LLMs for Healthcare Process Orchestration: Promises and Challenges

Healthcare processes combine stable workflow segments with open, case-dependent work. This makes the use of large language models (LLMs) and more autonomous AI systems less a question of technical capability alone than of orchestration: different degrees of AI autonomy fit different degrees of process structure. Based on a conceptual review, this paper develops a BPMN-CMMN lens for distinguishing between structured tasks that support tightly bounded LLM-guided execution and case-dependent segments in which more autonomous coordination may become relevant. The literature suggests that LLMs fit best in documentation, summarization, communication, referral support, and other bounded workflow steps, whereas more autonomous systems become more plausible where progression depends on changing context, exceptions, and discretionary judgment. The main challenge lies in evaluation. Classical process indicators remain necessary, but they become harder to interpret once work is distributed across humans and AI. In addition to operational KPIs, healthcare organizations need measures that capture orchestration quality, supervision burden, context carryover, and the stability of outcomes across variable cases.