critical threat”) and averaged into a composite score with good internal consistency (alpha=0.83). High perceived risk was defined a priori as a mean score ≥7.00, corresponding to at least “large threat.” Predictors were examined using hierarchical logistic regression. Effects are reported as OR with 95%CI. The study was funded by the National Science Centre, Poland (Grant 2021/43/B/NZ7/02140).
Results: Respondents’ mean age was 48.93 years (SD = 12.04). Mean e-health literacy was 31.78 (SD = 4.22), digital competence 46.52 (SD = 6.52), and AI preparedness 3.24 (SD = 0.84). High perceived AI-related technological dependence risk was identified in 211 nurses (26.4%). The final model was significant but explained limited variance (Nagelkerke R²=0.10). Higher odds were associated with residence in large cities (OR = 2.09, 95%CI: 1.18–3.71) and higher digital competence (OR = 1.05, 95%CI: 1.02–1.09). Lower odds were associated with hospital employment (OR = 0.63, 95%CI: 0.42–0.93) and greater AI preparedness (OR = 0.78, 95%CI: 0.62–0.99).
Conclusions: One in four nurses perceived AI-related technological dependence as a high risk. The model suggests that these concerns are not reducible to individual characteristics alone. AI implementation should combine digital skills development with transparent risk communication and governance of technological dependence.
Key messages: • One in four nurses perceived AI-related technological dependence as a high risk. These concerns were only weakly explained by measured characteristics.
• Higher digital competence increased perceived risk, while AI preparedness reduced it, highlighting the need for balanced implementation and transparent risk communication.
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BibTeX
@article{wojcieszko2026perceived,
title = {{Perceived AI-Related Risks of Technological Dependence in Healthcare Among Nurses in Poland}},
author = {M Wojcieszko and P Smoła and T Sikorski and M Duplaga},
journal = {The European Journal of Public Health},
year = {2026},
month = oct,
doi = {10.1093/eurpub/ckag169.401},
url = {https://doi.org/10.1093/eurpub/ckag169.401},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Perceived AI-Related Risks of Technological Dependence in Healthcare Among Nurses in Poland
AU - M Wojcieszko
AU - P Smoła
AU - T Sikorski
AU - M Duplaga
PY - 2026
DA - 2026/10/01
JO - The European Journal of Public Health
DO - 10.1093/eurpub/ckag169.401
UR - https://doi.org/10.1093/eurpub/ckag169.401
AB - Background: Artificial intelligence (AI) may improve healthcare delivery, but wider adoption raises concerns about technological dependence. As AI enters clinical practice, its public health relevance extends to workforce and organizational effects. The study’s aim was to assess perceived risks of AI-related technological dependence among nurses and identify factors associated with high perceived risk. Methods: We analyzed data from a national online survey of nurses in Poland (N = 800). Perceived AI-related technological dependence risks were measured with four items (rated on the scale from “no threat” to “critical threat”) and averaged into a composite score with good internal consistency (alpha=0.83). High perceived risk was defined a priori as a mean score ≥7.00, corresponding to at least “large threat.” Predictors were examined using hierarchical logistic regression. Effects are reported as OR with 95%CI. The study was funded by the National Science Centre, Poland (Grant 2021/43/B/NZ7/02140). Results: Respondents’ mean age was 48.93 years (SD = 12.04). Mean e-health literacy was 31.78 (SD = 4.22), digital competence 46.52 (SD = 6.52), and AI preparedness 3.24 (SD = 0.84). High perceived AI-related technological dependence risk was identified in 211 nurses (26.4%). The final model was significant but explained limited variance (Nagelkerke R²=0.10). Higher odds were associated with residence in large cities (OR = 2.09, 95%CI: 1.18–3.71) and higher digital competence (OR = 1.05, 95%CI: 1.02–1.09). Lower odds were associated with hospital employment (OR = 0.63, 95%CI: 0.42–0.93) and greater AI preparedness (OR = 0.78, 95%CI: 0.62–0.99). Conclusions: One in four nurses perceived AI-related technological dependence as a high risk. The model suggests that these concerns are not reducible to individual characteristics alone. AI implementation should combine digital skills development with transparent risk communication and governance of technological dependence. Key messages: • One in four nurses perceived AI-related technological dependence as a high risk. These concerns were only weakly explained by measured characteristics. • Higher digital competence increased perceived risk, while AI preparedness reduced it, highlighting the need for balanced implementation and transparent risk communication.
SN - 1101-1262
SN - 1464-360X
ER -