Enhancing IT-Driven Business Management Systems in Healthcare through the Integration of Machine Learning and Artificial Intelligence with Advanced Cybersecurity Frameworks

Ananta Toer Paramaditha

Authors

Ananta Toer Paramaditha Indonesia Author

Keywords:

Healthcare, Business Management Systems, Machine Learning, Artificial Intelligence, Cybersecurity, IT-driven systems, Data Security

Abstract

The healthcare sector is experiencing a transformative shift due to the integration of Information Technology (IT) with emerging technologies such as Machine Learning (ML) and Artificial Intelligence (AI). These advancements offer the potential to enhance operational efficiency, optimize resource management, and improve patient care. However, the increased reliance on IT systems also elevates the risk of cyber threats, necessitating robust cybersecurity frameworks to safeguard sensitive healthcare data. This paper explores the integration of ML and AI in IT-driven business management systems in healthcare, focusing on how advanced cybersecurity frameworks can mitigate the risks associated with such integration. Through a review of current literature, we analyze existing approaches, challenges, and propose strategies to secure these systems. The research highlights the potential of ML and AI to enhance business management in healthcare, while demonstrating the critical role of cybersecurity frameworks in ensuring system integrity.

References

Baker, L., et al. (2021). AI in Cybersecurity: Intrusion Detection Systems for Healthcare. Journal of Cybersecurity, 15(4), 65-77.

Cybersecurity Ventures. (2022). Healthcare Cybersecurity Breaches and Costs. [online] Available at: https://www.cybersecurityventures.com [Accessed 10 Oct. 2023].

Goodfellow, I., et al. (2022). Adversarial Attacks on AI Systems. Advances in Neural Information Processing Systems, 33, 22-29.

Srinivasagopalan, L. N. (2023). Evaluating Healthcare Insurance Through Integrated Frameworks: Implications for Equity and Social Justice in Public Health. Frontiers in Health Informatics, 12, 6920–6932.

Jones, K., et al. (2023). Impact of IT-Driven Management Systems on Healthcare Efficiency. Healthcare Information Review, 9(2), 34-48.

Ponemon Institute. (2022). Cost of Data Breach Report. IBM Security. [online] Available at: https://www.ponemon.org/research [Accessed 11 Oct. 2023].

Srinivasagopalan, L. N. (2023). Predicting health insurance premiums using machine learning: A novel regression-based model for enhanced accuracy and personalization. World Journal of Advanced Research and Reviews, 19(01), 1580–1592.

Smith, R., et al. (2022). AI in Healthcare: Enhancing Efficiency and Outcomes. Journal of Medical Systems, 46(1), 11-25.

Chen, Y., et al. (2022). Cybersecurity Challenges in the Healthcare Sector: A Review of Emerging Threats and Solutions. Journal of Healthcare Informatics, 18(3), 150-167.

Srinivasagopalan, L. N. (2023). Advancing risk pooling mechanisms in healthcare coverage to strengthen system resilience and equity in modern health systems. Nanotechnology Perceptions, 19(2), 70–84.

Liu, X., et al. (2021). Machine Learning Applications in Healthcare Management: A Systematic Review. International Journal of Medical Informatics, 156, 104618.

Kshetri, N. (2021). The Economics of Healthcare Data Breaches and Cybersecurity Investments. Health Policy and Technology, 10(4), 100546.

Patel, V., et al. (2020). The Role of AI in Enhancing Healthcare Cybersecurity: Current Trends and Future Directions. Health Information Science and Systems, 8(2), 77-90.

Srinivasagopalan, L. N. (2023). Understanding the link between healthcare coverage literacy and policyholder outcomes in a rapidly changing global healthcare landscape. Nanotechnology Perceptions, 19(3), 142–155.

Geisler, B., et al. (2023). Blockchain and AI for Securing Health Data: A Comparative Study of Emerging Technologies. Journal of Digital Health, 12(1), 29-41.

Nasr, M., et al. (2022). Adopting AI-Powered Cybersecurity Frameworks in Healthcare: Opportunities and Challenges. Computers in Biology and Medicine, 140, 105098.