Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"

TL;DR

This paper presents a shared task on clinical text generation, focusing on radiology report and discharge summary generation to automate healthcare documentation and reduce clinician workload.

Contribution

It introduces two novel shared tasks, RRG24 and 'Discharge Me!', to evaluate AI systems in generating specific clinical report sections from medical data.

Findings

201 submissions for RRG24 from 8 teams

211 submissions for 'Discharge Me!' from 16 teams

Clinician review of 'Discharge Me!' submissions

Abstract

Recent developments in natural language generation have tremendous implications for healthcare. For instance, state-of-the-art systems could automate the generation of sections in clinical reports to alleviate physician workload and streamline hospital documentation. To explore these applications, we present a shared task consisting of two subtasks: (1) Radiology Report Generation (RRG24) and (2) Discharge Summary Generation ("Discharge Me!"). RRG24 involves generating the 'Findings' and 'Impression' sections of radiology reports given chest X-rays. "Discharge Me!" involves generating the 'Brief Hospital Course' and 'Discharge Instructions' sections of discharge summaries for patients admitted through the emergency department. "Discharge Me!" submissions were subsequently reviewed by a team of clinicians. Both tasks emphasize the goal of reducing clinician burnout and repetitive workloads by generating documentation. We received 201 submissions from across 8 teams for RRG24, and 211 submissions from across 16 teams for "Discharge Me!".