Abstract
Artificial intelligence revolutionizes nursing informatics and healthcare by enhancing patient outcomes and healthcare access while streamlining nursing workflow. These advancements, while promising, have sparked debates on traditional nursing ethics like patient data handling and implicit bias. The key to unlocking the next frontier in holistic nursing care lies in nurses navigating the delicate balance between artificial intelligence and the core values of empathy and compassion. Mindful utilization of artificial intelligence coupled with an unwavering ethical commitment by nurses may transform the very essence of nursing.
Keywords
Introduction
Technological advancements are essential to advancing quality patient care in an ever-evolving healthcare landscape. Nursing informatics (NI), a dynamic and interdisciplinary field that merges data analytics with the healing touch of human caring, is at the heart of healthcare’s adoption of new tech. 1 This specialized field is the central hub of nursing, computer, and information science to streamline data management and facilitate communication. As technology advances, the integration of AI in nursing has spearheaded the modernization of healthcare delivery by influencing digital innovations and pathways in patient teaching, monitoring, diagnosing, and treatments. 1 With these advancements comes an ethical charge for nurses, as AI use in NI demands an exploration of the moral and ethical implications to preserve patient safety, promote quality of care, and uphold the profession’s integrity.
The intersection of nursing informatics and AI technology
The history of NI in practice dates back to the early 1970s and is marked by continual evolution – from computerized patient records and telehealth to clinical decision support systems and AI.1,2 The inspiration for these innovations is often to enhance patient outcomes and streamline clinical workflows. As NI embraces AI and other powerful technologies, it is crucial to ensure these advancements are ethically sound, prioritizing patient well-being, transparency, and human connection. 3 Exploring the potential benefits and challenges of ethical considerations inherent in AI integration within NI serves to (a) guide responsible implementation and (b) ensure new technological procedures benefit the patients, their nurses, and the healthcare community.
Background: Technology in nursing informatics foundational concepts and examples
Several basic aspects of AI and examples of their integration into NI must be first conceptualized in order to appreciate the discussion of ethical implications within NI fully.
Understanding artificial narrow intelligence
Artificial Narrow Intelligence (ANI), also known as weak AI, is designed to perform specific tasks efficiently. It utilizes both hardware and software to achieve a particular goal but is unable to perform tasks beyond its programmed area. Examples of ANI include virtual assistants or systems that provide recommendations based on internet browsing history. In the field of nursing, one use of ANI is for patient medication management to (a) flag potential drug interactions and (b) send alerts for late medications. 4
The promise of artificial general intelligence
Artificial general intelligence (AGI), also known as strong AI, is designed to learn and comprehend a wide range of problems. It utilizes past experiences and data to adapt to unforeseen situations. It is designed to be capable of autonomous problem-solving and can integrate information from diverse domains. Experts in the field believe AGI to have the potential for self-awareness. However, currently, strong AI currently remains merely a promising theoretical concept. 4 In nursing, an example of AGI would be the fabled Nurse Robot, which is capable of fully replacing nurses at the bedside in all aspects of patient care.
Machine learning and predictive models in healthcare
Machine learning is a technique used in ANI and is a broad field that works to enable computers to perform tasks without explicit instructions. It uses large data sets to form algorithms identifying relationships and patterns within the data. Often, it is conceptualized as teaching computers how to learn and make decisions based on data. 4
Prediction models are a product of machine learning; they use data input to detect errors early on based on identified patterns and algorithms. 4 An example of this in nursing would be alerts in the patient charts within an electronic health record (EHR), which indicates an early warning system for patient value trends such as identifying high fall-risk patients.
