Nurses’ perceptions of the AI-supported digital healthcare environment

Abstract Background As artificial intelligence (AI) becomes increasingly prevalent in healthcare, understanding its impact on clinical environments is essential. Nurses play a critical role in responding to this technological shift. This study explores nursing personnel's opinions on the implementation of AI in healthcare. Methods A telephone-based survey was performed in a sample of 600 Polish nurses in 2024. Respondents’ attitudes toward AI were assessed using the Shinners AI Perception tool (SHAIP). Multivariable linear regression models (MM) were developed for SHAIP subscores: ‘Professional impact of AI’ (PI) and ‘Preparedness for AI’ (PR). Independent variables included sociodemographic characteristics, level of professional development, and opinions on and use of digital tools. Results MM revealed that significant predictors of PI subscore included possession of a speciality certificate (SC), use of social media (SM), perceived usefulness of the Internet for health (USEF), and the Nurses’ Attitudes Toward Computers (NATC) score. Nurses with an SC were likelier to report lower PI (B, 95%CI: -0.88, -1.69 - -0.06). Infrequent SM users also showed lower PI compared to frequent users (B, 95%CI: -1.66, -3,27 - -0.05). Higher NATC and USEF scores were positively associated with PI (B, 95%CI: 0.04, 0.004 - 0.07, and 1.29, 0.33 - 2.26, respectively). Significant predictors of PR subscore included level of education, professional experience, and USEF, but not NATC. Nurses with a Bachelor's degree reported higher PR than those with a Master's degree (B, 95%CI: 0.65, 0.05 - 1.24). Additionally, both SC and USEF were associated with higher PR scores (B, 95%CI: 0.80, 0.31 - 1.28, and 0.64, 0.09 - 1.20, respectively). Conclusions The determinants of the nurses’ perception of the professional impact and preparedness to use AI show a complex pattern. It appears that higher levels of education and professional competencies are associated with more cautious attitudes toward AI. Key messages • Nurses with higher levels of education and training tend to be less optimistic about the impact of AI and their preparedness for its use in healthcare. • Belief in the usefulness of the Internet for health-related purposes consistently predicts more positive attitudes toward AI in healthcare.

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Publication Details

Journal
European Journal of Public Health
Published
2025-10-01
DOI
https://doi.org/10.1093/eurpub/ckaf161.229
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

Nurses’ perceptions of the AI-supported digital healthcare environment

European Journal of Public Health
Artificial Intelligence in Healthcare and Education
article

Nurses’ perceptions of the AI-supported digital healthcare environment

article en

Abstract

Abstract Background As artificial intelligence (AI) becomes increasingly prevalent in healthcare, understanding its impact on clinical environments is essential. Nurses play a critical role in responding to this technological shift. This study explores nursing personnel's opinions on the implementation of AI in healthcare. Methods A telephone-based survey was performed in a sample of 600 Polish nurses in 2024. Respondents’ attitudes toward AI were assessed using the Shinners AI Perception tool (SHAIP). Multivariable linear regression models (MM) were developed for SHAIP subscores: ‘Professional impact of AI’ (PI) and ‘Preparedness for AI’ (PR). Independent variables included sociodemographic characteristics, level of professional development, and opinions on and use of digital tools. Results MM revealed that significant predictors of PI subscore included possession of a speciality certificate (SC), use of social media (SM), perceived usefulness of the Internet for health (USEF), and the Nurses’ Attitudes Toward Computers (NATC) score. Nurses with an SC were likelier to report lower PI (B, 95%CI: -0.88, -1.69 - -0.06). Infrequent SM users also showed lower PI compared to frequent users (B, 95%CI: -1.66, -3,27 - -0.05). Higher NATC and USEF scores were positively associated with PI (B, 95%CI: 0.04, 0.004 - 0.07, and 1.29, 0.33 - 2.26, respectively). Significant predictors of PR subscore included level of education, professional experience, and USEF, but not NATC. Nurses with a Bachelor's degree reported higher PR than those with a Master's degree (B, 95%CI: 0.65, 0.05 - 1.24). Additionally, both SC and USEF were associated with higher PR scores (B, 95%CI: 0.80, 0.31 - 1.28, and 0.64, 0.09 - 1.20, respectively). Conclusions The determinants of the nurses’ perception of the professional impact and preparedness to use AI show a complex pattern. It appears that higher levels of education and professional competencies are associated with more cautious attitudes toward AI. Key messages • Nurses with higher levels of education and training tend to be less optimistic about the impact of AI and their preparedness for its use in healthcare. • Belief in the usefulness of the Internet for health-related purposes consistently predicts more positive attitudes toward AI in healthcare.

European Journal of Public HealthVol. 35(Supplement_4)
Jagiellonian University (PL)
Openalex Percentile: Top 51%
Artificial Intelligence in Healthcare and Education
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