Dynamic Healthcare Intelligence: Integrating AI Predictive Analytics with Kubernetes Scaling for Enhanced Patient Outcomes
DOI:
https://doi.org/10.15662/4x1hjq50Keywords:
Predictive Healthcare Analytics, Kubernetes Scaling, Human-AI Collaboration, Medical Resource Optimization, Real-Time Clinical Decision SupportAbstract
This article examines the transformative integration of artificial intelligence predictive analytics with Kubernetesenabled scaling infrastructure in contemporary healthcare settings. The article presents a comprehensive framework detailing how these technologies work in concert to detect potential medical emergencies before they manifest, while dynamically adjusting computational resources based on patient volume and data complexity. The article highlights the critical role of human-AI collaboration, where clinicians retain decision-making authority while leveraging AI-generated insights to enhance diagnostic and treatment processes. The article encompasses implementation challenges, including data security concerns, technical deployment obstacles, and institutional adaptation barriers, alongside proposed solutions and empirical evidence of system performance. The article suggests that this technological integration creates more resilient healthcare systems capable of delivering personalized care while efficiently managing resources during
both routine operations and crisis scenarios. This article contributes to the evolving discourse on healthcare technology by emphasizing the symbiotic relationship between computational capabilities and human medical expertise.
References
[1] Vandana Whig; Bestoon Othman, et al., "An Empirical Analysis of Artificial Intelligence (AI) as a Growth Engine for the Healthcare Sector," IEEE Conference Publication, April 28-29, 2022. https://ieeexplore.ieee.org/abstract/document/9823607
[2] Tibério Baptista; Luís Bastião Silva, et al., "Highly Scalable Medical Imaging Repository Based on Kubernetes," IEEE Xplore. https://ieeexplore.ieee.org/document/9669559/citations#citations
[3] Sohail Imran, Tariq Mahmood, et al., "Big Data Analytics in Healthcare — A Systematic Literature Review and Roadmap for Practical Implementation," IEEE/CAA Journal of Automatica Sinica, January 2021. https://ieeejas.net/article/doi/10.1109/JAS.2020.1003384?pageType=en
[4] Amogh Chaudhari, Vidya Sarode, et al., "A Review of Artificial Intelligence for Predictive Healthcare Analytics and Healthcare IoT Applications," Lecture Notes in Networks and Systems (LNNS), August 5, 2023. https://link.springer.com/chapter/10.1007/978-981-99-3177-4_42
[5] Víctor Medel; Omer Rana, et al., "Modelling Performance & Resource Management in Kubernetes," IEEE/ACM 9th International Conference on Utility and Cloud Computing (UCC), 20 March 2017. https://ieeexplore.ieee.org/document/7881642
[6] Mohamed Mouine; Mohamed Aymen Saied, "Model-Driven Approach to Design & Architecture of Healthcare IoT Infrastructure," IEEE Conference Publication, 10 April 2023. https://ieeexplore.ieee.org/document/10092095
[7] Liuping Wang, Zhan Zhang, et al., "Human-centered design and evaluation of AI-empowered clinical decision support systems," Frontiers in Computer Science, 2023. https://www.frontiersin.org/journals/computerscience/articles/10.3389/fcomp.2023.1187299/full
[8] Oksana, M., Kotsipak, M, et al., "Collaborative Human-AI Decision-Making Systems with Numerical Channels," 12th International Conference on Advanced Computer Information Technologies (ACIT), 2022. https://www.diva-portal.org/smash/get/diva2:1711729/FULLTEXT01.pdf
[9] Blake Murdoch, "Privacy and Artificial Intelligence: Challenges for Protecting Health Information in a New Era," BMC Medical Ethics, September 15, 2021. https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-021-00687-3
[10] Eman AbuKhousa; Piers Campbell, "Predictive Data Mining to Support Clinical Decisions: An Overview of Heart Disease Prediction Systems," IEEE Conference Publication, 31 May 2012. https://ieeexplore.ieee.org/document/6207745
[11] Ahmad Malekian Borujeni; Mahmood Fathy, et al., "Developing and Evaluating a Real-Time and Energy-Efficient Architecture for an Internet of Health Things," IEEE Conference Publication, 10 November 2020. https://ieeexplore.ieee.org/document/9250196
[12] Yun Zhao; Yuqing Wang, et al., "Empirical Quantitative Analysis of COVID-19 Forecasting Models," IEEE Conference Publication, 14 January 2022.. https://ieeexplore.ieee.org/document/9679932
[13] Hung-Ming Chen; Shih-Ying Chen, et al., "Designing an Improved ML Task Scheduling Mechanism on Kubernetes," IEEE Conference Publication, August 23, 2023. https://ieeexplore.ieee.org/abstract/document/10219484
