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The Nurse's Role in Generative AI-Assisted Clinical Decision Support Systems
This Master's-level nursing essay examines the nurse's evolving role in working with generative AI and clinical decision support (CDS) systems. Written in APA (7th edition) format, it serves as a model academic paper for students in nursing, health informatics, and healthcare technology programs. The essay explains how AI CDS systems operate — using machine learning to analyze patient data, flag deterioration, and support documentation — and positions nurses as the primary users and contributors to these tools. It then works through the central tensions: ethical concerns around autonomy, deskilling, the "accountability vacuum," and algorithmic bias, followed by a balanced look at the effects of generative AI on patient outcomes and nurse workload. Notably, it maintains academic caution, repeatedly flagging where large-scale causal evidence is still lacking. This makes it a strong reference for evidence-based, critically balanced argumentation and for integrating recent research in a healthcare-technology context.
June 1, 2026
* The sample essays are for browsing purposes only and are not to be submitted as original work to avoid issues with plagiarism.
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The Nurse's Role in Generative AI-Assisted Clinical Decision Support Systems
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The Nurse's Role in Generative AI-Assisted Clinical Decision Support Systems
The use of Artificial Intelligence (AI) in the health care system is complex and constantly
evolving. AI Clinical Decision Support (CDS) systems can help nurses and clinicians with
diagnosis, detection of patient deterioration, and optimization of workload and documentation.
With the integration of these systems in care delivery, many challenges arise, such as the correct
boundaries for the limits of machine intelligence and the continued importance of clinical
judgment. The rapidly growing and fluid nature of AI creates the need for a more critical-
thinking and informed nursing workforce. This paper will outline how an AI CDS system
operates and how it might be used in the nursing field, the ethical issues surrounding algorithms
and autonomy and responsibility, and the impact on nurse workload and patient outcomes.
The role of the nurse in interpreting the use of AI Clinical Decision Support Tools
AI clinical decision support systems rely on machine learning algorithms to examine
historical clinical data in order to detect patterns. These algorithms run huge volumes of patient
data, such as vital signs, lab results, medication histories, and even health records. They alert in
real-time, calculate risk stratification, and give suggestions to physicians and nurses on what they
should do. In nursing, AI assists in making clinical decisions, employing predictive analysis, and
automating documentation. The nurses are the main users and the largest contributors to the
system (Brydges, 2025). Nurses are integrating AI systems at different points in their service
delivery, such as AI systems that can alert them to patients in critical conditions during initial
assessment; AI systems that can suggest medication-related problems during medication
administration; and AI aids that can help them determine care priorities in the formulation of care
plans. AI systems can be used to help nurses in identifying patients with more severe conditions,
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allocating nurse staffing resources, and improving nurse productivity. The successful
implementation of systems is mainly based on the engagement of the nurses with the systems,
and particularly with what they give as a result of the systems.
Ethical Issues
While technology can help, AI applications in clinical nursing raise unique ethical
concerns that must be resolved. AI systems could also threaten nurses' autonomy by encouraging
clinicians to rely on the AI system instead of making clinical decisions (Levin et al., 2025). Thus,
it is likely that if the use of an AI system becomes the norm, it will lead to deskilling. However,
the problem of accountability only further adds to questions. Who is at fault for the harm caused
by the use of AI systems in healthcare? Is the AI system developed by the nurse, the institution,
or the company?
From an ethical perspective, this ‘accountability vacuum' is problematic within the
nursing profession, where there are strict ethical codes in place. The role of nurses remains the
same, and nurses need to maintain and demonstrate their clinical accountability when using AI
systems or other related tools to support clinical practice. There is the potential for AI systems to
become biased. Bias occurs when AI systems are trained on historical and unequal data, which in
turn becomes a source of bias and inequality. AI systems trained with historical and inequitable
data will be inequitable and biased, and often perform poorly with marginalized and low-
resourced populations. Nurses need to be sensitive and to think critically about AI systems.
Generative AI on Patient Outcomes and Nurse Workload
There is preliminary evidence suggesting that generative and conversational AI may have
a positive effect on certain patient outcomes, such as education, medication adherence, and post-
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discharge follow-up. Tools such as the Quincy chatbot help monitor patients after discharge and
verify that follow-up appointments are scheduled, that medications are refilled, and that patients
are reporting symptoms. This can aid the nursing staff in performing early interventions and may
help reduce the risk of patients being readmitted (Brydges, 2025). AI-powered virtual assistants
that offer electronic educational material can aid in the enhancement of patient understanding
and can help improve engagement and adherence to treatment plans (Brydges, 2025). However,
the evidence of large-scale, well-designed studies that can show a direct causal relationship
between the use of generative AI and a decrease in patients’ morbidity and/or mortality is lacking
because a majority of these studies are still in the development and/or testing phases (Cant et al.,
2026).
In the case of nurse workload, the use of generative AI, together with ambient voice
technologies, is intended to decrease the workload associated with documentation and streamline
the performance of administrative tasks. Ambient voice technology can record conversations that
occur at the bedside and then capture that data in an electronic health record. When this
technology is integrated in the healthcare setting, it allows nurses to once again be able to
provide direct patient care (Brydges, 2025). AI, combined with natural language processing to
capture and extract clinical information from nursing documentation, can reduce the amount of
time spent on data entry and improve workflows (Brydges, 2025). Even with these potential
work applications, the evidence of effectiveness in the real world is lacking because of limited
controlled studies on the work applications and the lack of practicing nurses on the AI design
teams.
Conclusion
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AI is changing the future of clinical nursing and presents novel opportunities for decision
support, workload, and patient safety management. The preparation, critical engagement, and
quality of the nurse using AI are what are crucial to successful integration, rather than advanced
algorithms. AI-based decision support will mean that nurses will experience new roles, such as
interpreters of outputs, guardians of ethics, and custodians of justice through equitable care. The
introduction of AI will not eliminate the nursing profession. The work of nurses is going to be
more complex, complicated, and involved in the provision of care with the help of artificial
intelligence.
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References
Brydges, G. (2025). Artificial intelligence in nursing practice: decisional support, clinical
integration, and future directions. OJIN the Online Journal of Issues in Nursing, 30(2).
https://doi.org/10.3912/ojin.vol30no02man04
Cant, R., Ryan, C., & Chugh, R. (2026). Artificial Intelligence Technologies in Nursing Clinical
Decision-Making: An umbrella review. Journal of Advanced Nursing. https://doi.org/
10.1111/jan.70579
Levin, C., Zaboli, A., Turcato, G., & Saban, M. (2025). Nursing judgment in the age of
generative artificial intelligence: A cross-national study on clinical decision-making
performance among emergency nurses. International Journal of Nursing Studies, 172,
105216. https://doi.org/10.1016/j.ijnurstu.2025.105216
Rony, M. K. K., Parvin, M. R., & Ferdousi, S. (2023). Advancing nursing practice with artificial
intelligence: Enhancing preparedness for the future. Nursing Open, 11(1). https://doi.org/
10.1002/nop2.2070
June 1, 2026
Academic level:
Graduate
Type of paper:
Essay
Discipline:
Nursing
Citation:
APA
Pages:
4 (990 words)
Spacing:
Double
* The sample essays are for browsing purposes only and are not to be submitted as original work to avoid issues with plagiarism.