Expert systems and clinical decision support
Expert systems are designed based on human input with the intent that the computer system will mimic the human specialist using their expert knowledge. 4 One example is a sepsis early warning system, where medical expertise was used concerning the stages of systemic inflammatory response syndrome (SIRS) and multiple organ dysfunction syndrome (MODS) to create a system that helps (a) prioritize treatment methods, (b) improve provider recognition and (c) increase consistency in treatments and response times. 5
Natural language processing
Natural language processing (NLP) is achieved through statistical analysis of patterns found in language. It is considered a branch of AI and machine learning and is used to help develop algorithms for identifying patterns, main topics, and similarities. 4 An example of this in nursing is ambient listening technology which physicians can use on their phone while speaking with patients to fully automate their progress note documentation. 6
The role of fuzzy logic
Fuzzy logic refers to algorithms that must process imprecise data and produce reasoning. In other words, these algorithms are designed to navigate the gray areas where nurses often rely on their experience and instincts. 4 In nursing, often used in decision support, these methods include weighted categories to produce a decision path. According to Al-Dmour et al., one example is the treatment of patients with diabetes, in which the influence of prandial and post-prandial blood glucose levels along with predicted insulin resistance and anticipated response to the composition of the carbohydrates, fats, and proteins all influence treatment and patient outcomes. 7
Deep learning: Neural networks in nursing
Deep learning indicates data patterns formed through mathematically designed neural networks that operate in a semi-supervised setting. 4 It consists of an input layer, a hidden layer, and an output layer. We see this in wearable devices, remote patient monitoring, and customized patient education recommendations.8–11 One common concern about deep learning is our inability to supervise information processing and how it evolves. The unknown period is often called the “black box” due to the lack of transparency in algorithms. From our perspective, data goes in (input), is processed in the hidden layer, and then the product or response (output) emerges.2,12
Generative AI: The next step in healthcare technology
Generative AI models, such as OpenAI’s Chat GPT, Anthropic’s Claude, Google’s Bard, and Microsoft’s latest version of Bing, have gained widespread attention. The attention is partly due to their remarkable ability to respond to prompts quickly and efficiently…almost like a human. Although these advancements are still considered a form of ANI, they represent a significant step towards the development of AGI. 2
The ethical challenges of AI in nursing
In this section, we explore the ethical challenges of implementing advanced technology like ANI in nursing. These technologies have the potential to improve significantly how nurses care for patients by making healthcare more efficient.1,2 However, as nurses embrace these tools, we must be mindful of the complex ethical issues they present. One of the main concerns is the possibility that ANI could unintentionally favor certain groups over others, leading to unfair treatment in healthcare settings. In addition, how these AI systems make decisions is often unclear, making it hard to trust that they are reliable and fair. It is essential that nurses, who are often the closest healthcare providers to patients, use AI to help them in their work, not to take over their role in providing care and comfort. While it is true that AI can do a lot of good—for example, by taking over routine tasks so nurses can concentrate on their patients—it is also true that we cannot ignore the moral questions that come with it. Nurses must ensure that AI is used safely, protecting patients’ private information and not introducing bias.
As such, the importance of caring for each patient as a whole person becomes increasingly apparent as the core of nursing. As we move forward with AI, it is crucial to remember the values central to nursing—compassion, fairness, and respect for each person’s dignity. By keeping these values in mind, we can use AI to support the work of nurses and improve patient care without losing the human touch essential to healing.
Bias and transparency in AI algorithms
While ANI promises substantial advancements in healthcare, its integration into nursing practice does raise several complex ethical concerns. These concerns revolve around potential biases within ANI algorithms, which could exacerbate healthcare inequalities and unfairly disadvantage specific patient populations. Transparency and accountability also remain crucial considerations, as the inner workings of AI algorithms often lack transparency, making it difficult to assess their fairness and reliability. 1
Empathy and connection in the age of AI
Along those lines, the potential for ANI or AGI to evolve to a point where it is used as a substitute for human interaction in patient care raises concerns about the erosion of empathy and human connection, fundamental tenets of nursing practice. As trusted patient advocates, nurses must ensure that AI is used to support, not replace, human judgment and compassion.
