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Cited by 72Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AINursing education and management

Leading with AI in critical care nursing: challenges, opportunities, and the human factor

Open access
Hassan, Eman Arafa, El-Ashry, Ayman Mohamed·2024·BMC Nursing
DOI 10.1186/s12912-024-02363-4PMC PMC11475860
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Abstract

Introduction: The integration of artificial intelligence (AI) in intensive care units (ICUs) presents both opportunities and challenges for critical care nurses. This study delves into the human factor, exploring how nurses with leadership roles perceive the impact of AI on their professional practice. Objective: To investigate how nurses perceive the impact of AI on their professional identity, ethical considerations surrounding its use, and the shared meanings they attribute to trust, collaboration, and communication when working with AI systems. Methods: An interpretive phenomenological analysis was used to capture the lived experiences of critical care nurses leading with AI. Ten nurses with leadership roles in various ICU specializations were interviewed through purposive sampling. Semi-structured interviews explored nurses’ experiences with AI, challenges, and opportunities. Thematic analysis identified recurring themes related to the human factor in leading with AI. Findings: Thematic analysis revealed two key themes which are leading with AI: making sense of challenges and opportunities and the human factor in leading with AI. The two main themes have six subthemes which revealed that AI offered benefits like task automation, but concerns existed about overreliance and the need for ongoing training. New challenges emerged, including adapting to new workflows and managing potential bias. Clear communication and collaboration were crucial for successful AI integration. Building trust in AI hinged on transparency, and collaboration allowed nurses to focus on human-centered care while AI supported data analysis. Ethical considerations included maintaining patient autonomy and ensuring accountability in AI-driven decisions. Conclusion: While AI presents opportunities for automation and data analysis, successful integration hinges on addressing concerns about overreliance, workflow adaptation, and potential bias. Building trust and fostering collaboration are fundamentals for AI integration. Transparency in AI systems allows nurses to confidently delegate tasks, while collaboration empowers them to focus on human-centered care with AI support. Ultimately, dealing with the ethical concerns of AI in ICU care requires prioritizing patient autonomy and ensuring accountability in AI-driven decisions.

AI snapshot

Written from the paper’s full text.

TL;DR

This interpretive phenomenological study explored how 10 critical care nurse leaders in four hospitals perceived AI’s impact on their practice, identity, ethics, trust, collaboration, and communication. It found that AI was seen as offering task automation and data analysis, but successful integration depended on addressing overreliance, workflow adaptation, bias, transparency, and accountability.

Key findings

  • Thematic analysis identified two main themes and six subthemes: “leading with AI: making sense of challenges and opportunities” and “the human factor in leading with AI.”
  • Nurses reported AI benefits such as automating routine tasks, generating early warning scores, compiling patient data summaries, and supporting real-time data analysis, but also raised concerns about overreliance, information overload, and maintaining clinical judgment.
  • Trust hinged on transparency: some nurses valued clear explanations and user-friendly interfaces, while others described “black-box” AI as hindering trust and collaboration.
  • Ethical concerns included potential bias from non-diverse training data, maintaining patient autonomy, and ensuring accountability in AI-driven decisions.

Methods

  • Design: Interpretive Phenomenological Analysis (IPA) using in-depth, semi-structured, one-on-one virtual interviews lasting 60–90 minutes.
  • Sample: purposive sampling of 10 critical care nurses with leadership roles (nurse managers, head nurses, in-charge nurses), at least two years of critical care experience, and exposure to AI tools; participants came from General Adult ICU, Coronary Care Unit, Surgical ICU, Medical ICU, and Trauma ICU across four hospitals.
  • Analysis: six-step inductive IPA thematic analysis, with initial noting, theme identification, cross-case analysis, member checking with two nurses, intercoder reliability between two authors, and expert review of cross-case analysis.

Limitations

  • The paper states that the sample size of 10 participants may restrict generalizability to the broader population of critical care nurses, and future studies could benefit from a larger sample.
  • The paper states that further research exploring bedside nurses who interact directly with AI could reveal challenges and opportunities not evident from a leadership standpoint.
  • Evident from the text: findings rely on self-reported interview accounts from nurse leaders only, with no observational or patient-outcome data, and the specific AI systems used by participants are not described in detail.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1186/s12912-024-02363-4

Cites

44 of 44 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

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  • Findingown data

    Concerns existed about overreliance on AI.

