International Journal of Science, Engineering and TechnologyAuthors: Divya Bhaskar, Jyothi Raj, Sangeetha Kumari, Banu Priya
Abstract: In the healthcare sector, server uptime and system reliability are directly linked to clinical outcomes, operational continuity, and regulatory compliance. Downtime in systems hosting electronic health records (EHRs), picture archiving and communication systems (PACS), or laboratory information systems (LIS) can result in data inaccessibility, delayed diagnoses, and risk to patient safety. Traditional monitoring tools often fail to detect early warning signs of infrastructure degradation across heterogeneous UNIX environments such as Solaris, AIX, and Red Hat Enterprise Linux (RHEL). This review explores how artificial intelligence (AI) can be leveraged to forecast server failures in healthcare IT, offering predictive insights before outages occur. The article examines the technical landscape of predictive analytics applied to server telemetry, including data sources like SMART diagnostics, IPMI sensors, SNMP traps, OS-level metrics, and application logs. It evaluates machine learning (ML) models ranging from supervised classifiers and unsupervised anomaly detectors to time-series forecasting techniques that can identify subtle degradation trends. Additionally, the review details how AI pipelines can be integrated with IT service management (ITSM), configuration management databases (CMDB), and real-time monitoring platforms to create automated, intelligent remediation loops. Real-world case studies highlight deployments in radiology departments, hospital middleware, and clustered EHR environments, showcasing tangible improvements in uptime and incident response. The article also addresses key challenges such as data sparsity, platform heterogeneity, and privacy constraints under regulations like HIPAA and FDA 21 CFR Part 11. Future directions include federated learning, self-healing infrastructure, digital twins, and explainable AI tailored to regulated healthcare environments. Ultimately, the adoption of AI-driven predictive maintenance is positioned as a critical step toward achieving resilient, high-availability UNIX infrastructures in modern healthcare systems.
DOI: http://doi.org/10.5281/zenodo.15846309
