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Transforming Healthcare Delivery and Patient Outcomes

by Barbara Davis
1
University of Zadar
*
Author to whom correspondence should be addressed.
JEIT  2021 3(1):71; https://doi.org/10.xxxx/xxxxxx
Received: 10 February 2021 / Accepted: 31 March 2021 / Published Online: 31 March 2021

Abstract

This paper investigates the transformative impact of AI-driven engineering on healthcare delivery and patient outcomes. Through case studies and research insights, it examines how artificial intelligence is revolutionizing various aspects of healthcare, including disease diagnosis, treatment planning, and patient monitoring. The study highlights the application of AI techniques such as machine learning, natural language processing, and medical imaging analysis in improving diagnostic accuracy, personalized treatment regimens, and predictive healthcare analytics. Additionally, it discusses the integration of AI with electronic health records, wearable devices, and telemedicine platforms to enable remote monitoring, proactive interventions, and personalized patient care. The paper also addresses challenges such as data privacy, regulatory compliance, and ethical considerations in the adoption of AI-driven engineering solutions in healthcare. It emphasizes the need for interdisciplinary collaboration, evidence-based practices, and stakeholder engagement to maximize the benefits of AI in transforming healthcare delivery and improving patient outcomes.


Copyright: © 2021 by Davis. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Davis, B. Transforming Healthcare Delivery and Patient Outcomes. Journal of Engineering Innovations & Technology, 2021, 3, 71. doi:10.xxxx/xxxxxx
AMA Style
Davis B.. Transforming Healthcare Delivery and Patient Outcomes. Journal of Engineering Innovations & Technology; 2021, 3(1):71. doi:10.xxxx/xxxxxx
Chicago/Turabian Style
Davis, Barbara 2021. "Transforming Healthcare Delivery and Patient Outcomes" Journal of Engineering Innovations & Technology 3, no.1:71. doi:10.xxxx/xxxxxx

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References

  1. Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29.
  2. Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Stroke and Vascular Neurology, 2(4), 230-243.
  3. Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. New England Journal of Medicine, 375(13), 1216-1219.
  4. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. New England Journal of Medicine, 380(14), 1347-1358.
  5. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
  6. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, 3-13.
  7. Wu, Y., Jiang, X., Kim, J., Ohno-Machado, L., & Jiang, X. (2019). A comparative study of current clinical natural language processing systems on handling abbreviations in discharge summaries. AMIA Summits on Translational Science Proceedings, 2019, 489-498.