Healthcare
Lauren Barham

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.

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Company news

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.

“A vast amount of critical clinical data exists as unstructured text which is difficult to access and analyze. We are leveraging the power of NLP to replace the manual, inefficient data extraction processes that many healthcare organizations struggle with, in order to advance our quality care initiatives more rapidly,” said Joe Kimura, MD, Atrius Health’s chief medical officer. “Linguamatics NLP allows us to close gaps in care, enhance clinical documentation for chronic disorders, reduce litigation risks, and streamline Medicare ACO quality reporting.”

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Life Sciences
Jane Reed

Understanding drug-drug interactions can improve drug safety

A considerable proportion of adverse drug events are caused by interactions between drugs. With an ageing population, and associated increasing multiplicity of age-related illnesses, there is an increase in the potential for increased risk of drug-drug interactions (DDIs). One way of alleviating some DDIs is by ensuring that potentially interacting drugs are taken at suitable time intervals apart. But, what is the best interval to recommend?

In a recent seminar, Keith Burkhardt of the FDA described a project using text mining to survey the landscape of information on DDIs from FDA Drug Labels. And, in particular, the FDA review division wanted to find labelling for drugs where the time separation was stated, in order to prevent potential drug safety events.

Mining Data from FDA Drug Labels: dosing regimens and time separation

The drug classes of interest included bile acid sequestrants and exchange resins (such as cholestyramine, colestipol, colesevelam, all LDL cholesterol lowering drugs), phosphate binders (e.g. sevelamer; used for patients with chronic kidney failure), and chelators (used to treat excessively high levels of lead, iron or copper in the blood; e.g. deferasirox, deferiprone). These drug classes can all alter the bioavailability of other drugs, particularly for those with a narrow therapeutic range such as warfarin or antiepileptic drugs.

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Healthcare
Liz Marshall

If you have any experience with military and leadership (actual or even observed) you probably have at least heard of the book, Art of War written by a great Chinese military strategist Sun Tzu.  It’s amazing how much wisdom is still valid and can be derived from a piece of 5th century BC literature. There are many knowledgeable quotes for leadership and life from the very famous, “Keep your friends close, and your enemies closer.” to the, “Know yourself and you will win all battles.” As brilliant as most Sun Tzu’s advice is, when it comes to the battle on cancer, it’s much more complex- how do you win a battle if you ARE the enemy?

Battling with Cancer Cells: Being your Own Worst Enemy

“To know your Enemy, you must become your Enemy.” - cancer has mastered that.

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Company news

New innovations increase democratic and programmatic access to text analytics, expanding and speeding the transformation of unstructured data for knowledge discovery

Cambridge, UK & Boston, USA – December 7th, 2017 – Natural Language Processing (NLP) text analytics provider Linguamatics today announced that the company has extended its position as a leading provider of NLP text-mining solutions for healthcare and the life sciences with the introduction of multiple innovations in 2017.

New capabilities include custom search interfaces using I2E web portals, providing much larger, wide-ranging user communities with web access to the benefits of deep NLP capabilities. Programmatic use of I2E is enhanced by I2E AMP, which delivers high-throughput, fault tolerant workflow management for real-time document processing. AMP is deployed at multiple customer sites in a variety of workflows, including safety and regulatory affairs.

“Over the last year more customers have taken advantage of the capabilities of I2E with AMP for ETL (Extract, Transform, and Load) use cases,” said Linguamatics Chief Business Development Officer Phil Hastings. “It’s exciting to see the numerous ways customers are benefitting from the ability to transform their unstructured data to a structured format that can be loaded into data warehouses or data lakes, or consumed by downstream applications for broader access and decision support.”

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