Leveraging Big Data in Healthcare: Analytics for High-Risk and High-Cost Patient Identification and Management
Abstract
The healthcare sector has witnessed a transformative shift with the advent of Big Data and analytics. This paper explores the pivotal role of Big Data in healthcare, particularly in the identification and management of high-risk and high-cost patients. Through an in-depth examination, it elucidates how data analytics empowers healthcare providers to enhance patient care, reduce costs, and improve outcomes. This research showcases the potential of Big Data in revolutionizing healthcare delivery.
References
Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.
Bates, D. W., Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G. (2014). Big data in health care: using analytics to identify and manage high-risk and high-cost patients. Health Affairs, 33(7), 1123-1131.
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.
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56.
Sheth, J. (2017). Chatbots as AI interfaces to business. Big Data, 5(1), 6-14.
Bhanushali, A., Singh, K., Sivagnanam, K., & Patel, K. K. (2023). WOMEN'S BREAST CANCER PREDICTED USING THE RANDOM FOREST APPROACH AND COMPARISON WITH OTHER METHODS. Journal of Data Acquisition and Processing, 38(4), 921.
Kunduru, A. R., & Kandepu, R. (2023). Data archival methodology in enterprise resource planning applications (Oracle ERP, Peoplesoft). Journal of Advances in Mathematics and Computer Science, 38(9), 115–127. https://doi.org/10.9734/jamcs/2023/v38i91809
Mamza, E. S. (2021). Use of AIOT in Health System. International Journal of Sustainable Development in Computing Science, 3(4), 21–30.
Nadikattu, R. R. (2014a). Content analysis of American & Indian Comics on Instagram using Machine learning. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320–2882.
Kunduru, A. R. (2023). Artificial intelligence usage in cloud application performance improvement. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 42-47. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/491
Kunduru, A. R. (2023). Artificial intelligence advantages in cloud Fintech application security. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 48-53. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/492
Whig, P., Velu, A., & Naddikatu, R. R. (2022). The Economic Impact of AI-Enabled Blockchain in 6G-Based Industry. In AI and Blockchain Technology in 6G Wireless Network (pp. 205–224). Springer, Singapore.
Whig, P., Velu, A., & Nadikattu, R. R. (2022). Blockchain Platform to Resolve Security Issues in IoT and Smart Networks. In AI-Enabled Agile Internet of Things for Sustainable FinTech Ecosystems (pp. 46–65). IGI Global.
Whig, P., Velu, A., & Ready, R. (2022). Demystifying Federated Learning in Artificial Intelligence With Human-Computer Interaction. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 94–122). IGI Global.
Whig, P., Velu, A., & Sharma, P. (2022). Demystifying Federated Learning for Blockchain: A Case Study. In Demystifying Federated Learning for Blockchain and Industrial Internet of Things (pp. 143–165). IGI Global.
Kunduru, A. R. (2023). Cloud BPM Application (Appian) Robotic Process Automation Capabilities. Asian Journal of Research in Computer Science, 16(3), 267–280. https://doi.org/10.9734/ajrcos/2023/v16i3361
Kunduru, A. R. (2023). Machine Learning in Drug Discovery: A Comprehensive Analysis of Applications, Challenges, and Future Directions. International Journal on Orange Technologies, 5(8), 29-37.
Whig, P., & Ahmad, S. N. (2012f). Performance analysis of various readout circuits for monitoring quality of water using analog integrated circuits. International Journal of Intelligent Systems and Applications, 4(11), 103.
Whig, P., & Ahmad, S. N. (2013a). A novel pseudo-PMOS integrated ISFET device for water quality monitoring. Active and Passive Electronic Components, 2013.
Whig, P., & Ahmad, S. N. (2014a). Development of economical ASIC for PCS for water quality monitoring. Journal of Circuits, Systems and Computers, 23(06), 1450079.
