An Assessment of the Role of Artificial Intelligence in Improving Nursing Diagnostics at Ahmadu Bello University Teaching Hospital, Zaria
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Background of the Study
Artificial Intelligence (AI) has become an essential tool in healthcare, offering the potential to revolutionize nursing diagnostics. In the context of Ahmadu Bello University Teaching Hospital (ABUTH), AI technologies, such as machine learning algorithms and predictive analytics, are increasingly being integrated into clinical practices to assist nurses in diagnosing patient conditions more accurately and efficiently (Yusuf & Suleiman, 2024). AI can help nurses interpret complex clinical data, identify patterns in patient health records, and even predict health risks before they manifest as clinical symptoms (Olaleye & Bello, 2023). These technological advancements offer the potential to enhance diagnostic accuracy, reduce human errors, and provide timely interventions, ultimately leading to improved patient outcomes.
Despite the promising benefits of AI in healthcare, the integration of AI technologies in nursing diagnostics, particularly at ABUTH, has faced several challenges, including limited resources, training gaps, and resistance to adopting new technologies (Kashim & Rafiq, 2023). This study aims to assess the role of AI in improving nursing diagnostics at ABUTH by exploring its impact on diagnostic accuracy, nursing efficiency, and patient care.
Statement of the Problem
Although AI holds significant promise for enhancing diagnostic processes in nursing, the integration of AI in nursing practice at ABUTH remains an underexplored area. Nurses' ability to harness the full potential of AI technologies is hindered by a lack of training, technological infrastructure, and proper integration within existing healthcare systems (Sani & Ali, 2024). Consequently, AI's role in improving nursing diagnostics has not been fully realized, which could result in missed opportunities for enhancing patient care. This study seeks to investigate the ways AI can improve nursing diagnostics at ABUTH and the barriers that nurses face in adopting these technologies.
Objectives of the Study
1. To assess the role of AI technologies in improving diagnostic accuracy among nurses at ABUTH.
2. To evaluate the impact of AI on nursing efficiency and workflow in diagnostic procedures at ABUTH.
3. To identify the barriers to the effective integration of AI technologies in nursing diagnostics at ABUTH.
Research Questions
1. How do AI technologies contribute to improving diagnostic accuracy among nurses at ABUTH?
2. What impact does AI integration have on nursing efficiency in diagnostic tasks at ABUTH?
3. What are the key barriers to the adoption of AI in nursing diagnostics at ABUTH?
Research Hypotheses
1. AI technologies significantly improve diagnostic accuracy among nurses at ABUTH.
2. The use of AI in nursing diagnostics enhances nursing efficiency and workflow at ABUTH.
3. The lack of proper training and infrastructure hinders the adoption of AI technologies in nursing diagnostics at ABUTH.
Scope and Limitations of the Study
This study will focus on the use of AI in nursing diagnostics at ABUTH. It will examine the nurses' use of AI tools and their impact on diagnostic procedures but will not assess the broader use of AI in other healthcare disciplines or patient care beyond diagnostics. The limitations of this study include the potential biases in nurses' self-reported experiences with AI tools, as well as constraints in access to AI technologies within the hospital.
Definitions of Terms
• Artificial Intelligence in Healthcare: The use of machine learning, predictive analytics, and other AI technologies to assist in medical diagnoses and treatment decisions.
• Diagnostic Accuracy: The ability to correctly identify a patient's condition based on clinical data.
• Nursing Efficiency: The ability of nurses to perform diagnostic and other healthcare tasks effectively within a given timeframe, using available resources.
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