Speed up hospital discharge note writing, reduce doctors' workload, and minimize patient risks.
The process of writing discharge notes by doctors in hospitals is currently plagued by two major issues: excessive time consumption and increased risk of patient infection. These problems arise due to the additional time required for hospitalization and the potential transmission of infections. Addressing these concerns is essential to streamline the discharge process, optimize healthcare provider productivity, and prioritize patient safety.
Both these issues compromise the quality of healthcare delivery and patient outcomes. The protracted discharge note writing process strains healthcare resources, affecting both patients' timely discharge and doctors' ability to attend to other critical duties. Moreover, the augmented risk of patient infection threatens the fundamental principle of healthcare, which is to 'do no harm.' Thus, it is imperative to devise interventions that address the excessive time spent on discharge note writing while concurrently mitigating the potential transmission of infections to safeguard patient safety and optimize healthcare operations.
To address the challenges associated with the time-consuming process of writing discharge notes in hospitals, as well as the increased risk of patient infection, an AI-powered Language Model (LM) can be utilized to assist doctors in drafting comprehensive and accurate discharge notes. Leveraging the capabilities of a Large Language Model (LLM), doctors can provide prompts and receive real-time assistance in generating the necessary documentation.