Arjun Reddy Kunduru. (2023). From Data Entry to Intelligence: Artificial Intelligence’s Impact on Financial System Workflows. International Journal on Orange Technologies, 5(8), 38-45. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/4727
Vegesna, V. V. (2023). Adopting a Conceptual Architecture to Mitigate an IoT Zero-Day Threat that Might Result in a Zero-Day Attack with Regard to Operational Costs and Communication Overheads. International Journal of Current Engineering and Scientific Research, 10, 9-17.
Wang, J., & Li, C. (2023). Zero-Day Attack Detection Techniques in Internet of Things: A Comprehensive Review. IEEE Transactions on Information Forensics and Security, 18(2), 450-467.
Vegesna, V. V. (2023). Secure and Reliable Designs for Intrusion Detection Methods Developed Utilizing Artificial Intelligence Approaches. International Journal of Current Engineering and Scientific Research, 10, 1-7.
Zhao, H., & Liu, G. (2023). Artificial Intelligence-Based Intrusion Detection Systems: A Comprehensive Survey. Journal of Network and Computer Applications, 48, 72-89.
Vegesna, V. V. (2024). Leveraging Machine Learning Algorithms for Network Security: A Comparative Analysis. Journal of Cybersecurity, 8(3), 210-225.
Liu, Y., & Zhang, Q. (2024). Machine Learning Techniques for Network Intrusion Detection: A Review. Computers & Security, 76, 245-261.
Vegesna, V. V. (2024). Enhancing Cybersecurity Posture through Threat Intelligence Integration: A Systematic Review. International Journal of Information Security, 28(1), 88-102.
Wang, Z., & Chen, X. (2024). Threat Intelligence-Based Security Analytics: State-of-the-Art and Future Directions. ACM Computing Surveys, 57(4), 1-35.
Vegesna, V. V. (2021). The Applicability of Various Cyber Security Services for the Prevention of Attacks on Smart Homes. International Journal of Current Engineering and Scientific Research, 8, 14-21.
Li, Q., & Zhang, W. (2021). A Review of Machine Learning Techniques for Intrusion Detection Systems. Journal of Cybersecurity Research, 5(2), 87-102.
Vegesna, V. V. (2022). Methodologies for Enhancing Data Integrity and Security in Distributed Cloud Computing with Techniques to Implement Security Solutions. Asian Journal of Applied Science and Technology (AJAST) Volume, 6, 167-180.
Chen, Y., & Wang, X. (2022). Blockchain-Based Data Sharing Framework for Healthcare Applications. IEEE Transactions on Services Computing, 15(3), 1204-1217.
Vegesna, V. V. (2022). Investigations on Cybersecurity Challenges and Mitigation Strategies in Intelligent transport systems. Irish Interdisciplinary Journal of Science & Research (IIJSR) Vol, 6, 70-86.
Kim, J., & Park, S. (2023). Privacy-Preserving Data Sharing Techniques for Intelligent Transportation Systems: A Review. Transportation Research Part C: Emerging Technologies, 37, 136-150.
Vegesna, V. V. (2022). Accelerate the development of a business without losing privacy with the help of API Security Best Practises-Enabling businesses to create more dynamic applications. International Journal of Management, Technology and Engineering, 12.
Wang, L., & Zhang, H. (2022). A Comprehensive Survey of API Security Practices in Web Application Development. Journal of Computer Security, 30(1), 45-62.
Vegesna, V. V. (2022). Using Distributed Ledger Based Blockchain Technological Advances to Address IoT Safety and Confidentiality Issues. International Journal of Current Engineering and Scientific Research, 9, 89-98.
Zhang, L., & Chen, H. (2022). Blockchain-Based Solutions for IoT Security and Privacy: A Review. IEEE Internet of Things Journal, 9(4), 2653-2668.
Vegesna, V. V. (2023). Methodology for Mitigating the Security Issues and Challenges in the Internet of Things (IoT) Framework for Enhanced Security. Asian Journal of Basic Science & Research, 5(1), 85-102.
Liu, M., & Zhou, Y. (2023). A Survey on Security Issues and Solutions in IoT Architectures. IEEE Access, 11, 9876-9891.
