Artificial Intelligence (AI) in healthcare refers to the ability of computers to function independently in transforming clinical data into knowledge that can be used as a guide in decision-making or for autonomous actions. In recent years, AI technology has gained relevance in healthcare following its major contributions to the industry. Nursing is a major beneficiary of AI technology as there are multiple computer software programs that nurses can use to interpret data and learn from what the data reveals to inform clinical and operational decision-making (Douthit et al., 2022). The data is reliable for the nurses as it uses computational algorithms with the electronic health record (EHR) to source data. Using AI, nurses’ core practices, such as assessment, planning, and outcome evaluation, help them deliver the best possible care. As a result, there has been evidence AI has become an invaluable tool in improving patient outcomes and enhancing healthcare practices. This essay will explore the several impacts AI has had in nursing, including developing protocols being used in nursing, influencing innovations in nursing and having had an impact on the ethical and legal considerations of AI in nursing.
Supporting protocols used in nursing is a major impact AI has had. One of the ways through which AI supports these protocols is the development of clinical decision support systems. These systems use tools such as alerts in the EHR, dashboards, order sets, clinical practice guidelines and reports. The systems integrate patient information, medical literature, and best practices to offer real-time guidance. Based on the data available, the clinical decision support systems use the relevant tools to provide nurses with evidence-based predictions or suggestions to make decisions that would have otherwise been challenging to develop, relying on the human capacity (Douthit et al., 2022). Some of the contributions AI make in decision-making is helping accurately identify at-risk patients by considering more diverse patient information from the EHR and other information sources. Also, the systems may provide a nurse with information about a patient’s potential drug interactions, recommend appropriate diagnostic tests based on symptoms, and offer guidance on the right treatment protocols based on the patient’s specific conditions. Notably, healthcare organizations have adopted clinical decision support systems supported by AI I guiding nursing in decision making because they can quickly consider large data volumes in the risk prediction due to the AI automated adjustments that can select desired variables that need to be used a time, and also the AI’s increased intervention specificity makes it advantageous. These advantages of AI eliminate the risk of making uninformed decisions resulting from human error and reduce the time used in analyzing patient data.
Another way AI supports protocols used in nursing is by supporting predictive analytics. Predictive analytics is an important part of nursing as it uses patient data to identify patterns and trends, enabling nurses to anticipate and intervene in potential health complications. AI makes analyzing data easier due to its ability to analyze large datasets, such as reviewing patients’ medical history, looking into vital signs that a patient shows, and accurately evaluating a patient’s lab results. AI in predictive analytics matters because it allows early identification of at-risk patients developing certain conditions or complications. The results from the predictive analytics supported by the AI allow timely interventions and preventive measures to improve patient outcomes before the identified problem worsens. Also, it leads to nurses making informed decisions on the best patient care. The AI provides comprehensive insights that are evidence-based (Robert, 2019). Also, using AI for predictive analytics helps nurses optimize resources. The insights nurses gain help them allocate these resources more efficiently as they understand the resources the patient will need most and also account for the demand for specific services they identify. As a result, relying on AI for predictive analytics enhances improved patient safety because the data obtained reveal potential risks a patient is likely to suffer from, allowing the implementation of fall prevention protocols that will minimize risks to the patient and promote patient safety.
The third example in which AI supports protocols being used in nursing supports Natural Language Processing (NLP). AI in NLP has significantly impacted nursing by coordinating the interaction between computers and human language. One of the impacts of AI-powering NLP in nursing is facilitating clinical documentation. Typically, automating tasks by AI’s NLP technologies helps convert unstructured data into structured formats by extracting relevant information from clinical notes, dictated reports, nursing narratives and other textual data (Douthit et al., 2022). AI-powered NLP is also medical image analysis. The AI can extract information from medical images and radiology reports and then present the information in textual form, helping in faster and more accurate diagnosis. Also, it facilitates patient engagement and support as chatbots and virtual assistants with NLP capabilities can understand and respond to patient queries. In this case, AI-powered NLP enables these virtual assistants to interpret and generate human-like responses, improving access to healthcare information. These benefits nurses by ensuring they can attend to patients in person, where alternatives could be used.
The other way AI has impacted nursing is through influencing innovations in nursing. The AI has supported piloted innovations at the trial phase facilitating the gathering of adequate feedback on innovation before its implementation. It has also supported in-progress innovations to enhance outcomes of the innovations underway for success. An example of innovations AI has supported in nursing is the development of robotics in healthcare. Robotic technologies are being increasingly integrated into nursing practice to provide care companions. Also, robotics have to create used in developing remote-controlled tools, such as telepresence robots that offer face-to-face patient care where nurses can use a video or voice application to instruct the robot to deliver care (Douthit et al., 2022). For example, nurses use robot-assisted surgery for precise but minimally invasive procedures, reducing patient trauma and recovery time. Some other tasks robots assist nurses in include administering medication and monitoring patients. Physical therapy is another way robots have helped nurses as robotic companions can provide emotional support to patients in long-term care settings, reducing social isolation. Also, AI has supported the development of robotics that can offer online interactions with patients remotely. This has been increasingly essential to nurses in reducing face-to-face interactions with patients since the Covid-19 pandemic.
