Operational reliability in hospital information systems depends on the continuous availability of clinical software, network infrastructure, and bedside medical devices. Traditional incident response practices rely on manual escalation and reactive troubleshooting, which delay access to critical systems and disrupt patient workflows during infrastructure faults or abnormal device behavior. This paper proposes an AIOps-driven DevOps pipeline that automates incident detection and remediation across hospital IT environments by combining statistical anomaly analysis, event correlation, and rule-based remediation triggers. Classic machine learning models process device telemetry, network failures, and application health metrics to detect anomalies without requiring generative inference. Detected events are then executed as automated playbooks through DevOps pipelines to isolate faulty components, quarantine impacted nodes, initiate system restarts, or escalate alerts based on clinical priority. The proposed architecture treats operational reliability as a continuous deployment of corrective actions rather than a post-failure activity. By embedding anomaly detection into DevOps workflows, hospitals can reduce remediation time, improve service availability, and preserve clinical continuity without dependence on emerging generative models.