The healthcare sector is undergoing drastic changes amid rapid adoption of artificial intelligence (AI). According to the American Medical Association (AMA), two-thirds of physicians currently use AI. Meanwhile, insights from McKinsey and the American Nurses Foundation suggest that nurses also wish to make the most of this technology, with 42% convinced it will improve patients’ quality of care.
However, there remains some opposition to AI; skeptics are concerned not only about the ethical implications but also the technological learning curve. These concerns, although valid, may be addressed and overcome through tech-focused training, both within colleges and in the workforce.
Intrigued by the future of artificial intelligence? There is much to look forward to alongside several issues worth addressing, including ongoing skepticism from a subset of the nursing workforce. Below, we explore the future of AI in nursing while answering: How is AI used in nursing, and what will it take to support adoption on a wide scale?
How Is AI Used in Nursing?
Artificial intelligence can serve numerous functions in nursing. Mainly, it offers the chance to automate time-consuming or mundane tasks as well as improve quality of care by shedding light on patient patterns or other concerns that busy nurses may otherwise overlook. More specific uses include:
- Clinical decision support systems (CDSS). While computer-assisted solutions such as CDSS have played a central role in healthcare for several decades, these systems are seeing dramatic improvements in response to AI integrations. Drawing on larger volumes of data, today’s CDSS can make personalized recommendations in real time, with research also revealing its value in “triage systems [that] optimize patient prioritization in emergency departments.”
- Predictive analytics. Used to forecast future conditions based on historic health information, predictive analytics can help nurses understand when patients are deteriorating or when additional interventions are needed. This is a critical element of precision medicine, offering, as advocates explain, a more targeted approach to addressing patients with “different clinical characteristics and genetic backgrounds.”
- Automated documentation. Promising to limit the administrative burden of contemporary nursing, automated documentation can free up nurses to focus on clinical matters and patient engagement. According to the American Nurses Association (ANA), this is important, in part, because documentation burden can influence turnover and may also “prevent nurses from performing as knowledge workers,” even turning them into “low-skilled data entry clerks.”
- Virtual nursing assistants. AI-driven virtual assistants provide yet another opportunity for tackling the administrative and documentation burden of modern nursing while helping make sense of high volumes of information. Mayo Clinic, for example, has created a generative AI tool known as the Nurse Virtual Assistant, which displays curated information within electronic health record (EHR) systems. Mayo’s Chief Nursing Officer Ryannon Frederick explains, “By reducing administrative burden, we allow nurses to focus on the most important part of their work: caring for patients with skill, compassion and presence.”
- Workload management. Time management in nursing relies on prioritization. Nurses select tasks based on urgency while also actively seeking to limit the effects of burnout. AI can support this effort by helping predict nursing workloads and highlighting potential bottlenecks.
Why Are Nurses Hesitant About AI?
Nurses, like many professionals, are cautiously optimistic about AI-powered solutions while worrying about the implications of these cutting-edge technologies. Their concerns are multifaceted, reflecting growing anxiety surrounding not only employment prospects but also ethical care and compliance. Some simply find AI intimidating. Several express a blend of concerns that are often closely linked.
The following are among the most common and impactful fears, often shared even by tech-focused nurses:
1. Workload Concerns
Nurses may worry about changes to their carefully established routines. Some suspect that the process of adapting to AI will be difficult, disrupting their workflows or even taking time away from patient care. Yet, in reality, AI has the power to accomplish the opposite: It can limit the hassle of documentation, freeing up nurses to spend more time connecting with patients.
Still, workload-related concerns are understandable, as nurses who lack tech skills may find it time-consuming to develop these competencies — especially when training is stacked onto already considerable professional development requirements.
2. Ethical Concerns
At the same time, ethical questions must be addressed. This becomes a matter of how AI is adopted and which safeguards are in place. Issues worth considering include:
- Patient privacy. AI systems depend on vast volumes of data, including everything from vitals to lab and imaging results. This can exacerbate already significant concerns surrounding patient consent.
- Bias in algorithms. If AI systems are trained on biased information, they may, in turn, adopt these biases. This could cause nurses to inadvertently reinforce existing disparities, perhaps misinterpreting the needs of their patients or acting on misguided recommendations.
- Human oversight. Human interpretation ultimately determines how (or to what extent) AI-powered solutions influence clinical care. According to ANA, AI can serve as a valuable supplement but should never “replace a nurse’s decision-making, judgment, critical thinking, or assessment skills.” AI-averse nurses, however, may worry about becoming overly reliant on algorithms — potentially leading to diminished clinical judgment over time.
