Artificial Intelligence in Nursing: Technological Benefits to Nurse's Mental Health and Patient Care Quality

Overview

Journal Healthcare (Basel)

Specialty Health Services

Date 2025 Jan 8

PMID 39765983

Authors

Hamad Ghaleb Dailah

Mahdi Koriri

Alhussean Sabei

Turky Kriry

Mohammed Zakri

Affiliations

Soon will be listed here.

Abstract

Nurses are frontline caregivers who handle heavy workloads and high-stakes activities. They face several mental health issues, including stress, burnout, anxiety, and depression. The welfare of nurses and the standard of patient treatment depends on resolving this problem. Artificial intelligence is revolutionising healthcare, and its integration provides many possibilities in addressing these concerns. This review examines literature published over the past 40 years, concentrating on AI integration in nursing for mental health support, improved patient care, and ethical issues. Using databases such as PubMed and Google Scholar, a thorough search was conducted with Boolean operators, narrowing results for relevance. Critically examined were publications on artificial intelligence applications in patient care ethics, mental health, and nursing and mental health. The literature examination revealed that, by automating repetitive chores and improving workload management, artificial intelligence (AI) can relieve mental health challenges faced by nurses and improve patient care. Practical implications highlight the requirement of using rigorous implementation strategies that address ethical issues, data privacy, and human-centred decision-making. All changes must direct the integration of artificial intelligence in nursing to guarantee its sustained and significant influence on healthcare.

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References

Khalid N, Qayyum A, Bilal M, Al-Fuqaha A, Qadir J . Privacy-preserving artificial intelligence in healthcare: Techniques and applications. Comput Biol Med. 2023; 158:106848. DOI: 10.1016/j.compbiomed.2023.106848. View

Catton H . Nursing in the COVID-19 pandemic and beyond: protecting, saving, supporting and honouring nurses. Int Nurs Rev. 2020; 67(2):157-159. PMC: 7361934. DOI: 10.1111/inr.12593. View

Ferrer J, Boelle P, Salomon J, Miliani K, LHeriteau F, Astagneau P . Management of nurse shortage and its impact on pathogen dissemination in the intensive care unit. Epidemics. 2014; 9:62-9. DOI: 10.1016/j.epidem.2014.07.002. View

Khanna N, Maindarkar M, Viswanathan V, Fernandes J, Paul S, Bhagawati M . Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment. Healthcare (Basel). 2022; 10(12). PMC: 9777836. DOI: 10.3390/healthcare10122493. View

Habli I, Lawton T, Porter Z . Artificial intelligence in health care: accountability and safety. Bull World Health Organ. 2020; 98(4):251-256. PMC: 7133468. DOI: 10.2471/BLT.19.237487. View

Kitzmiller R, Vaughan A, Skeeles-Worley A, Keim-Malpass J, Yap T, Lindberg C . Diffusing an Innovation: Clinician Perceptions of Continuous Predictive Analytics Monitoring in Intensive Care. Appl Clin Inform. 2019; 10(2):295-306. PMC: 6494616. DOI: 10.1055/s-0039-1688478. View

Ghafur S, Grass E, Jennings N, Darzi A . The challenges of cybersecurity in health care: the UK National Health Service as a case study. Lancet Digit Health. 2020; 1(1):e10-e12. DOI: 10.1016/S2589-7500(19)30005-6. View

Soriano G, Yasuhara Y, Ito H, Matsumoto K, Osaka K, Kai Y . Robots and Robotics in Nursing. Healthcare (Basel). 2022; 10(8). PMC: 9407759. DOI: 10.3390/healthcare10081571. View

Villarante D, ODonoghue S, Medeiros M, Milton E, Walsh K, ODonoghue A . A National Survey of Stress and Burnout in Critical Care Nurses: A Prepandemic Study. Dimens Crit Care Nurs. 2023; 42(5):248-254. PMC: 10403271. DOI: 10.1097/DCC.0000000000000598. View

10.

Weston M, Nordberg A . Stigma: A Barrier in Supporting Nurse Well-Being During the Pandemic. Nurse Lead. 2021; 20(2):174-178. PMC: 8577880. DOI: 10.1016/j.mnl.2021.10.008. View

11.

Leiter M, Maslach C . Nurse turnover: the mediating role of burnout. J Nurs Manag. 2009; 17(3):331-9. DOI: 10.1111/j.1365-2834.2009.01004.x. View

12.

Zhang L, Zhang T, Ren Z, Jiang G . Predicting compassion fatigue among psychological hotline counselors using machine learning techniques. Curr Psychol. 2021; 42(5):4169-4180. PMC: 8074269. DOI: 10.1007/s12144-021-01776-7. View

13.

Lipscomb C . Medical Subject Headings (MeSH). Bull Med Libr Assoc. 2000; 88(3):265-6. PMC: 35238. View

14.

OConnor S, Yan Y, Thilo F, Felzmann H, Dowding D, Lee J . Artificial intelligence in nursing and midwifery: A systematic review. J Clin Nurs. 2022; 32(13-14):2951-2968. DOI: 10.1111/jocn.16478. View

15.

Adriaenssens J, De Gucht V, Maes S . Causes and consequences of occupational stress in emergency nurses, a longitudinal study. J Nurs Manag. 2013; 23(3):346-58. DOI: 10.1111/jonm.12138. View

16.

Cimiotti J, Aiken L, Sloane D, Wu E . Nurse staffing, burnout, and health care-associated infection. Am J Infect Control. 2012; 40(6):486-90. PMC: 3509207. DOI: 10.1016/j.ajic.2012.02.029. View

17.

Johnson E, Dudding K, Carrington J . When to err is inhuman: An examination of the influence of artificial intelligence-driven nursing care on patient safety. Nurs Inq. 2023; 31(1):e12583. DOI: 10.1111/nin.12583. View

18.

Huang K, Jiao Z, Cai Y, Zhong Z . Artificial intelligence-based intelligent surveillance for reducing nurses' working hours in nurse-patient interaction: A two-wave study. J Nurs Manag. 2022; 30(8):3817-3826. DOI: 10.1111/jonm.13787. View

19.

Golas S, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T . A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018; 18(1):44. PMC: 6013959. DOI: 10.1186/s12911-018-0620-z. View

20.

Flynn C, Watson C, Patton D, OConnor T . The impact of burnout on paediatric nurses' attitudes about patient safety in the acute hospital setting: A systematic review. J Pediatr Nurs. 2024; 78:e82-e89. DOI: 10.1016/j.pedn.2024.06.023. View