    “AI offered benefits like task automation, but concerns existed about overreliance and the need for ongoing training.”
    • Also stated in: § Leading with AI: making sense of challenges and opportunities · plain
      “some nurses also expressed concerns about overreliance on AI and the potential for AI-generated alerts to lead to information overload.”
    Quote in the paperSubstantial claim
  • Findingown data

    There was a need for ongoing training and support to effectively utilize AI in critical care.

    “AI offered benefits like task automation, but concerns existed about overreliance and the need for ongoing training.”
    • Also stated in: § Leading with AI: making sense of challenges and opportunities · plain
      “all participants emphasized the importance of ongoing training and support to effectively utilize AI in critical care.”
    Quote in the paperSubstantial claim
  • Findingown data

    Thematic analysis identified two main themes: leading with AI: making sense of challenges and opportunities, and the human factor in leading with AI.

    “Thematic analysis revealed two key themes which are leading with AI: making sense of challenges and opportunities and the human factor in leading with AI.”
    • Also stated in: § Finding · plain
      “The analysis identified two main themes which are leading with AI: making sense of challenges and opportunities (theme 1) and the human factor in leading with AI (theme 2).”
    Quote in the paperSubstantial claim
  • Findingnone given

    Building trust in AI hinged on transparency.

    “Building trust in AI hinged on transparency, and collaboration allowed nurses to focus on human-centered care while AI supported data analysis.”
    Quote in the paperSubstantial claim
  • Findingown data

    Clear communication and collaboration were crucial for successful AI integration.

    “Clear communication and collaboration were crucial for successful AI integration.”
    • Also stated in: § Leading with AI: making sense of challenges and opportunities · plain
      “Interviews also revealed the importance of clear communication and collaboration among nurses, other healthcare professionals, and IT staff for a smooth transition to AI-integrated workflows.”
    Quote in the paperSubstantial claim
  • May combine two claims. 26
  • Classified as probably not a substantial claim. 27

  • May combine two claims.

    Findingown data

    Ethical considerations included maintaining patient autonomy and ensuring accountability in AI-driven decisions.

    “Ethical considerations included maintaining patient autonomy and ensuring accountability in AI-driven decisions.”
    Quote in the paperSubstantial claim

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    Findingown data

    Collaboration allowed nurses to focus on human-centered care while AI supported data analysis.

    “Building trust in AI hinged on transparency, and collaboration allowed nurses to focus on human-centered care while AI supported data analysis.”
    Quote in the paperSubstantial claim

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    Findingown data

    Troubleshooting technical issues and adapting to new routines could create initial challenges.

    “Troubleshooting technical issues and adapting to new routines could create initial challenges.”
    Quote in the paperNot a substantial claim

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    Findingown data

    Some critical care nurses in this study reported that understanding how AI systems work and the data they use to generate outputs fostered trust and allowed them to interpret AI recommendations critically and integrate them into decision-making.

    “Some nurses emphasized the importance of understanding how AI systems work and the data they use to generate outputs. This transparency fostered a sense of trust and allowed nurses to interpret AI recommendations critically and integrate them effectively into their decision-making processes.”
    • Also stated in: § The human factor in leading with AI · plain
      ““Trusting AI requires understanding how it works…….Transparency in data and analysis allows critical evaluation and integration of AI recommendations into patient care.” Nurse number 9.”
    Quote in the paperSubstantial claim

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    Findingown data

    The current study revealed concerns about overreliance on AI and the potential erosion of clinical skills.

    “However, the current study revealed concerns about overreliance on AI and the potential erosion of clinical skills.”
    Quote in the paperSubstantial claim

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    Findingown data

    Nurses in this study stressed the importance of maintaining clinical expertise alongside AI use, highlighting the need for balanced integration.

    “Nurses in our study stressed the importance of maintaining clinical expertise alongside AI use, highlighting the need for balanced integration.”
    Quote in the paperSubstantial claim

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    Findingown data

    Concerns arose among nurses in this study regarding workload, information overload, and difficulties interpreting AI outputs.

    “While nurses acknowledged AI’s potential to streamline workflows, improve data analysis, and facilitate early patient deterioration detection, concerns arose regarding workload, information overload, and difficulties interpreting AI outputs.”
    Quote in the paperSubstantial claim

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    Findingown data

    The current study participants raised ethical concerns regarding AI in critical care, particularly around potential biases in AI algorithms and accountability in AI-driven decisions.

    “The current study participants raised ethical concerns regarding AI in critical care, particularly around potential biases in AI algorithms and accountability in AI-driven decisions.”
    Quote in the paperSubstantial claim

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    Findingown data

    Nurses in this study acknowledged AI's potential to streamline workflows, improve data analysis, and facilitate early patient deterioration detection.

