Enterprise AI Operations in Healthcare IT Infrastructure

Abstract

Healthcare information technology (IT) infrastructure has grown substantially in complexity alongside the accelerating adoption of electronic health records, clinical decision support systems, telehealth platforms, and patient-facing digital services. Managing this infrastructure reliably under high availability expectations, strict privacy obligations, and patient safety constraints poses operational challenges that conventional IT management approaches cannot adequately address at scale. Enterprise AI Operations (AIOps) offers a principled methodology for meeting these challenges by integrating machine learning with operational workflows to automate detection, diagnosis, and resolution of IT incidents. This paper presents the Healthcare AIOps Reference Architecture (HARA): a three-layer framework that adapts AIOps design principles to the specific regulatory, governance, and safety requirements of clinical IT environments. The three layers, Clinical Data Observability, Healthcare Intelligence and Orchestration, and Clinical Governance and Compliance, are described with formal theoretical foundations and algorithmic specifications. Three formal equations are contributed: an incident routing latency model under alert volume growth, a cache hit efficiency model under steady-state access patterns, and an inference cost reduction model under model pruning, each grounded in the published literature. Two pseudocode algorithms with formal complexity analysis are presented for healthcare alert triage and escalation and for adaptive clinical model retraining triggering. Three illustrative case vignettes ground the architecture in hospital network incident management, laboratory and imaging system prioritization, and patient-facing service resilience. Governance design addresses privacy regulation, algorithmic bias, explainability, and human oversight within healthcare IT contexts. The paper contributes a design-level framework for healthcare IT architects and platform operations engineers seeking to apply AIOps principles within healthcare regulatory, safety, and equity constraints and identifies federated AIOps and carbon-aware clinical scheduling as future research directions.