Version 1
: Received: 28 February 2024 / Approved: 29 February 2024 / Online: 29 February 2024 (10:03:33 CET)
How to cite:
Shah, N.; Chen, H. Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings. Preprints2024, 2024021694. https://doi.org/10.20944/preprints202402.1694.v1
Shah, N.; Chen, H. Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings. Preprints 2024, 2024021694. https://doi.org/10.20944/preprints202402.1694.v1
Shah, N.; Chen, H. Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings. Preprints2024, 2024021694. https://doi.org/10.20944/preprints202402.1694.v1
APA Style
Shah, N., & Chen, H. (2024). Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings. Preprints. https://doi.org/10.20944/preprints202402.1694.v1
Chicago/Turabian Style
Shah, N. and Henry Chen. 2024 "Optimizing Healthcare Delivery: Investigating Key Areas for AI Integration and Impact in Clinical Settings" Preprints. https://doi.org/10.20944/preprints202402.1694.v1
Abstract
This study investigates the pivotal areas where Artificial Intelligence (AI) integration in healthcare warrants further exploration, leveraging insights from Boston Specialists, a forefront healthcare provider in AI adoption. Employing a qualitative case study approach, enriched by in-depth interviews and comprehensive literature reviews, we identify three main domains for potential AI expansion: scheduling efficiency, clinical documentation, and AI-driven medical research. Our results underscore the substantial enhancements AI brings to operational workflows and patient care, notably through improved scheduling systems, accuracy in medical documentation, and the facilitation of data-driven research methodologies. These findings suggest a critical need for healthcare institutions to further invest in AI technologies, focusing on these key areas to harness AI's full potential in personalizing care, optimizing efficiency, and advancing medical knowledge. The study concludes with a call for continued investigation into AI's integration within healthcare settings, emphasizing the importance of a supportive organizational infrastructure, ethical considerations, and data security in realizing AI's transformative impact on healthcare delivery and patient outcomes.
Keywords
artificial intelligence; healthcare; clinical workflow; medical research
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
