Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"
<|reference_start|>Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!": 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!".<|reference_end|>
@article{xu2024overview,
    title={Overview of the First Shared Task on Clinical Text Generation: RRG24 and
  "Discharge Me!"},
    author={Justin Xu, Zhihong Chen, Andrew Johnston, Louis Blankemeier, Maya
  Varma, Jason Hom, William J. Collins, Ankit Modi, Robert Lloyd, Benjamin
  Hopkins, Curtis Langlotz, Jean-Benoit Delbrouck},
    journal={Proceedings of the 23rd Workshop on Biomedical Natural Language
  Processing (2024) 85-98},
    year={2024},
    doi={10.18653/v1/2024.bionlp-1.7},
    archivePrefix={arXiv},
    eprint={2409.16603},
    primaryClass={cs.CL}
}