Nevertheless, dismissing AI solely for its ethical complexities may be unwise as it ignores the many potential benefits it offers. 13 Medical professionals already utilize AI-powered tools for various tasks, including medical imaging analysis, disease prediction, improving documentation efficiency, alerting providers of specific patient conditions and patterns, and drug discovery. For instance, hospitals increasingly employ AI-powered chatbots to answer patient queries and provide basic medical information, freeing nurses to focus on complex cases and direct patient care.13,14
Therefore, viewing the recent development of generative AI technologies, like ChatGPT, not as a revolutionary disruption but rather as the latest chapter in the ongoing evolution of nursing technology is crucial. While these advancements offer exciting possibilities, ignoring the ethical implications associated with their implementation could prove fatal. 1
Balancing innovation with integrity
Integrating ANI 15 into NI enhances patient outcomes and nursing workflows and allows nurses to combine compassionate care with technology. 16 AI-driven technologies such as predictive analytics, virtual assistants, and machine learning are being utilized for the early detection of health issues, personalized care, and patient education. These advancements can potentially address challenges like staffing shortages, nurse burnout, and patient safety.17,18
However, the integration of AI also raises complex ethical considerations. Key concerns include the principle of nonmaleficence in handling sensitive patient data, the risk of implicit bias affecting the principle of justice, and overreliance on AI, potentially hindering nursing students’ critical thinking development.13,18 In these dilemmas, the principle of caring ethics emerges as a sturdy bridge between technological advances and the heart of nursing. 19 Nurses are encouraged to recognize and value the interdependence of individuals, the moral significance of their care, their nurturing, and their understanding of human relationships. We emphasize a need to preserve human connection in patient care and issue a call to balance the scientific advantages of AI with the empathetic essence of nursing, rooted in caring ethics. 20
The theoretical reasoning: AI from a utilitarian viewpoint on Nursing Informatics
As a specialized field, NI can potentially unleash exponential advancements for the profession and those we serve. 3 Utilitarianism is a philosophical approach that emphasizes the greatest good for the most significant number of people. The principle often summarizes that the best action is the one that maximizes utility, and in the world of AI, the potential for benefits is far-reaching and plentiful.
Benefits: Enhancing access and care through AI
One area in which the greatest good for the greatest number may be seen is access to care. Already, AI-driven predictive analytics can facilitate early identification of patient deterioration, despite issues of distance or staffing, leading to timely interventions and lower mortality rates. 21 Machine learning algorithms analyze vast datasets, generating evidence-based insights that can bolster clinical decision-making and ensure personalized patient care through electronic health records. 22 In numerous organizations, virtual assistants and AI-powered chatbots actively engage and educate patients, fostering health literacy and empowering them in their healthcare journey. 18 Likewise, telemedicine, powered by AI, has expanded access to healthcare in remote areas, bridging the gap between urban and rural healthcare disparities. 23 Moreover, integrating this cutting-edge technology has the added benefit of streamlining administrative tasks, allowing nurses to channel their efforts into delivering compassionate care and nurturing robust patient-nurse relationships.
Benefits: a powerful partnership between nurses and AI
The incorporation of ANI into NI holds promise for transforming patient care and addresses critical challenges healthcare professionals face. 3 Short staffing in hospitals could be mitigated through AI-powered tools that optimize task allocation and workflow management. 4 By automating routine administrative tasks, nurses could focus more on direct patient care, possibly improving patient safety. 12 AI’s ability to analyze vast amounts of patient data enables early detection of potential health issues, allowing for timely interventions and ultimately the potential for mitigating some of the chronic stressors contributing to nursing burnout.24,25 Integrating AI as a supportive ally in nursing practice not only enhances efficiency and patient outcomes but also potentially fosters a nurturing work environment, protecting the well-being and resilience of nursing professionals.24,25 Overall, the potential for benefits with AI incorporation in NI appears boundless and transcends many literacy issues, resources, and workload.3,4
Minimizing complications: The theory and values behind the greatest good
However, as with any cutting-edge technological advancement, introducing AI into NI leads to intricate ethical dilemmas that require persistent scrutiny and thoughtful consideration. Nurses must grapple with ethical principles such as nonmaleficence, veracity, stewardship, justice, and autonomy when making informed decisions about AI usage within their practice. By diligently addressing these multifaceted ethical aspects, nurses can leverage the latest technology in a manner that is both responsible and effective, ensuring they harness the full benefits without compromising their ethical obligations.
Nonmaleficence in AI: prioritizing patient harm prevention
One primary example is the use of patient data.19,26 The ethical principle of nonmaleficence is the obligation to do no harm and prevent harm to others. In the context of patient data, nurses need to handle this information responsibly to avoid potential patient harm. Sharing or disclosing patient information without proper authorization can lead to privacy breaches, causing emotional distress and potentially harming patients. Nonmaleficence requires nurses to diligently safeguard patient data from unauthorized access, use, or disclosure.