Another example of innovations AI has supported in nursing is the development of Virtual Reality (VR) and Augmented Reality (AR). AI has enhanced AR technologies to provide nurses with the best experience. VR is a computer-generated simulation that creates a fully immersive digital environment isolating the user from the physical world. AR overlays digital information onto the real-world environment to help a user perceive reality (Ng et al., 2022). Ways in which AI-supported VR and AR technologies have been useful include enhancing simulation-based training. Nurse students are exposed to realistic patient scenarios, medical conditions, and responses to enhance their clinical practice skills and improve their critical thinking for better decision-making in a safe and controlled setting. They have also been helpful to nurses by providing telehealth to assist patients in remote areas. BY OFFERING CLINICAL DECISION SUPPORT, AI-powered AR and VR technologies have also been crucial to nurses. Nurses can receive real-time guidance when making clinical decisions for improved patient outcomes. Additionally, nurses utilize VR and AR technologies supported by AI to distract patients during painful procedures and create educational simulations to enhance patient understanding of their conditions and treatment plans.
Also, AI has supported innovation within the nursing field by developing wearable devices and remote monitoring. With technological advancement, wearable devices, such as fitness trackers and smartwatches, have become increasingly popular and integrated into nursing care as they are being used as sensor-based technologies. The devices are placed in the home or hospital environment where the patient is to measure body movement, collect weight, monitor movement, and collect environmental data such as temperature, light, sound, and air quality (Robert, 2019). They also collect real-time health data on patients’ heart rates, sleep patterns, and activity levels, encouraging patients to engage in self-care. This information is useful to nurses as they can monitor patients remotely and identify the need for additional care. Also, remote monitoring helps nurses to detect deteriorating health conditions as early as possible for intervention.
AI also has impacted the ethical and legal considerations in AI in nursing. The increasing use of AI raises questions regarding the ethical and legal considerations nurses should hold in their line of work. An example of an ethical consideration triggered by the use of AI in nursing is the issue of privacy and confidentiality. AI in nursing involves collecting, storing, and analyzing sensitive patient data for analysis using the AI tool. Normally, nurses are supposed to ensure the privacy and confidentiality of patient data such that it should not be accessible by unauthorized individuals (Ng et al., 2022). However, using AI increases the need for nurses to foresee patient data privacy and security for confidentiality and protect patient rights. Some of the ethical considerations nurses should ensure to ensure data privacy are obtaining informed consent from the patient to use their data on AI systems, anonymizing data we’re sharing for analyses, and ensuring AI systems have adequate security measures for protection from the risk of a data breach.
The use of AI in nursing has also created the need to address laws and regulations governing the use of AI. As much as AI is gaining popularity in nursing, nurses must adhere to laws and regulations governing data protection laws. Data protection laws focus on ensuring patients’ data remains confidential even when used on AI systems for analyses. Another aspect is informed consent requirements when using any AI interventions on the patients, such as AI robot-assisted surgery, to ensure a patient understands the concept of AI in improving health outcomes. Nurses should also openly communicate the potential risks of using AI systems for treatment intervention so patients make informed decisions (Stokes & Palmer, 2020). The other aspect that the legal laws and regulations regarding the use of AI in nursing emphasize is the liability issue. This involves the proper allocation of responsibilities so that developers of the AI system used in nursing focus on looking out for problems affecting the intended use of an AI system and addressing the issue.
Another ethical consideration influenced by the use of AI in nursing is the need to ensure the safe and effective use of AI. This reminds nurses that safe and effective implementation of AI in nursing should adhere to standards and guidelines specific to AI technologies need to be established. One of the issues nurses must foresee is AI transparency such that also patients have access to information about the AI systems used in healthcare. The other aspect of abiding by is explainability, where nurses ensure they understand AI-based decisions and can explain the rationale to patients (Robert, 2019). The other aspect is accountability which requires nurses to understand their roles in interpreting AI-generated outputs.
In summary, using AI in nursing has positively impacted the healthcare field. Beginning with enhancing nursing protocols, such as clinical decision support systems, predictive analytics, and natural language processing, nurses can make evidence-based decisions and provide more personalized care to patients. Also, the use of AI in nursing enhances ongoing innovations, and those being tested, such as robotics in healthcare, VR and AR, and wearable devices, are helping I offering nursing students simulation-based learning, enabling remote monitoring and providing nurses with valuable insights from textual data. However, AI in nursing calls for careful consideration of ethical and legal considerations such as informed consent, data protection and security. At the same time, nurses must adhere to AI technologies’ transparency, accountability, and explainability standards and guidelines.
References
Douthit, B. J., Shaw, R. J., Lytle, K. S., Richesson, R. L., & Cary, M. P. (2022). Artificial intelligence in nursing. American Nurse. https://www.myamericannurse.com/ai-artificial-intelligence-in-nursing/
Ng, Z. Q. P., Ling, L. Y. J., Chew, H. S. J., & Lau, Y. (2022). The role of artificial intelligence in enhancing clinical nursing care: A scoping review. Journal of nursing management, 30(8), 3654-3674. https://doi.org/10.1111/jonm.13425
Robert, N. (2019). How artificial intelligence is changing nursing. Nursing Management, 50(9), 3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7597764/
Stokes, F., & Palmer, A. (2020). Artificial intelligence and robotics in nursing: ethics of caring as a guide to dividing tasks between AI and humans. Nursing Philosophy, 21(4), e12306. https://doi.org/10.1111/nup.12306
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