Posts from February 2018

Linguamatics exhibiting at HIMSS18

The 2018 Annual HIMSS Conference & Exhibition takes place next month in Las Vegas, where the Sands Expo Convention Center will be filled with 40,000+ healthcare industry professionals, who have come together from around the world to discuss hot topics in health IT, industry issues and cutting-edge solutions.

For a fourth consecutive year, Linguamatics is proud to be exhibiting at HIMSS, an event that aims to help health IT professionals find innovative solutions to the challenges facing their organizations.

The five-day conference gives attendees the opportunity to educate themselves on ‘what’s new’ and ‘hot topics’ within the industry, and to participate in powerful networking sessions at breakfast networking events and the careers fair.

Linguamatics will showcase their world-leading NLP text mining software, I2E, which has social determinants of health, pathology and other healthcare modules set up to automatically “read” clinical documentation, and rapidly extract relevant elements into discrete values for analysis and effective decision support.


I2E Natural Language Processing improves Medicare Advantage reimbursement, streamlines ACO reporting and reduces care gaps

Boston, USA – February 1, 2018 – Natural Language Processing (NLP) text analytics provider Linguamatics today announced the implementation of the Linguamatics Health enterprise NLP platform, powered by I2E, at Atrius Health to identify and extract critical clinical information hidden within unstructured patient data.

Atrius Health is a non-profit healthcare leader providing primary care and specialty care to more than 740,000 adult and pediatric patients across eastern and central Massachusetts. As a long-term Accountable Care Organization (ACO) for Medicare, commercial, and Medicaid patients, Atrius Health requires ready access to clinical notes and data to address reporting requirements and advance quality care initiatives, including programs that require the proper identification of at-risk patients to minimize care gaps.