Evidence-Based Proposal and Annotated Bibliography on Technology in Nursing
Learner’s Full Name
Capella University of Health and Sciences
FPX4040: Managing Health Info & Tech
Professor’s Name
Month Year
Health care is rapidly changing with advanced artificial intelligence provisions for data analysis, diagnosis, and patient management tools. AI technologies are particularly important in the field of nursing, where these technologies can be put into practice in monitoring patient workflow management, and ensuring high standards of care and safety. This assessment will describe integration of AI into nursing will maintain for efficiency and accuracy, making health professionals spend more time with the patient rather than behind a computer doing administrative work.
Introduction of Chosen Technology topic
I choose this topic because AI in healthcare is a broad area of application, these technologies process huge amounts of information quickly and accurately, providing insights with potentially beneficial effects on the outcome of this or that patient. According to a report, AI technologies can reduce healthcare expenditure by 50% while increasing access to healthcare facilities and in some medical areas, diagnostic accuracy rates over 90% could be achievable using AI algorithms (IBM Education, 2024). AI can help nurses in making patient complications easier to detect at an early stage, facilitate routine tasks, and even support clinical decision-making. I have used credible databases like PubMed and Google Scholar with keywords such as “Artificial Intelligence”, “Healthcare Efficiency”, and “Patient Care”. I reviewed current data and studies from credible sources to understand the ways in which artificial intelligence could currently affect and make a difference in nursing practice and healthcare.
Annotated Bibliography
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021). The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4–15. https://doi.org/10.1093/bmb/ldab016
This paper focuses on existing applications in diagnosing capabilities, predictive analytics, and personalized medicine, to shed a better light on AI’s future in healthcare. Authors highlight the immense potential of AI, enhanced diagnostic accuracy, and patient outcomes despite, at the same time, outlining some challenges such as data privacy and the need for regulatory frameworks with some challenges, like data privacy and the need for regulatory frameworks, but, at the same time, outline that with proper care in their implementation, it will enhance benefits to the maximum level and reduce risks related to misuse. Secondly it highlights how AI could change the models of care delivery and clinical workflows. It shows the importance of the need to develop AI platforms that can assist healthcare professionals in optimizing patient care. This article provides end-to-end analysis that is important for the understanding of AI capabilities, as well as the limitation of AI in health institutions; thus, have huge importance to the health professionals and policymakers who require adequate integration of AI in their professional duties (Aung et al., 2021).
Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/blt.19.237487
This paper discusses AI-enabled healthcare in terms of accountability and safety by highlighting decision-making processes and the clear guidelines and regulatory frameworks. The authors discuss that since AI provides tremendous amendments in patient care, ethical and legal considerations should be taken into consideration to prevent potential harm and to ensure trust in AI-enabled systems. This article focuses on certain case studies of AI applications within clinical settings, showing both successful configurations and failed attempts due to the lack of proper supervision. It also analyzes the other category of mistakes or biases the AI could bring and the need for continuous monitoring and
improvement. This gives an insightful discussion, very critical for health professionals and management willing to implement AI technologies in ways safe and effective enough to ensure AI will enhance and not detract from patient care (Habli et al., 2020).
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. NCBI. https://doi.org/10.7861/fhj.2021-0095
This paper identifies AI application trends in healthcare practice related to the improvement of clinical workflows and more effective patient management. The following case studies illustrate the effect of AI on clinical outcomes by improving diagnostic accuracy and reducing operational inefficiencies. They also feedback the challenges that developing AI within the structures of an already existing healthcare setting could pose, especially in a situation where high-qualified training and adaptation of health professionals are needed. The article provides practical examples of AI application in areas like radiology, pathology, and patient monitoring. It, therefore, suggests that the development of AI tools should be interdisciplinary. This is a very helpful article for health and policymakers who want to know applications of AI towards improvement of health care delivery. Besides, entrepreneurs who need to understand practical applications and challenges of AI in health care will find this article particularly useful due to the examples provided in detail and the profound analysis carried out by Bajwa et al. (2021).
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01488-9
This structured literature review yields work on AI in healthcare with an indication of the benefits and challenges. The authors claim that AI might improve patient outcomes,
efficiency in operations, and support of clinical decisions. This will involve ethical issues related to data protection and algorithmic bias, from which they will be able to put forth suggestions on how this AI power can be responsibly implemented. The review include a deeper analysis of the AI application in selected medical areas. The authors emphasize the need for strict data governance and continuous evaluation criteria for the safe, effective, and ethical AI application. It will be useful for all healthcare professionals and policymakers who intend to implement effective and responsible AI technologies within care, showing a balanced view of opportunities and challenges that AI is presenting in the discipline (Secinaro et al., 2021).
