Learning to Write Notes in Electronic Health Records
This addresses clinician burden and burnout in healthcare by potentially reducing documentation time, though it is incremental as it builds on existing language modeling approaches.
The paper tackled the problem of clinicians spending excessive time writing notes in Electronic Health Records by proposing an AI-assisted note-writing task that predicts note content from patient data, finding that much content can be predicted and common templates learned.
Clinicians spend a significant amount of time inputting free-form textual notes into Electronic Health Records (EHR) systems. Much of this documentation work is seen as a burden, reducing time spent with patients and contributing to clinician burnout. With the aspiration of AI-assisted note-writing, we propose a new language modeling task predicting the content of notes conditioned on past data from a patient's medical record, including patient demographics, labs, medications, and past notes. We train generative models using the public, de-identified MIMIC-III dataset and compare generated notes with those in the dataset on multiple measures. We find that much of the content can be predicted, and that many common templates found in notes can be learned. We discuss how such models can be useful in supporting assistive note-writing features such as error-detection and auto-complete.