How to Build a Virtual Medical Scribe in Python Using Deepgram and OpenAI

An AI medical scribe can reduce clinicians’ documentation burden by combining Deepgram’s Nova-3 Medical speech-to-text model with OpenAI’s GPT-4.1 to convert doctor-patient conversations into structured SOAP notes for electronic health records. The tutorial explains why medical-specialized transcription and speaker diarization are important for accurately capturing clinical terminology and distinguishing participants, then demonstrates two implementation approaches in Python: processing prerecorded audio files through Deepgram’s API and streaming microphone audio live through Deepgram’s WebSocket endpoint. In both workflows, the transcript is sent to an LLM using a strict prompt that organizes available information into Subjective, Objective, Assessment, and Plan sections while avoiding unsupported assumptions. The prerecorded system reads an audio file, transcribes it, and writes a generated note to disk, while the real-time version uses PyAudio, asynchronous queues, and WebSockets to collect live speech, display finalized transcript segments, and create a SOAP note when recording ends. The article presents these tools as a way to let physicians concentrate more fully on patient interaction, while noting implementation details, required API keys, dependencies, sample testing methods, and a linked code repository.

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