Veracity and stewardship: managing patient data ethically
In the context of patient data, the ethical principles of veracity and stewardship are also paramount. Veracity refers to conveying the truth, being honest, and maintaining accuracy in statements and actions. Conversely, stewardship involves the responsible management of resources, particularly patient data, emphasizing ethical integrity and sustainability. Such data are essential in decision-making and treatment planning, and their integrity is crucial for the efficacy of integration into clinical informatics. 3 As ANI algorithms depend on the quality of information they are fed, any inaccuracies, irrelevant data, or biases can lead to erroneous clinical decisions, adversely affecting patients’ health outcomes. 4 This circumstance both threatens the ethical principle of nonmaleficence and undermines the concept of veracity by distorting the information’s truthfulness. Thus, nurses are entrusted with the responsibility of stewarding patient data carefully. This involves diligently verifying the accuracy of patient data, critically assessing for biases, and using only relevant and truthful information in their care plans. Nurses uphold these ethical principles by acting as conscientious protectors of this sensitive information, ensuring the responsible integration of ANI into NI. 26
Justice and equity: Mitigating bias in AI algorithms
A further critical concern is the potential for implicit bias within AI systems, an issue directly tied to the ethical principle of justice, which demands the fair and equitable treatment of all individuals without discrimination. This bias can take various forms in ANI, such as favoring specific algorithms or exhibiting preferences in text and image generation, language translation, and the representation of culturally specific nuances. 4 Given the existing and widespread apprehensions regarding bias within healthcare, the biases present within AI could exacerbate current disparities or introduce new inequalities in patient care. 26 In addition, some AI models are equipped with functions to offer medical advice when prompted to act as a nurse despite the accuracy or relevance of their non-medical training to patient questions. Because they do not have access to licensed health professionals, individuals who turn to this for healthcare advice may misdiagnose or mistreat their symptoms or those of their family members. Recognizing this, nursing professionals are called upon to actively identify and mitigate any bias in integrating AI into NI. In committing to these efforts, they affirm and uphold the principle of justice, reinforcing the commitment to equitable and impartial treatment for all patients, regardless of their unique backgrounds or characteristics.
Autonomy: Empowering nurses alongside AI
A growing concern among nursing educators is the increasing reliance of nurses, from novice to expert, on ANI for tasks that require critical thinking and clinical reasoning; this dependence could stifle their professional development.17,21,27–29 From an ethical perspective, this issue relates to the principle of autonomy. 19 This principle emphasizes individuals’ right to make informed, independent decisions. In nursing education, nurses need to develop their critical thinking abilities and judgment, honing their capacity to make decisions independently. More reliance on ANI might lead to an erosion of this autonomy, causing nurses to become excessively dependent on technology for their decision-making and problem-solving tasks. Likewise, even for expert nurses, adopting ANI technologies in healthcare is accompanied by a steep learning curve, necessitating ongoing training and education for healthcare professionals to promote safe and responsible use. Recognizing this potential pitfall, nurse educators must guide their nurses in striking a balance between leveraging ANI as a valuable tool and nurturing their innate clinical reasoning skills. By actively fostering autonomy within their nurses, educators help to shape confident and capable professionals who are well-equipped to make independent and informed decisions in the complex realm of healthcare.30–32
From theory to practice: Real-world implications of AI in nursing informatics
Although ANI is paramount to advancing the technological side of healthcare, it is far from replacing nurses at the bedside. One main reason for this can be found in the American Nurses Association’s Code of Ethics. 33 The provisions upheld as standards for registered nurses are essential to preserving human dignity and the healing connections provided through compassionate care from another person. These provisions address crucial aspects of patient care from the ethical and moral responsibilities of a nurse, including (a) relationships with patients, (b) conflict of interest, (c) professional responsibility in patient safety, (d) accountability and responsibility for patient outcomes, (d) maintaining competency, and (e) ethical obligations to the patient population. Whereas ANI may be able to offer advice to patients and even simulate empathetic responses at this time, they cannot replace the ethical safeguards of human intuition and genuine care. 32 The future of ANI (and potentially AGI) in nursing holds tremendous promise but is contingent on responsible implementation, stakeholder collaboration, and adherence to ethical principles.
