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Clinician must write a lengthy summary each time a patient is discharged fromthe hospital. This task is time-consuming due to the sheer number of uniqueclinical concepts covered in the admission. Identifying and covering saliententities is vital for the summary to be clinically useful. We fine-tuneopen-source LLMs (Mistral-7B-Instruct and Zephyr-7B-\b{eta}) on the task andfind that they generate incomplete and unfaithful summaries. To increase entitycoverage, we train a smaller, encoder-only model to predict salient entities,which are treated as content-plans to guide the LLM. To encourage the LLM tofocus on specific mentions in the source notes, we propose SPEER:Sentence-level Planning via Embedded Entity Retrieval. Specifically, we markeach salient entity span with special "{{ }}" boundary tags and instruct theLLM to retrieve marked spans before generating each sentence. Sentence-levelplanning acts as a form of state tracking in that the model is explicitlyrecording the entities it uses. We fine-tune Mistral and Zephyr variants on alarge-scale, diverse dataset of ~167k in-patient hospital admissions andevaluate on 3 datasets. SPEER shows gains in both coverage and faithfulnessmetrics over non-guided and guided baselines.

SPEER: Sentence-Level Planning of Long Clinical Summaries via Embedded Entity Retrieval