Roughly 80% of clinical information is buried in formats that physicians can’t readily access, such as other clinicians’ notes, radiology reports, genomics data, discharge summaries, and fax documents.
These unstructured formats are beyond the grasp of most analytics systems, making it difficult for healthcare professionals to access and utilize the information.
Clinical Data Silos
Emtelligent, a company founded by radiologist Tim O’Connell, MD, has been working to change this for over a decade.
The company’s VP of GTM Strategy and Operations, Jack Mosey, explained that emtelligent spent its first 7 years building a platform to resolve the data silo problem, leveraging the rise of large language models and artificial intelligence.
O’Connell, who still practices, has said that the company strives to bring clinical credibility to a space increasingly crowded with AI entrants, as well as an engineering philosophy that prioritizes data quality.
Medical Language Engine
Emtelligent’s flagship product, the Medical Language Engine, uses large language models and natural language processing trained on more than 2 million expert-annotated data points to transform unstructured clinical text into structured, coded data mapped to standard ontologies, including SNOMED, ICD-10, and specialty-specific classifications.
The company reports 99% benchmark accuracy and has processed more than 5 billion clinical notes across more than 10 therapeutic areas.
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A second core product, Document Manager, handles the intake side, splitting, identifying, and categorizing complex paper-based and digital medical records so they become immediately usable.
Together, these tools underpin Clinical Workflow, an AI-assisted review interface designed for clinicians, coders, and reviewers who currently spend hours manually searching through voluminous, disorganized patient records.
The platform serves payers, health systems, pharmaceutical and life sciences companies, and health technology and data services providers, with the latter representing a particularly important distribution channel.
Mosey explained that emtelligent often functions as embedded infrastructure within larger data aggregator platforms, structuring the unstructured clinical data before it feeds downstream analytics models.
Oncology Applications
Emtelligent has achieved commercial traction with real-world evidence companies and data resellers serving pharma, with Optum serving as its beta customer when it came to market in 2023.
Revenue is currently in the $5 million to $10 million range, with approximately 50 employees.
In oncology, Mosey highlighted two high-growth use cases: supporting EHR-to-EDC workflows and improving patient identification and trial screening by surfacing granular clinical insights from records.
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Mosey also pointed to the need to make sense of the unstructured data being created due to the rise of AI scribe transcripts, a technology that has taken off in oncology.
He noted that emtelligent’s Medical Language Engine could potentially move the unstructured AI transcript through its system, resulting in a better quality note.
Emtelligent’s efforts to structure unstructured clinical data have the potential to add significant value in clinical research and analytics, care delivery, and the payer space.
As the use of AI and large language models continues to grow, companies like emtelligent are playing a critical role in helping healthcare professionals access and utilize the information they need to provide better patient care.
The rise of AI-powered workflow solutions, such as emtelligent’s Clinical Workflow, is also changing the way clinicians, coders, and reviewers work with patient records.
By automating the process of searching through voluminous, disorganized records, these solutions are reducing the burden on clinic staff and eliminating errors.
It is essential for healthcare professionals to have access to accurate and structured clinical data to provide the best possible care for their patients.
