Nabla Case Study: how OpenAi Whisper is used in Healthcare

Recently, there's been a lot of talk about Whisper, an open source speech-to-text engine developed by OpenAI. Here is how we use Whisper at Nabla.

1️⃣ The well-documented Whisper issues the article highlights are exactly why we spent 3 years and $5m, gathered and manually annotate a unique dataset of 7000 hours of medical encounters audio, and leveraged that dataset to train our own model.

2️⃣ Our model is based on Whisper, but it contains many improvements specifically developed to suppress hallucinations and make the transcription of medical terms more accurate than any off-the-shelf speech-to-text engine on the market.

3️⃣ In addition, the transcript is not directly included in the patient record. A second layer of processing extracts relevant data from the transcript. Each note produced is split into atomic facts, which are checked via an LLM query against the transcript and the patient context. Only facts for which we find definitive proof are considered valid.

4️⃣ After data is extracted, it is manually reviewed, edited and corrected if needed by the same clinician who did the encounter. Nabla also enforces that the review happens shortly after the encounter.

5️⃣ Each new version of our model undergoes rigorous evaluation by human professionals to ensure the documentation is both comprehensive and aligned with industry standards.

6️⃣ We have processed 9 million medical encounters in the past 24 months and got direct feedback from nearly 10,000 physicians about ways to improve Nabla. While some transcription errors were sometimes reported, hallucination has never been reported as a significant issue.

Author

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