Visit summarization turns the transcript of a consult into structured outputs: an after-visit summary written in plain patient language, a draft clinical note for the chart, and extracted items such as orders and follow-up tasks. A large language model (LLM) does the drafting, condensing a long conversation into the few things that need to be recorded or acted on. Done well, it removes a large documentation burden from clinicians and gives patients a clearer record of what happened and what to do next.
The LLM is only the drafting engine; the safety architecture around it is what makes the feature usable in healthcare. That means grounding outputs strictly on the actual transcript, checking for hallucinations (plausible-sounding statements the conversation never supported), and requiring clinician sign-off before anything enters the medical record. Because the transcript and the outputs are protected health information (PHI), the model vendor must operate under a BAA, consistent with HIPAA — PHI goes into the model and comes back out, so it cannot be processed outside the compliance boundary.
Patient-facing summaries carry extra duties beyond accuracy. They should be written at an accessible reading level, localized into the patient's language, and phrased so that care instructions survive ambiguity rather than inviting misinterpretation. The common mistake is letting an unreviewed model output flow into the chart or to the patient; a fluent but wrong medication instruction or follow-up date is exactly the kind of confident error LLMs produce, and without mandatory human review it becomes a patient-safety incident rather than a typo.
