November always brings to mind Thanksgiving and turkey, but for those of us in medical informatics it means it’s time for AMIA’s national symposium and this time it is back in Washington DC. With political upheaval in healthcare and the opioid epidemic making headlines across the nation there will be no shortage of talking points. AMIA brings together some of the best and the brightest minds in medical informatics and a great place to engage with each other to highlight opportunities for IT to improve the lives of Americans with technology.

This will be my fifth time going to AMIA, a relative novice compared to many, but I find the scale of this event much more palatable than the behemoth that is HIMSS, with a more forward-looking vibe. Linguamatics have many events planned for AMIA this year, including a pre-symposium talk, a presentation on physician metrics by MUSC and a Learning Showcase presentation. Check out the details below and stop by and see us at booth #205.

Using Text Mining to Identify Risk of Opioid Medication Abuse

Presented by Erin Tavano, Clinical Data Scientist, Linguamatics

8:30AM – 4:30PM, Saturday November 4, 2017, Georgetown East


Media reports on the opioid abuse crisis in the U.S. increasingly dominate our TV screens and news feeds. But when do we start recognizing these statistics as actual people in need of help—what if we can identify at-risk individuals while they still have options?

Early recognition of opioid misuse is key to identifying people at risk and getting them timely treatment, but those “dancing with the devil” are hard to find in the early stages, and rarely come forward on their own.

In an article for HITECH Answers, Linguamatics’ Dr. Elizabeth Marshall reflects on how the crisis has affected her on a personal level. She looks at how using natural language processing (NLP) to analyze structured and unstructured data from sources such as clinician notes could help identify patterns that reveal possible opioid abuse. Dr. Marshall also suggests further measures that organizations, institutions, and communities might take to combat this growing epidemic.

Read the full article here.


 “Sometimes I think we’re becoming more of a data analytics company than anything else”

Humana Chief Medical Officer Roy Beveridge, M.D

Why are top payers investing in analytics?

In a recent Fierce Healthcare interview, Humana highlighted their long term commitment to analytics. Humana’s focus on Medicare Advantage means that they are increasingly in data partnerships with hospitals to provide insights and support through population health tools. What is fascinating to me, is this type of partnership would never have been possible in a fee-for-service world, and reflects the move to more value-based-care.

Population Health analytics

Population health tools in this space need to work with the heterogeneous data sets payers receive and are used to characterize and support their members. Many groups I have spoken to stratify high risk individuals in these data sets; for example smokers with heart disease are identified and given guidance by payers on quitting and incentives for exercise programs. I especially admire the way value-based providers and payers are working together to allow advice on high risk individuals to be given directly to the clinicians.


In the world of healthcare, quality measurement data collection and reporting is far from perfect. Patients move around, and transferring their health records from one organization to another creates a mass of documents that includes a large amount of unstructured data—around 80% of the medical record. Ignoring these unstructured notes often leads to reduced performance on quality measures.

HEDIS® (Healthcare Effectiveness Data Information Set) was “born” in 1991, making it a millennial in demographic terms. It was created to enable the health of patient populations to be assessed consistently, and has matured into a reliable means of comparing health plans and providers. Several HEDIS® measures combine structured and unstructured data, and Linguamatics Health, a natural language processing (NLP) platform powered by I2E, can help gather insights from unstructured text and move your HEDIS® scores “out of the basement,“ as every millennial’s parent aspires to do.

Read the full “Linguamatics Health for quality measures” application note to find out more about how powerful NLP solutions such as Linguamatics Health enable quality measures to be extracted automatically from clinical documentation, streamlining the collection of data.


Interest in artificial intelligence (AI), particularly natural language processing (NLP) and machine learning, has grown significantly within the healthcare community in recent years, as vendors, researchers, and providers look for ways to transform medical research and care through technology.

How do these techniques work? Machine learning can help to solve complex issues by analyzing existing data from sources such as electronic health records (EHRs), but often the data contained within EHRs is “trapped” in unstructured medical notes. NLP can interpret structure and meaning in this unstructured text, and make critical information accessible to machine learning applications.

Read the full Health IT Outcomes article to find out more about how the combination of NLP and machine learning can deliver a powerful solution for advancements in the understanding and delivery of care.

Read the full article

About Simon Beaulah:

Simon Beaulah is Linguamatics’ senior director of healthcare and is responsible for the company’s healthcare products and solutions, including applications for clinical risk models, population health, and medical research.