Background
The current world is facing change due to the boom in innovation brought about by the development of new technologies. The technologies have affected our lives in all aspects and have impacted on workplace, schools, and homes. Advancements in technology have been seen in the healthcare sector as well. The development of new technology in nursing aims to improve healthcare quality and patient outcomes. There have been concerns about human errors and inefficiency in surgery and intensive care interventions in health processes. Artificial intelligence has been developed to make work easier and deal with challenges in healthcare processes. Artificial intelligence has a purpose in using computers to help solve healthcare problems and issues. This paper aims to discuss artificial intelligence as an emerging technology in nursing.
Overview and Roles of AI in nursing
Innovative technology leads to more accurate and efficient solutions to healthcare problems. Health care could benefit from artificial intelligence (AI), which can help clinicians make better decisions and reduce the likelihood of avoidable scenarios. Learning algorithms are expected to significantly impact healthcare services, such as diagnostic techniques, therapies, and care processes. Using artificial intelligence to evaluate data to diagnose diseases like diabetes, Alzheimer’s, and various forms of cancer is becoming increasingly important in health care. Biomarkers for conditions such as cancer and diabetes could be discovered earlier in the screening process with the help of artificial intelligence (Mikdadi et al., 2022). Next-generation radiological instruments are being created with the use of artificial intelligence. Brain-computer interfaces may help people facing a terminal disease get more out of their final months and years in hospitals. Deep learning models can predict the effectiveness of illnesses and existing medications and therapies for conditions.
Project InnerEye from Microsoft is a radiotherapy AI tool that allows the patient’s 3D contouring procedure takes minutes rather than hours. Because of this, cancer can be detected faster, and the best treatment options can be determined for a particular patient (Sharma et al., 2021). Project Hanover, an AI system produced by Microsoft, aims to catalogue biomedical research papers by PubMed. Researchers have been involved in machine learning to track new trends in medical health and mental issues.
It is possible to examine hundreds of Reddit messages using an AI model and conclude that suicide intentions and feelings of loneliness had increased significantly over time. People’s mental health could be radically re-evaluated because of this. Telemedicine can be made more effective using chatbots. Chatbots combine with artificial intelligence to create a virtual interventional radiologist (VIR). This tech was designed to help both patients and doctors diagnose themselves. In addition, they can help patients gather information before effective treatment.
Ethical or Legal Issues
There are several ethical considerations associated with the use of AI in Medicare. First, there are security and privacy concerns. The AI technology should adhere to security and privacy requirements concerning patient data and information as it accesses massive amounts of health information that is supposed to be protected. The second ethical consideration is the safety and reliability of the AI technology, which may be involved in sensitive activities such as medical diagnosis. The AI needs to improve the outcome for the patient, and additional research is needed on the use of technology as it develops. The third ethical issue is inclusivity and fairness, where the tech should treat patent data in fair and balanced ways (Mitchell & Kan, 2019). Engineers, coders, and developers should incorporate inclusivity to eliminate bias. The fourth ethical considerations concern the accountability and transparency of Individuals who deploy, design, and use the systems. When the AI is involved in sensitive decision-making related to health care, it should be accountable and transparent to clinicians, patients and researchers.
Informatics Nurse’s Role Concerning Artificial Intelligence
The role of the informatics nurse is critical to the successful delivery of care and the development of innovative models. Using artificial intelligence (AI) in health care illustrates the creative ways physicians and organizations use to improve their patients’ quality of treatment outcomes. As a result, informatics nurses are experts who advise businesses and aid in adopting AI-based technologies to improve treatment and service quality (Strudwick et al., 2019). When hospitals use artificial intelligence, nurse informatics specialists are responsible for ensuring they are adhering to legal and ethical criteria for preserving patient data. A nurse’s informatics primary responsibility is to ensure that AI is used following established goals and objectives by providing superior software and hardware through the most appropriate channels.
Workflow Analysis, User-centered Design, and Human Factors Concepts Roles
To improve efficiency in operations, workflow analysis evaluates an organization’s processes. It highlights areas for process improvement, such as redundant or inefficient jobs or procedures, inefficient workplace layouts, and process bottlenecks (Chokshi & Mann, 2018). A thorough assessment of healthcare workflows is necessary to help hospitals and medical facilities improve efficiency. Business users can analyze processes on a task-by-task basis to maximize efficiency and productivity in the workplace.
Suppliers can identify potential AI application areas by examining the workflow. An institution’s ability to integrate new technologies to improve care delivery is dependent on factors such as the availability of human resources. According to the user-centered design philosophy, patients should benefit from AI, not just healthcare practitioners. The ultimate goal should be to maximize the benefits for all stakeholders by integrating AI capabilities into the healthcare system and network.
References
Chokshi, S. K., & Mann, D. M. (2018). Innovating from within: A process model for user-centered digital development in academic medical centers. JMIR human factors, 5(4), e11048.
Mikdadi, D., O’Connell, K. A., Meacham, P. J., Dugan, M. A., Ojiere, M. O., Carlson, T. B., & Klenk, J. A. (2022). Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery. Cancer Biomarkers, 33(2), 173-184.
Mitchell, M., & Kan, L. (2019). Digital technology and the future of health systems. Health Systems & Reform, 5(2), 113-120.
Sharma, A., Singh, P., & Dar, G. (2021). Artificial Intelligence and Machine Learning for Healthcare Solutions. Data Analytics in Bioinformatics: A Machine Learning Perspective, 281-291.
Strudwick, G., Nagle, L., Kassam, I., Pahwa, M., & Sequeira, L. (2019). Informatics competencies for nurse leaders: A scoping review. JONA: The Journal of Nursing Administration, 49(6), 323-330.