Large Language Models in Healthcare Management: Implementation Evidence, Governance Architecture, and Strategic Pathways

Abstract

Large language models (LLMs) are rapidly shifting from exploratory tools to production infrastructure in healthcare management. This review focuses on five implementation domains: model foundations and adaptation, knowledge retrieval and question answering, document intelligence, operations and decision support, and governance and safety. Across these domains, the literature converges on one key result: organizational value is created less by raw model capability and more by workflow design, policy alignment, and institutional accountability. Reported benefits include faster information turnaround, lower administrative friction, improved triage support, and broader access to decision resources. Conversely, reported risks encompass hallucination, uneven reliability across contexts, privacy leakage, automation bias, and governance lag. To address these, we identify recurrent sociotechnical patterns that differentiate robust deployments from fragile pilots: retrieval grounding, role-bounded human oversight, auditable interaction logs, staged rollout, and continuous quality monitoring. We further propose an implementation maturity pathway linking technical controls to management outcomes, emphasizing total cost of ownership, workforce adaptation, and cross-functional governance capability. The review concludes that the next phase of healthcare LLM adoption should prioritize longitudinal multi-site evaluation, management-centered benchmar