Spoken input
Conversations have to be transcribed before they can be analysed.
Doctor Patient Conversation to Structured Clinical Data
The Healthcare Speech-NLP Model is a Technomous product that turns doctor-patient conversations into standardised clinical data, mapped to ICD-10 and SNOMED, for documentation and integration.

What stood in the way.
Doctor-patient conversations hold valuable clinical information, but converting them into standardised clinical data for documentation and integration is slow and inconsistent by hand.
Conversations have to be transcribed before they can be analysed.
Doctors and patients describe the same thing in different words.
Output must use recognised coding systems to integrate with other systems.
How we answered it.
We built a pipeline that processes the conversation through ASR, applies medical NLP and named entity recognition to identify symptoms, conditions, medications and procedures, normalises the vocabulary and maps it to ICD-10 and SNOMED, producing structured clinical output.
The parts that made the difference.
The model takes a full doctor-patient conversation as input.
Automatic speech recognition transcribes the conversation.
Medical NLP interprets the clinical content of the transcript.
NER picks out the clinical entities in the text.
What changed for the client.
The model turns consultations into data that is ready for documentation and integration.
What it was built with.
Part of our AI and machine learning work.
The next big thing starts with a conversation.
An idea, a challenge, a “what if”. Tell us what you have in mind, and we’ll figure out the next step together.
What happens next
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