Summary of Recommendations
After a critical literature review on AI in health care, the following recommendations are made for guiding the successful integration of AI technologies in health care organizations: first, there is a need to develop robust regulatory frameworks that assure ethical and safe deployment of AI systems and guidelines for reducing risks and creating trust among patients and healthcare providers in AI-assisted decision-making to ensure safety (Habli et al., 2020). The second is to prioritize the governance and safeguard of data privacy for protecting sensitive information from patients and, thus, concerns related to security and unfairness of the algorithm (Secinaro et al., 2021). The third involves an interdisciplinary approach to AI development in which engagement of health professionals, researchers, and AI developers adapts solution for the clinical practices. Such collaboration will optimize AI applications in enhanced diagnostics across differing healthcare dimensions with a view toward increased diagnostic accuracy, ultimately leading to improved patient outcomes (Bajwa et al., 2021). Lastly, according to (Aung et al., 2021) environment compatible with consistent learning and adaptability will support the combination of
AI and clinical workflows in healthcare delivery. The suggestions all point toward proactive measures and collaboration for effectively tapping the transformative power of AI in healthcare.
Organizational Factors that Influence the Implementation of Technology
The successful implementation of AI in healthcare will be influenced by many of organizational factors that not only does frame adoption and integration processes but also set the vision and provide top leadership support, very critical in driving organizational change and establishing a culture that enables the adoption of AI. As noted by (Sittig et al., 2020) leadership aligns AI initiatives with strategic goals and leadership commitment makes the integration of AI technologies along clinical workflows seamless, hence efficiency in the reduction of wastage and the advancement of the outcomes on patient care. Other important organizational factors that may affect adoption of AI technologies across various healthcare settings are the staff training and engagement. As (Poongodi et al., 2020) have pointed out the need for comprehensive training programs for the health professional to have enough knowledge and skills to make appropriate use of the AI technologies. Such organizational factors are the base for ensuring that an enabling environment characterized by innovation and inter-professional collaborative practice is a significant aspect. Strong leadership, along with appropriate training and stakeholder engagement, are some of the necessary fundamentals that can allow the explanation of various complexities associated with AI implantation.
Justification of Implementation
The advancements in AI and their applications in healthcare provide a transformational opportunity to improve patient care and operational efficiency. AI aids in increasing diagnosis accuracy and better management on a more patient-specific level because of insight into the data, thereby serving as basic guiding keys toward improvement in overall patient outcome treated with complex medical conditions (Aung et al., 2021). In addition, Strong regulatory arrangements form
a core basis for accountability assurance and the required safety of the patients through AI-aided decision-making. Clearly specified guidelines reduce risks and increase the extent of trust by health professionals and patients in using AI, and therefore aid in the proper operationalization of these innovations. Furthermore, it helps in sighting the legal and ethical concerns to the adoption of AI (Habli et al., 2020). Further, there should be strict governance over the data to ensure the confidentiality of the patients’ information to reduce the possibility of existence of hidden algorithm biasedness (Secinaro et al., 2021). Strategic approaches with respect to technological advancement and enabling regulatory compliance for integration of AI within healthcare with ethical consideration are required. It means that implementing AI into healthcare creates incentives for innovation and establishes a high level of standards and ethical responsibility for care, all of which come hand-in-hand with the best way to make use of AI through diagnostic accuracy, workflow, and patient safety improvements.
Conclusion
In conclusion, Artificial intelligence integration into nursing practice is the key to efficiency, accuracy, and better patient outcomes across the globe. Recent statistics prove AI’s potential in improving diagnostic capabilities and streamlining healthcare delivery services in the near future. The consideration of increased benefits against risks, addressing the issues related to regulations and ethics, shall be very important in maximizing the benefits of AI while ensuring patient safety. Further research and a strategic drive for implementation are required to enable the transformation in nursing practice quality brought by AI.
References
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021). The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4–15. https://doi.org/10.1093/bmb/ldab016
Bajwa, J., Munir, U., Nori, A., & Williams, B. (2021). Artificial intelligence in healthcare: transforming the practice of medicine. Future Healthcare Journal, 8(2), e188–e194. NCBI. https://doi.org/10.7861/fhj.2021-0095
Habli, I., Lawton, T., & Porter, Z. (2020). Artificial intelligence in health care: accountability and safety. Bulletin of the World Health Organization, 98(4), 251–256. https://doi.org/10.2471/blt.19.237487
IBM Education. (2024, May 13). The Benefits of AI in Healthcare | IBM. Www.ibm.com. https://www.ibm.com/think/insights/ai-healthcare-benefits
Poongodi, T., Sumathi, D., Suresh, P., & Balusamy, B. (2020). Deep Learning Techniques for Electronic Health Record (EHR) Analysis. Bio-Inspired Neurocomputing, 73–103. https://doi.org/10.1007/978-981-15-5495-7_5
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01488-9
Sittig, D. F., Wright, A., Coiera, E., Magrabi, F., Ratwani, R., Bates, D. W., & Singh, H. (2020). Current challenges in health information technology–related patient safety. Health Informatics Journal, 26(1), 146045821881489. https://doi.org/10.1177/1460458218814893
