Kyle Silvestro is founder and CEO of
Tell me about yourself and the company.
I’ve been in the world of clinical natural language processing and semantic interoperability for the last decade. My team collectively has been in the industry for more than 45 years.
As a company, we focus on the world of data. We look at ourselves as an oil refiner, taking all the data that’s being created — transcription, dictation, typed notes, structured order entry, what have you — and creating a refinery process that we put it through. On the other side of that, we get structured data that’s semantically interoperable.
We focus on that pipeline that allows organizations to create normalized data to drive down to processes like analytics, decision support and population health.
People often get natural language processing confused with speech recognition. Describe NLP.
It’s the ability for the computer to go through a written document — a Word document, PDF, or something that is the by-product of speech recognition – and recognize and understand the content. Not only the content, the meaning behind the content as far as it’s something positive, something negative, or something concerning. Beyond that, be able to make decisions as far as how that should be encoded with a terminology or medical knowledge base such as SNOMED, ICD-9, or ICD-10.
I’m a huge fan of keeping the clinical narrative and patient narrative and not just discrete data element factoids. Is there a demand for that?
It’s interesting what’s occurred over the last decade and really the last several years. Data has become important and incentives are changing to where they’re making data much more relevant in the chain of care. As organizations are looking at this, they’re looking at a lot of claims data, which gives you an incomplete picture.
Until you start marrying the clinical narrative with the claims data, you are not going to see the outcomes or the population that needs to be managed comprehensively as you would just looking at a single point of data. The market is realizing that the data is important and the data is the key for them to being successful.
How good is NLP’s inference capability in reliably turning free text into discrete data?
That’s a question we get asked frequently. My response back is, how accurate is the physician’s note? At times, and depending on where you are across the nation, the note may mean different things. Words may mean different things, context may be a little bit different.
It’s about being able to create a ability to normalize that information and then continuously learn on top of it. Create a feedback loop of this data to ensure that the inferencing or accuracy gets extremely high. Once it’s extremely high, you can build some …read more
