Cloud Costs in Healthcare: Practical Approaches with Lifecycle Policies, Tagging, and Usage Reporting

Authors

  • Lalith Sriram Datla Consultant/Cloud Engineer at GE HealthCare, USA Author

Keywords:

Cloud computing, healthcare IT, cost optimization, lifecycle policies, cloud governance

Abstract

Rising usage of cloud technology by the healthcare industry to improve  innovation, scalability, & patient-centered care runs into a significant challenge: rising and often surprising cloud expenses. While cloud adoption provides additional operational advantages and flexibility, along with numerous healthcare firms struggle to control expenses due to inconsistent resource utilization, unused infrastructure & lack of cost control systems. Incases in cloud cost control compromise budgets, regulatory compliance and as well as service quality. Effective methods include the use of lifecycle controls, resource tracking, and thorough usage reporting are becoming perceptibly common in order to handle this problem. By helping to automatically decommission extra or further outdated resources, lifespan rules help to reduce these kinds of wastes. Tagging gives companies a comprehensive view & lets them categorize & track resources based on the departments, projects, or the compliance rules. Regular use of reporting reveals trends, points out anomalies, and helps to drive data-driven decisions. These technologies help hospital IT teams to maximize the cloud prices, therefore assuring that every dollar improves outcomes &  efficiency. By adding these concepts into daily operations, companies enhance the cloud infrastructure & they provide a mechanism for long-term cost control. Maintaining success requires using a deliberate, proactive strategy to establish greater spending control in an environment where financial management & the regulatory compliance take front stage.

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Published

07-10-2024

How to Cite

[1]
Lalith Sriram Datla, “Cloud Costs in Healthcare: Practical Approaches with Lifecycle Policies, Tagging, and Usage Reporting”, American J Cognit Comput AI Syst, vol. 8, pp. 44–66, Oct. 2024, Accessed: Jul. 29, 2026. [Online]. Available: https://ajccai.org/index.php/publication/article/view/13