Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"
This work addresses clinician burnout and repetitive documentation in healthcare, but it is incremental as it focuses on benchmarking existing methods rather than proposing new ones.
The paper tackled the problem of automating clinical text generation to reduce physician workload by introducing two shared tasks: RRG24 for radiology report generation and 'Discharge Me!' for discharge summary generation, with submissions from 8 and 16 teams respectively.
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!".