3. Data Privacy and Security in AI for Nursing
With hundreds of millions of records exposed during high-profile breaches, it’s clear why nurses are concerned about data privacy and causing harm by exposing their patients’ private information.
In an overview of nursing perspectives on data privacy — published in the Heliyon journal — one respondent refers to nurses as patients’ digital guardians, explaining, “In the complicated world of using AI, our main worry is keeping patient information safe and private.”
- HIPAA compliance. The Health Insurance Portability and Accountability Act (HIPAA) has long played a central role in tech-supported nursing, but remaining compliant can prove even more challenging amid the rise of AI. These challenges are intensified by AI’s use of multiple sources, along with its frequent reliance on automated solutions.
- Risk of data breaches. We’ve touched on the intrinsic importance of patient privacy, but this is also crucial due to the ever-present risk of data breaches. If patient information is not stored securely, it can leave patients vulnerable. While datasets may be anonymized, powerful AI solutions are actually capable of “re-identifying” patients and may expose information that seems to be well-protected.
- Data governance and accountability. A complex web of policies governs the use of AI in healthcare, determining how data can be collected and analyzed ethically. Nurses may find it challenging to keep up with (and consistently abide by) these policies, especially as emerging technological advancements lead to evolving requirements.
- Patient trust. Like nurses, many patients may be skeptical about the use of artificial intelligence in healthcare. Transparency is key, as patients deserve to know why their information is collected and how it will be used. Even when nurses recognize the value of AI, they may struggle to convey this when discussing technological advancements with skeptical patients.
4. Tech Overwhelm
Today’s nurses are expected to adapt to a wide range of new tools, systems, and equipment. From electronic health records (EHRs) to telehealth platforms to wearable devices, these advanced solutions can deliver substantial improvements in patient care but can also feel overwhelming from the nurse’s perspective.
This phenomenon — sometimes referred to as technostress — can elevate already concerning levels of burnout, making nurses feel as if it’s impossible to keep up (or, perhaps, pointless to even try). Research suggests that, without sufficient support, technostress can lead to emotional exhaustion, causing nurses to “struggle to maintain the emotional energy required to care for critically ill patients.”
5. Lack of Exposure and Training
Some nurses are open to the idea of incorporating AI in their workflows but are simply unfamiliar with these solutions. Until recently, after all, this was not consistently incorporated in nursing education or implemented across healthcare systems.
This is beginning to change, though. Moving forward, AI will likely be a core part of nursing curricula as well as promoted through workforce training and continuing education. Already, research suggests that a significant share of programs now utilize AI, with the goal of “prepar[ing] nursing students for advancements in clinical practice.”
Overcoming AI Hesitancy in Nursing
While time may help resolve AI hesitancy at least to some extent, due to simple exposure, an intentional approach can improve adoption — ensuring that nurses are both capable of using advanced AI-powered systems and feel confident about these advanced solutions. Opportunities for supporting nurses through this journey of AI adoption include:
1. Provide Comprehensive Training
AI training is increasingly available at the college level, and a growing share of nursing programs encourage students to explore advanced systems and solutions. As studies on the obstacles to AI adoption reveal, this training must feel integrated, rather than strictly stacked onto existing requirements. Ideally, training will aid nurses in navigating AI-powered solutions accurately and ethically.
2. Emphasize Collaboration Between Humans and AI
Nursing education can alleviate concerns about AI’s influence on the healthcare job market by clarifying that the tool is meant to be strictly supplemental — not a replacement for human nurses — and that there is likewise no expectation of reducing demand for skilled clinical professionals. Fears surrounding AI and the nursing workforce can be eased, in part, by citing feedback from industry authorities such as ANA, which continue to emphasize the human element of contemporary nursing.
3. Address Ethical Concerns Directly
Ethical challenges should not feel like the elephant in the room. Nurses are well aware of these concerns, after all, and eager for answers. To that end, training efforts should acknowledge that ethical issues not only exist but can also exacerbate biases or data privacy concerns.
From there, students can explore solutions for alleviating these ethical concerns so they ultimately feel confident about leveraging AI’s various benefits without placing patients at risk. Case studies or projects enable nursing students to explore real-world scenarios in which ethical challenges can come into play, thinking deeply about how they might respond.
4. Integrate AI Gradually
Carefully paced exposures to AI can help nurses master new technologies one step at a time. Gradual integration may begin with simple tools that automate scheduling or documentation. This allows nurses to get comfortable with the idea of using AI and with basic AI-powered systems. Expanded AI integrations may help nurses take advantage of predictive analytics and AI-enabled personalization.
