The goal of this study is to learn whether an artificial intelligence (AI) tool built into the electronic health record helps hospital physicians write discharge summaries. A discharge summary is the document that tells a patient's family doctor and other providers what happened during a hospital stay.
The main questions this study aims to answer are:
Does access to the AI tool reduce the time physicians spend writing discharge summaries? Does it change the quality of discharge summaries or how soon they are completed? How does it affect physician workload?
Physicians will be randomly assigned to either have access to the AI tool or complete discharge summaries as they usually do. Physicians with access can choose whether to use the tool, and they review and edit any AI-generated text before it becomes part of the discharge summary.
An EHR-embedded Pragmatic Parallel-group, Operationally-driven Clinical Trial of AI-assisted Discharge Summary Generation
Discharge summaries are a key communication tool at care transitions, but they are time-consuming to write and are often delayed or incomplete. Generative AI tools embedded in the electronic health record can draft a summary of the hospital course from existing chart data, but their effect on physician time, documentation quality, and workload has not been evaluated in a randomized trial.
This multicenter pragmatic trial randomizes staff physicians on general medicine/hospitalist services to access to the Epic Hospital Course Generator, a large language model tool that drafts a hospital course narrative on request, or to usual care. Documentation time and completion timing are measured with Epic audit log metadata. Documentation quality is assessed by expert physician review of a sample of discharge summaries and by an automated LLM-as-judge approach using the PDSQI-9. Physician workload and tool usability are assessed by survey.
