AI-driven Hospital Workflow Optimization for Enhanced Patient Care: Developing AI-driven solutions to optimize hospital workflows and improve the efficiency of patient care delivery
Published 07-09-2024
Keywords
- Hospital Workflow
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
Abstract
This paper presents an innovative approach to enhancing patient care through the optimization of hospital workflows using artificial intelligence (AI). The healthcare industry faces numerous challenges, including increasing patient volumes, limited resources, and the need for efficient and effective care delivery. AI technologies offer promising solutions to these challenges by automating and streamlining various aspects of hospital operations. This research focuses on developing AI-driven solutions that optimize hospital workflows to improve patient care quality, reduce wait times, and enhance overall operational efficiency. The proposed solutions leverage AI algorithms, including machine learning and natural language processing, to analyze patient data, optimize staff allocation, and streamline administrative processes. The paper also discusses the implementation challenges and ethical considerations associated with AI-driven healthcare solutions. Overall, this research contributes to the growing body of knowledge on AI applications in healthcare and provides valuable insights for healthcare practitioners and policymakers.
Downloads
References
- Saeed, A., Zahoor, A., Husnain, A., & Gondal, R. M. (2024). Enhancing E-commerce furniture shopping with AR and AI-driven 3D modeling. International Journal of Science and Research Archive, 12(2), 040-046.
- Shahane, Vishal. "A Comprehensive Decision Framework for Modern IT Infrastructure: Integrating Virtualization, Containerization, and Serverless Computing to Optimize Resource Utilization and Performance." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 53-75.
- Biswas, Anjanava, and Wrick Talukdar. "Guardrails for trust, safety, and ethical development and deployment of Large Language Models (LLM)." Journal of Science & Technology 4.6 (2023): 55-82.
- N. Pushadapu, “Machine Learning Models for Identifying Patterns in Radiology Imaging: AI-Driven Techniques and Real-World Applications”, Journal of Bioinformatics and Artificial Intelligence, vol. 4, no. 1, pp. 152–203, Apr. 2024
- Talukdar, Wrick, and Anjanava Biswas. "Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracity." arXiv preprint arXiv:2408.04023 (2024).
- Chen, Jan-Jo, Ali Husnain, and Wei-Wei Cheng. "Exploring the Trade-Off Between Performance and Cost in Facial Recognition: Deep Learning Versus Traditional Computer Vision." Proceedings of SAI Intelligent Systems Conference. Cham: Springer Nature Switzerland, 2023.
- Alomari, Ghaith, et al. “AI-Driven Integrated Hardware and Software Solution for EEG-Based Detection of Depression and Anxiety.” International Journal for Multidisciplinary Research, vol. 6, no. 3, May 2024, pp. 1–24.
- Choi, J. E., Qiao, Y., Kryczek, I., Yu, J., Gurkan, J., Bao, Y., ... & Chinnaiyan, A. M. (2024). PIKfyve, expressed by CD11c-positive cells, controls tumor immunity. Nature Communications, 15(1), 5487.
- Borker, P., Bao, Y., Qiao, Y., Chinnaiyan, A., Choi, J. E., Zhang, Y., ... & Zou, W. (2024). Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy. Cancer Research, 84(6_Supplement), 7479-7479.
- Gondal, Mahnoor Naseer, and Safee Ullah Chaudhary. "Navigating multi-scale cancer systems biology towards model-driven clinical oncology and its applications in personalized therapeutics." Frontiers in Oncology 11 (2021): 712505.
- Saeed, Ayesha, et al. "A Comparative Study of Cat Swarm Algorithm for Graph Coloring Problem: Convergence Analysis and Performance Evaluation." International Journal of Innovative Research in Computer Science & Technology 12.4 (2024): 1-9.
- Pelluru, Karthik. "Enhancing Cyber Security: Strategies, Challenges, and Future Directions." Journal of Engineering and Technology 1.2 (2019): 1-11.
- Mustyala, Anirudh, and Sumanth Tatineni. "Cost Optimization Strategies for Kubernetes Deployments in Cloud Environments." ESP Journal of Engineering and Technology Advancements 1.1 (2021): 34-46.