Case studies in nursing informatics
Several examples of ANI integration into NI suggest that there is room for development. Predictive models for sepsis in NI were tested with high accuracy during the trial phase and yet were found to miss 67% of actual cases of sepsis in real-time patients in the field after integration.4,34 In another case, measurement bias went undiscovered when male data was used to create algorithms for patients whose symptoms may indicate a myocardial infarction (MI). Yet, women experience MI symptoms differently, and reliance on these algorithms led to missed diagnoses in women.4,35 Furthermore, while studies have shown that (a) nursing professionals believe that Chatbots could help support their patient’s mental health and (b) many patients are interested in using them, it has also been found that (c) many Chatbots do not consistently provide mental health resources; instead opting to respond with unhelpful and risky responses in highly sensitive cases such as victims of abuse or rape, intention to harm others, and intention for self-injury.12,36,37
In other cases, user discussion with ANI has been implicated in actually facilitating both suicide and divorce. In one memorable instance, a father of two discussed his suicidal ideations extensively with a generative AI chatbot for 6 weeks. He found encouragement in sacrificing himself in the name of population reduction for environmental sustainability. 12 Another case reported a woman engaged in an affair seeking professional advice on whether to end her marriage. She received encouragement from the ANI on a favorable outcome and acted on the advice, ultimately crediting the ANI for her decision. 12 These examples emphasize that ANI in clinical informatics must be seen as a tool and requires nursing knowledge, expertise, and involvement to safeguard patients during its development and implementation into healthcare. 38
The vision: caring ethics and AI collaboration in patient-centered care
As NI increasingly incorporates ANI, concern for the risk of depersonalizing patient care has also emerged. 38 Compassion and caring are central to nursing, reflecting not just a clinical approach but an ethical one grounded in caring ethics.14,19 Patient-centered care cannot exist independently of that human touch, which acknowledges the emotional and relational aspects of healing. The main challenge that will arise with the ongoing development and use of ANI in NI is the time-honored tradition of blending the science and art of healing into one seamless practice while preserving the values of empathy, understanding, and compassion that are integral to caring ethics. Genuine, empathetic connections with patients in their times of need remain vital to the nursing profession.8,14 Likewise, patient autonomy, cultural backgrounds, and ethical concerns, including stewardship, veracity, and nonmaleficence, must stay at the forefront of nurse considerations when incorporating ANI into the care of their patients. By aligning technology with caring ethics, nurses can ensure that patient-centered care remains just that, balancing innovation with the deeply human elements of nursing practice.14,19
In this context, caring ethics is a theory that emphasizes holistic patient care and the emotional connections that bind people together. In caring ethics, it is essential to direct nursing energy and efforts to understanding and responding to others’ unique situations, needs, and feelings. Nurses are encouraged to recognize and value the interdependence of individuals, the moral significance of their care, their nurturing, and their understanding of human relationships. When coupled with the advances in NI, this lens helps focus nursing care on the importance of empathy, attentiveness, trust, and individualized care that recognizes the whole person, not just their medical condition. Holistic nursing care supports the idea that nursing actions demonstrate clinical competence, compassion, patience, and the ability to connect with patients on a human level. 19
Conclusion
The integration of ANI into NI has the potential to improve the efficiency of care, but it also raises ethical questions and the potential for missing critical aspects of both nursing cognition and nursing care. AI-driven predictive analytics, virtual assistants, and machine learning algorithms, to name only a few, have already demonstrated their potential to improve patient outcomes and promote health literacy.3,10,14 Along with these promising advancements, ethical challenges demand careful consideration. Nurses must navigate the delicate balance of using patient data responsibly, safeguarding privacy, and ensuring data accuracy and relevance while upholding the ethical principles of nonmaleficence and justice. Moreover, implicit bias in ANI systems raises concerns about the principle of justice, necessitating ongoing efforts to identify and mitigate biases to ensure equitable patient care. 7
Charting a responsible path forward
As NI evolves, the principles of caring ethics become a crucial bridge between technological innovation and the timeless values of nursing. Nurse educators must address the risk of overreliance on ANI among students and novice nurses, fostering their autonomy and critical thinking abilities while instilling empathy, compassion, and attentiveness central to caring ethics. Preserving the human connection in AI-driven nursing is paramount, as these qualities remain integral to patient-centered care. 8 Combining the science of ANI with the art of nursing—and the ethical framework of caring ethics—requires understanding how ANI can enhance but not replace the essence of nursing practice.14,18 By embracing ANI technology responsibly and maintaining the core principles of nursing intertwined with caring ethics, nurses can continue to provide compassionate, patient-centered care that makes a difference in the lives of those they serve.
Footnotes
Declaration of conflicting interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
