Shimo Lab at “Discharge Me!”: Discharge Summarization by Prompt-Driven Concatenation of Electronic Health Record Sections (2024)

Abstract: In this paper, we present our approach to the shared task “Discharge Me!” at the BioNLP Workshop 2024. The primary goal of this task is to reduce the time and effort clinicians spend on writing detailed notes in the electronic health record (EHR). Participants develop a pipeline to generate the “Brief Hospital Course” and “Discharge Instructions” sections from the EHR. Our approach involves a first step of extracting the relevant sections from the EHR. We then add explanatory prompts to these sections and concatenate them with separate tokens to create the input text. To train a text generation model, we perform LoRA fine-tuning on the ClinicalT5-large model. On the final test data, our approach achieved a ROUGE-1 of 0.394, which is comparable to the top solutions.

## introduction electronic health records (ehr) eliminate the need for end-users to write medical records by hand and provide easy access to digital records @xcite . however, the use of ehr sometimes increases the burden on end-users @xcite @xcite . with this in mind, there has been active research in recent years into applying natural language processing (nlp) to ehr to reduce the burden on end-users @xcite @xcite . to explore the potential of nlp in ehr, the shared task "discharge me!" @xcite at the bionlp workshop 2024 evaluates the ability to generate discharge summaries. the goal of this task is to reduce the time and effort clinicians spend on writing detailed notes in the ehr. participants develop a pipeline that leverages the ehr data to generate discharge summaries. input text ehr discharge instructions brief hospital course section extraction & prompt addition & <@xmath0 figure 1: overview of our pipeline. to create input text, we extract sections from the ehr, add explanatory prompts, and then concatenate them @xmath1. we then generate discharge summaries using clnicalt5large, which has been fine-tuned for each target. in this paper, we present our approach to the shared task. fig. 1 provides an overview of our pipeline. we preprocess the ehr, as illustrated in fig. 2 , by removing noise and extracting sections that are essential for the target summary. the sections are selected based on a predetermined priority. for extracted sections, we prepend the prompt from ## conclusion we presented our approach to the shared task "discharge me!" at the bionlp workshop 2024. extracting the relevant sections from the ehr, we added explanatory prompts to these sections and concatenated them @xmath3. we then performed lora fine-tuning on the clinicalt5-large model. on the final test data, our approach achieved a rouge-1 score of 0.394, which is comparable to the top solutions.