    “While nurses acknowledged AI’s potential to streamline workflows, improve data analysis, and facilitate early patient deterioration detection, concerns arose regarding workload, information overload, and difficulties interpreting AI outputs.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Accountability is another ethical issue highlighted by study participants.

    “Accountability is another ethical issue highlighted by the study participants.”
    Quote in the paperNot a substantial claim

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    Findingown data

    Study participants described a transition towards supervisory roles overseeing AI outputs and new responsibilities such as managing AI systems.

    “The study participants described a transition towards supervisory roles overseeing AI outputs and new responsibilities such as managing AI systems.”
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    Interpretationreasoning

    Transparency in AI systems allows nurses to confidently delegate tasks.

    “Transparency in AI systems allows nurses to confidently delegate tasks, while collaboration empowers them to focus on human-centered care with AI support.”
    Quote in the paperNot a substantial claim

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    Premisecitation

    Nurse leaders face challenges integrating AI into existing workflows and ensuring they have the skills and knowledge to effectively utilize these new tools.

    “Nurse leaders also face challenges integrating AI into existing workflows and ensuring they have the skills and knowledge to effectively utilize these new tools [2, 3].”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites [2] Shen J, et al. Artificial Intelligence Versus clinicians in Disease diagnosis: systematic review. JMIR Med Inf. 2019;7:e10010. source

      Not checked in the free run.

    • cites [3] Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 2019 251 25, 44–56 (2019). source

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  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    Some nurse leaders express optimism about the ability of AI to enhance clinical decision-making and improve patient safety.

    “Some nurse leaders express optimism about the ability of AI to enhance clinical decision-making and improve patient safety [6].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [6] Buchanan C, et al. Predicted influences of Artificial Intelligence on the domains of nursing: scoping review. JMIR Nurs. 2020;3(1):e23939. https//nursing.jmir.org/2020/1/e23939. 3, e23939 (2020). source

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    Interpretationnone given

    Successful integration of AI hinges on addressing concerns about overreliance, workflow adaptation, and potential bias.

    “While AI presents opportunities for automation and data analysis, successful integration hinges on addressing concerns about overreliance, workflow adaptation, and potential bias.”
    Quote in the paperNot a substantial claim

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    Interpretationreasoning

    Building trust and fostering collaboration are fundamentals for AI integration.

    “Building trust and fostering collaboration are fundamentals for AI integration.”
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    Premisecitation

    AI has the potential to revolutionize critical care by automating tasks, providing real-time data analysis, and supporting clinical decision-making.

    “Artificial Intelligence (AI) has the potential to revolutionize critical care by automating tasks, providing real-time data analysis, and supporting clinical decision-making [1].”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites [1] Sun K, Roy A, Tobin JM. Artificial intelligence and machine learning: definition of terms and current concepts in critical care research. J Crit Care. 2024;82:154792. source

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    Premisecitation

    Critical care nurse leaders have an important role in leveraging AI technologies to enhance patient outcomes.

    “Critical care nurse leaders have an important role in leveraging AI technologies to enhance patient outcomes [4].”
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    • cites [4] Fontenot J. Spotlight on Leadership what nurse leaders need to know about Artificial Intelligence. J Nurs Adm. 2024;54:74–6. source

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    AI has the potential to improve patient outcomes through early intervention and reduce the burden of routine tasks on healthcare professionals.

    “This aligns with findings by Gallo et al., (2024) [22], which underscore AI’s potential to improve patient outcomes through early intervention and reduce the burden of routine tasks on healthcare professionals.”
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    • cites Gallo et al., (2024) [22] source

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    Study design and conductown data

    A detailed audit trail documented all research decisions, including interview protocols, coding schemes, and revisions.

    “we acknowledged the potential for researcher bias and maintained a detailed audit trail to document all research decisions, including interview protocols, coding schemes, and any revisions made throughout the analysis process.”
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    Interpretationown data

    Transparency in AI outputs enhanced trust and allowed nurses to integrate AI recommendations more seamlessly into their workflow.

    “This transparency enhanced trust and allowed nurses to integrate AI recommendations more seamlessly into their workflow.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Study design and conductreasoning

    The study included critical care nurses from various ICU specializations at four different hospitals, allowing a broader understanding of how AI integration impacts critical care practices across different patient populations and care settings.