5. Share Success Stories
Research-backed insights can inspire confidence, but nurses may connect more authentically to anecdotal evidence, like success stories that exemplify the difficult-to-predict impacts of AI integration. This could involve discussions with current nurse leaders or informatics professionals, who can provide practical insights into how AI integrations play out on a day-to-day basis.
AI in Nursing Burnout: A Tool for Relief, Not a Burden
Burnout is a common problem in nursing and across the healthcare sector. While solutions have been proposed to limit the burden on hardworking nurses, AI is uniquely capable of reducing workloads across diverse settings and situations. Advantages include:
- Reducing repetitive tasks. From charting to patient monitoring and even triage assessment, many nursing tasks can become repetitive. These can often be streamlined via AI, which amplifies solutions such as voice charting and medication reconciliations.
- Providing decision support. Nurses often struggle to weigh competing priorities when assessing patients or developing treatment plans. AI can help them clarify symptoms and risk factors. This minimizes guesswork, in turn allowing nurses to make crucial decisions according to clinical data and evidence-based standards of care.
- Freeing up time. Expediting both decision-making and repetitive tasks, AI helps nurses free up valuable time. This is essential given rising caseloads, with busy nurses otherwise struggling to dedicate sufficient time or attention to their patients. By reducing time constraints, AI-powered solutions can help nurses feel less rushed, too, thereby limiting the potential for both errors and burnout.
The Role of Nursing Education in Preparing for AI
Nursing programs can offer an introduction to AI, detailing not only how this is incorporated in contemporary nursing practice but also shedding light on ethical concerns. Such topics are thoroughly explored in Bachelor of Science in Nursing (BSN) programs, such as the online RN to BSN program through Nevada State University, giving registered nurses an edge in today’s increasingly tech-driven healthcare sector. Areas covered in cutting-edge nursing curricula include:
- Health informatics and data management. Through tech-focused coursework, students learn to fuse nursing science with information technology (IT). Along the way, they learn how clinical information systems are organized — and how the strategic use of data-backed insights can lead to improved patient outcomes.
- Ethical and legal implications of AI in healthcare. Bioethics courses address a variety of ethical challenges, clarifying the need for patient consent and transparency, plus the potential for algorithmic bias.
- Evidence-based practice supported by AI tools. While evidence-based practice has long been a core area of focus within the BSN, today’s programs reveal how AI influences this integration of research and clinical practice. Students explore new, tech-supported opportunities for examining relevant research and selecting models that best meet patients’ needs.
- Leadership and advocacy in technology adoption. Leadership-focused courses discuss the importance of organizing and controlling diverse resources, including technological tools or systems. Throughout the entire BSN, students gain the confidence to advocate for innovative nursing solutions.
Looking Ahead: The Future of AI in Nursing
We are just beginning to understand the profound influence AI could have on nursing and the healthcare industry at large. As AI solutions continue to evolve and the nursing community adapts accordingly, expanded adoption is to be expected.
- Robotics in patient care. AI holds great potential for elevating robotic-assisted surgery, bringing greater precision and efficiency to complex procedures. Moving forward, robotic solutions may also influence infection control, endoscopy, and medication dispensing.
- AI-driven telehealth platforms. Telehealth experienced major growth during the onset of the COVID-19 pandemic, but it remains a popular option for providing quality care amid geographic or mobility concerns. AI further enhances telehealth by supporting real-time monitoring or even automating elements of the triage process.
- Virtual simulations. AI-enhanced simulations can introduce an interactive element to online nursing education — for example, boosting virtual reality (VR) solutions that immerse students in realistic scenarios where they can develop decision-making skills in low-stakes environments.
FAQs About AI in Nursing
How is AI used in nursing?
Artificial intelligence holds diverse use cases in nursing, including clinical decision support and automated documentation, promoting efficiency and personalized care.
Does AI increase or decrease nursing burnout?
When it comes to AI in nursing burnout, artificial intelligence can minimize symptoms of burnout by expediting repetitive tasks and limiting administrative burdens, thus freeing up nurses to focus on meaningful interactions with patients.
What ethical concerns exist with AI in healthcare?
AI presents ethical challenges surrounding data privacy, especially regarding transparency and patient consent. AI systems may be vulnerable to algorithmic bias, too, which can exacerbate healthcare disparities.
Will AI replace nurses?
AI will not replace nurses, as there remains a strong need for human empathy and expertise. AI plays a strictly supplemental role, freeing up time so nurses can provide high-quality care without suffering burnout.
How can nursing education prepare me for AI?
Earning a nursing education can provide a comprehensive introduction to cutting-edge AI solutions, with courses covering relevant topics such as biostatistics, informatics, and bioethics.
Embrace Tech-Forward Nursing With Nevada State University
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