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Electronic patient charts are essential for follow-up and multi-disciplinary care, but either take up an exorbitant amount of time during the patient encounter using a key-stroke entry system, or suffer from poor recall when made long after the encounter. Transcribing in-situ, natural dictations by the clinician, recorded during the encounter, with minimal workflow impact, is a promising solution. However, human transcription requires significant manual resources, whereas automated transcription currently lacks the accuracy for specialized clinical language. Our ultimate goal is to automate clinical transcription, particularly for Emergency Departments, with as an end-result a structured SOAP report. Towards this goal, we present the Adaptive Clinical Transcription System (ACTS). We compare the accuracy and processing times of state-of-the-art speech recognition tools, studying the feasibility of streaming-style dynamic transcription and opportunities of incremental learning.

Towards an Adaptive Clinical Transcription System for In-Situ Transcribing of Patient Encounter Information