    “The study included critical care nurses from various intensive care unit (ICU) specializations at four different hospitals. These specializations included General Adult ICU, Coronary Care Unit, Surgical ICU, Medical ICU, and Trauma ICU. This diversity in ICU types allowed for a broader understanding of how AI integration impacts critical care practices across different patient populations and care”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Interpretationown data

    Bias in AI algorithms could lead to unfair treatment decisions for certain patient populations.

    “This could lead to unfair treatment decisions for certain patient populations.”
    Quote in the paperSubstantial claim

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    Interpretationown data

    Lack of clear explanations for AI-generated recommendations could lead to confusion and hinder nurses' ability to effectively utilize the information in patient care decisions.

    “The lack of clear explanations for AI-generated recommendations could lead to confusion and hinder nurses’ ability to effectively utilize the information in patient care decisions.”
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    Study design and conductcitation

    The interview guide was developed based on a review of relevant literature and expert consultations to ensure it effectively addressed the study's aims.

    “The guide was developed based on a review of relevant literature and expert consultations to ensure it effectively addressed the study’s aims [18].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [18] Kallio H, Pietilä AM, Johnson M, Kangasniemi M. Systematic methodological review: developing a framework for a qualitative semi-structured interview guide. J Adv Nurs. 2016;72:2954–65. source

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    Study design and conductnone given

    An expert in qualitative research reviewed the cross-case analysis to ensure the validity and reliability of the findings.

    “Furthermore, an expert in qualitative research reviewed our cross-case analysis to ensure the validity and reliability of our findings.”
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    Premisecitation

    Apprehensions about "de-skilling" healthcare professionals have been documented in the literature.

    “This sentiment is reflected in the literature, where apprehensions about “de-skilling” healthcare professionals have been documented [24].”
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    • cites [24] Choudhury A, Chaudhry Z. Large Language models and user trust: consequence of Self-Referential Learning Loop and the deskilling of Health Care professionals. J Med Internet Res. 2024;26:e56764. source

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    Study design and conductcitation

    Data saturation was achieved after ten interviews, meaning no new themes or insights were emerging.

    “Ten interviews continued until data saturation was achieved, meaning no new themes or insights were emerging from subsequent interviews [21].”
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    • cites [21] Guest G, Bunce A, Johnson L. How many interviews are enough? An experiment with data saturation and variability. Field Methods. 2006;18:59–82. source

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    Premisecitation

    AI could process vast amounts of data more efficiently than humans, supporting timely and accurate clinical decisions.

    “Arnold et al., (2019) [23] highlighted that AI could process vast amounts of data more efficiently than humans, supporting timely and accurate clinical decisions.”
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    Study design and conductcitation

    Audio-recording and transcription ensured accuracy and completeness of the interview data.

    “The recordings were then transcribed for analysis, ensuring accuracy and completeness [20].”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites [20] Braun V, Clarke V. Using thematic analysis in psychology. Qual Res Psychol. 2006;3:77–101. source

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    Interpretationown data

    Lack of transparency in AI algorithms could hinder trust and limit nurses' willingness to rely on AI outputs.

    “This lack of transparency could hinder trust and limit nurses’ willingness to rely on AI outputs.”
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    Premisecitation

    Patient-centered care principles prioritize patient autonomy and individualized treatment plans.

    “This aligns with patient-centered care principles, prioritizing patient autonomy and individualized treatment plans [39].”
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    • cites [39] Epstein RM, Street RL. The values and value of patient-centered care. Ann Fam Med. 2011;9:100. source

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    Premisecitation

    A synergistic approach where AI complements but does not replace human judgment is argued for by Aquino et al. (2023).

    “This is consistent with Aquino et al., (2023) [25] views, who argue for a synergistic approach where AI complements but does not replace human judgment.”
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    Effective AI tools in healthcare should be designed with end-users in mind, ensuring that outputs are easily interpretable and actionable.

    “As Sendak et al. (2020) [43] noted, effective AI tools in healthcare should be designed with end-users in mind, ensuring that outputs are easily interpretable and actionable.”
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    Interpretationreasoning

    Ensuring fairness and mitigating potential bias within AI algorithms is essential for ethical patient care.

    “Additionally, ensuring fairness and mitigating potential bias within AI algorithms is essential for ethical patient care.”
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    Premisecitation

    Training healthcare professionals to understand and work with AI is vital for successful integration.

    “These findings are supported by Grote and Berens (2020) [41], who emphasize that training healthcare professionals to understand and work with AI is vital for successful integration.”
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    Premisecitation

    Ethical frameworks that clearly define accountability in AI-driven healthcare are needed.

