Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus
Abstract Background Neonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | BMC Nursing |
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| Online Access: | https://doi.org/10.1186/s12912-025-03044-6 |
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| author | Abeer Nuwayfi Alruwaili Afrah Madyan Alshammari Ali Alhaiti Nadia Bassuoni Elsharkawy Sayed Ibrahim Ali Osama Mohamed Elsayed Ramadan |
| author_facet | Abeer Nuwayfi Alruwaili Afrah Madyan Alshammari Ali Alhaiti Nadia Bassuoni Elsharkawy Sayed Ibrahim Ali Osama Mohamed Elsayed Ramadan |
| author_sort | Abeer Nuwayfi Alruwaili |
| collection | DOAJ |
| description | Abstract Background Neonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its impact on nurses’ decision-making, professional autonomy, and organizational workflows. Aim This study explored how neonatal nurses experience and integrate generative AI in clinical decision-making, examining its influence on nursing practice, organizational dynamics, and cultural adaptation in Saudi Arabian NICUs. Methods An interpretive phenomenological approach, guided by Complexity Science, Normalization Process Theory, and Tanner’s Clinical Judgment Model, was employed. A purposive sample of 33 neonatal nurses participated in semi-structured interviews and focus groups. Thematic analysis was used to code and interpret data, supported by an inter-rater reliability of 0.88. Simple frequency counts were included to illustrate the prevalence of themes but were not used as quantitative measures. Trustworthiness was ensured through reflexive journaling, peer debriefing, and member checking. Results Five themes emerged: (1) Clinical Decision-Making, where 93.9% of nurses reported that AI-enhanced judgment but required human validation; (2) Professional Practice Transformation, with 84.8% noting evolving role boundaries and workflow changes; (3) Organizational Factors, as 97.0% emphasized the necessity of infrastructure, training, and policy integration; (4) Cultural Influences, with 87.9% highlighting AI’s alignment with family-centered care; and (5) Implementation Challenges, where 90.9% identified technical barriers and adaptation strategies. Conclusions Generative AI can support neonatal nurses in clinical decision-making, but its effectiveness depends on structured training, reliable infrastructure, and culturally sensitive implementation. These findings provide evidence-based insights for policymakers and healthcare leaders to ensure AI integration enhances nursing expertise while maintaining safe, patient-centered care. |
| format | Article |
| id | doaj-art-b06b40f51ff14ac7be31b7a459eb51f8 |
| institution | DOAJ |
| issn | 1472-6955 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Nursing |
| spelling | doaj-art-b06b40f51ff14ac7be31b7a459eb51f82025-08-20T03:10:07ZengBMCBMC Nursing1472-69552025-04-0124112010.1186/s12912-025-03044-6Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicusAbeer Nuwayfi Alruwaili0Afrah Madyan Alshammari1Ali Alhaiti2Nadia Bassuoni Elsharkawy3Sayed Ibrahim Ali4Osama Mohamed Elsayed Ramadan5College of Nursing, Nursing Administration and Education Department, Jouf UniversityCollege of Nursing, Department of Maternity and Pediatric Health Nursing, Jouf UniversityDepartment of Nursing, College of Applied Sciences, Almaarefa UniversityCollege of Nursing, Department of Maternity and Pediatric Health Nursing, Jouf UniversityCollege of Medicine, Department of Family and Community Medicine, King Faisal UniversityPediatric Nursing Department, Faculty of Nursing, Cairo UniversityAbstract Background Neonatal nurses in high-risk Neonatal Intensive Care Units (NICUs) navigate complex, time-sensitive clinical decisions where accuracy and judgment are critical. Generative artificial intelligence (AI) has emerged as a supportive tool, yet its integration raises concerns about its impact on nurses’ decision-making, professional autonomy, and organizational workflows. Aim This study explored how neonatal nurses experience and integrate generative AI in clinical decision-making, examining its influence on nursing practice, organizational dynamics, and cultural adaptation in Saudi Arabian NICUs. Methods An interpretive phenomenological approach, guided by Complexity Science, Normalization Process Theory, and Tanner’s Clinical Judgment Model, was employed. A purposive sample of 33 neonatal nurses participated in semi-structured interviews and focus groups. Thematic analysis was used to code and interpret data, supported by an inter-rater reliability of 0.88. Simple frequency counts were included to illustrate the prevalence of themes but were not used as quantitative measures. Trustworthiness was ensured through reflexive journaling, peer debriefing, and member checking. Results Five themes emerged: (1) Clinical Decision-Making, where 93.9% of nurses reported that AI-enhanced judgment but required human validation; (2) Professional Practice Transformation, with 84.8% noting evolving role boundaries and workflow changes; (3) Organizational Factors, as 97.0% emphasized the necessity of infrastructure, training, and policy integration; (4) Cultural Influences, with 87.9% highlighting AI’s alignment with family-centered care; and (5) Implementation Challenges, where 90.9% identified technical barriers and adaptation strategies. Conclusions Generative AI can support neonatal nurses in clinical decision-making, but its effectiveness depends on structured training, reliable infrastructure, and culturally sensitive implementation. These findings provide evidence-based insights for policymakers and healthcare leaders to ensure AI integration enhances nursing expertise while maintaining safe, patient-centered care.https://doi.org/10.1186/s12912-025-03044-6Generative artificial intelligenceNeonatal nursingClinical decision-makingCultural contextProfessional practiceSaudi Arabia |
| spellingShingle | Abeer Nuwayfi Alruwaili Afrah Madyan Alshammari Ali Alhaiti Nadia Bassuoni Elsharkawy Sayed Ibrahim Ali Osama Mohamed Elsayed Ramadan Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus BMC Nursing Generative artificial intelligence Neonatal nursing Clinical decision-making Cultural context Professional practice Saudi Arabia |
| title | Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus |
| title_full | Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus |
| title_fullStr | Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus |
| title_full_unstemmed | Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus |
| title_short | Neonatal nurses’ experiences with generative AI in clinical decision-making: a qualitative exploration in high-risk nicus |
| title_sort | neonatal nurses experiences with generative ai in clinical decision making a qualitative exploration in high risk nicus |
| topic | Generative artificial intelligence Neonatal nursing Clinical decision-making Cultural context Professional practice Saudi Arabia |
| url | https://doi.org/10.1186/s12912-025-03044-6 |
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