The advent of Healthcare 4.0, marked by the convergence of advanced technologies and data-driven healthcare, has ushered in a new era of precision and personalization. At the forefront of this transformation is the integration of fractional computing, a paradigm that introduces nuanced numerical accuracy to healthcare applications. This paper explores the implications of fractional computing in the healthcare landscape, focusing on precision medicine, data analytics, and resource optimization. The integration of fractional computing presents opportunities to revolutionize healthcare delivery, enabling personalized medication dosages, enhancing genomic analysis, and facilitating real-time patient-specific simulations. However, the deployment of fractional computing is not without ethical considerations. Patient data privacy, security, transparency in algorithmic decision-making, and the mitigation of biases in healthcare algorithms demand careful attention to ensure responsible and equitable healthcare practices. Patient empowerment emerges as a central theme in this narrative, as fractional computing becomes a tool for fostering collaboration between healthcare providers and patients. Access to personal health data, shared decision-making, and educational initiatives empower individuals to actively participate in their healthcare journey. Ethical considerations and patient empowerment are intricately linked, forming the foundation for a healthcare ecosystem that prioritizes both innovation and the well-being of patients. As Healthcare 4.0 continues to unfold, the responsible and ethical use of fractional computing stands as a guiding principle. This paper delves into the complexities, trends, and solutions surrounding fractional computing in healthcare, emphasizing the need for a balanced approach that aligns technological advancements with ethical considerations and patient empowerment.
Healthcare 4.0, Fractional Computing, patient care, medical research, ChatGPT, healthcare management, IoT, Artificial Intelligence (AI) and Machine Learning (ML)
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