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Interdisciplinary rounds, discharge planning meetings, and tumor boards contain high-value clinical reasoning that is often only partially reflected in the medical record. These discussions shape treatment priorities, medication decisions, consult plans, and discharge readiness, yet their verbal and collaborative nature makes them difficult to document comprehensively. Manual summarization of care team discussions requires time, attention, and clinical synthesis that busy clinicians may not have during or immediately after meetings. Existing documentation practices often capture final decisions but omit uncertainty, rationale, task ownership, and evolving care coordination needs. This article proposes a large language model pipeline that could summarize interdisciplinary care discussions using secure meeting transcripts combined with active problem lists, medication lists, and discharge planning notes. The objective is to describe a conceptual architecture for generating accurate, structured, and clinically reviewable summaries of team communication. The proposed approach uses retrieval-augmented generation to ground the language model in structured clinical context while processing a diarized transcript of the care discussion. The model would focus on identifying decisions, medication changes, unresolved issues, discharge barriers, and action items requiring follow-up. Conceptually, the pipeline would generate a note-ready summary with lower hallucination risk because the model is constrained by structured clinical anchors and transcript evidence. It could help distinguish new decisions from repeated background information and convert a documentation-light meeting into a structured clinical artifact. A secure, grounded large language model system could support safer and more complete documentation of interdisciplinary care discussions. By combining transcript evidence with patient-specific structured data, such a system could reduce cognitive burden and improve continuity across clinical teams.