    “A study by Morley et al. (2020) [7] supports this by highlighting the need for ethical frameworks that clearly define accountability in AI-driven healthcare.”
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    Interpretationown data

    The positive experiences reported by some nurses when AI systems provided clear explanations underscore the importance of designing AI tools that prioritize usability and transparency.

    “The positive experiences reported by some nurses when AI systems provided clear explanations underscore the importance of designing AI tools that prioritize usability and transparency.”
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    Premisecitation

    Integrating AI in healthcare requires continuous professional development and adaptation to new workflows.

    “This transition is supported by findings from Bajwa et al. (2021) [28], who noted that integrating AI in healthcare requires continuous professional development and adaptation to new workflows.”
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    Clear guidelines and accountability frameworks are necessary to delineate responsibilities in AI-assisted medical decision-making.

    “This concern is reflected in the work of Elendu et al., (2023) [38], who argue that clear guidelines and accountability frameworks are necessary to delineate responsibilities in AI-assisted medical decision-making.”
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    AI can enhance human capabilities in healthcare by handling data-intensive tasks.

    “This collaborative approach is advocated by Topol (2019) [3], who suggests that AI can enhance human capabilities in healthcare by handling data-intensive tasks.”
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    Interpretationcitation

    The second theme underscores the critical role of human factors in successfully integrating AI into nursing practice.

    “The second theme underscores the critical role of human factors in successfully integrating AI into nursing practice.”
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    Interpretationreasoning

    The key to unlocking AI's potential lies in addressing human factors.

    “The key to unlocking AI’s potential lies in addressing these human factors.”
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    Premisecitation

    Biases in training data can lead to biased AI systems, which can perpetuate healthcare disparities.

    “Obermeyer et al. (2019) [36] and Nazerid et al., (2023) [8] discuss how biases in training data can lead to biased AI systems, which can perpetuate healthcare disparities.”
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    • cites Obermeyer et al. (2019) [36] source

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    • cites Nazerid et al., (2023) [8] source

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    Interpretationreasoning

    Clear and concise communication from AI systems, with user-friendly interfaces and transparent explanations for recommendations, is crucial for building trust and fostering collaboration.

    “Clear and concise communication from AI systems, with user-friendly interfaces and transparent explanations for recommendations, is crucial for building trust and fostering collaboration.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductreasoning

    Despite adhering to the rigorous qualitative research methodology of interpretive phenomenology and conducting the study across four hospitals to capture diverse nursing experiences, this research still has several limitations.

    “Despite adhering to the rigorous qualitative research methodology of interpretive phenomenology and conducting the study across four hospitals to capture diverse nursing experiences, this research still has several limitations.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

    Interpretationcitation

    Effective communication and ongoing training are critical for integrating AI into nursing practice.

    “Effective communication and ongoing training are critical for integrating AI into nursing practice.”
    Quote in the paperNot a substantial claim
47 of 47

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Each contradiction between two passages listed here was confirmed by an independent check.

  • Holzinger et al. is cited with conflicting publication years

    Minor

    The discussion dates the cited study to 2020, but reference 42 lists its publication year as 2019.

1 of 1

Cites

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Research works printed but not linked: 1

  1. Smith JA, Osborn M. Interpretative Phenomenological Analysis. In J. A. Smith, qualitative psychology: a practical guide to Research methods (Pp. 53–80). London: SAGE; 2003.

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Related

Authors
Hassan, E. A., & El-Ashry, A. M.
Affiliations
Alexandria University (EG)
Corresponding author
Hassan, Eman Arafa
ORCID
Hassan, Eman Arafa
Venue
BMC Nursing
Publisher
Springer Science and Business Media LLC
Published
2024-10-14
Volume
23
Issue
1
Pages
752-752
ISSN
1472-6955 (ISSN-L)
DOI
10.1186/s12912-024-02363-4
PMC
PMC11475860
OpenAlex
W4403397850
Semantic Scholar
66c191ae518969e618da5fb45ab8d3a0e55a31ff
Cited by
72
References
44
Citations by year
2026: 35 · 2025: 35 · 2024: 2
FWCI
6.75
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Nursing (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Alexandria University
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
  • Health Sciences › Nursing › Research and Theory › Nursing education and management
Keywords
artificial intelligence in nursing, critical care nursing, intensive care unit, nursing leadership, human factors, professional identity, trust in AI, human-AI collaboration, AI ethics, patient autonomy, overreliance on AI, workflow adaptation, algorithmic bias, transparency, human-centered care, task automation

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