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.


In healthcare, the excitement about the potential for big data and machine learning is palpable, and there is more accessible electronic information than ever before.

The challenge for the healthcare community is that approximately 80% of the data in a typical electronic health record (EHR) is trapped within unstructured notes, which requires expensive human annotation to make it accessible to machine learning systems.

So what’s the solution? The use of Natural language processing (NLP), another artificial intelligence (AI) technique, can turn this unstructured text into a set of features for machine learning to use. Data-driven, rule-based NLP techniques can extract information from text using linguistic patterns and terminologies with high precision and recall —avoiding the need to manually annotate training data for the machine learning model.

Read the full PM360 article to find out more about how the combination of NLP and machine learning can be a powerful tool for developing predictive models in healthcare and life science.

Read the full article

About David Milward:

David Milward is Chief Technology Officer at Linguamatics. He is a pioneer of interactive text mining, and a founder of Linguamatics. He has over 20 years of experience in natural language processing (NLP) product development, consultancy, and research.


With a background such as mine - medicine/ information technology/ government/ military - you need to know your audience, and ensure acronyms are appropriate.

In healthcare alone, DOA can mean several things: degenerative osteoarthritis, date of arrival, drug of abuse, dead on arrival, etc. Most of which I REALLY don’t want to see in a healthcare analytical report for Rheumatology.

Although ETL is no exception, it is widely used in the world of healthcare now as “Extract Transform and Load” and - unless you are speaking to a someone in the area of pulmonary and respiratory diseases - it will seldom get confused with “expiratory threshold load” which helps determine respiratory muscle efficiency. Then there is AMP, which in medicine is most commonly known as a adenosine monophosphate a vital component in all living cells. But for Linguamatics Health users, AMP is an acronym that is vital in it’s own right and stands for Asynchronous Messaging Pipeline.

Here at Linguamatics we are grateful to have some very talented folks that can explain our technological world in a way that is (sometimes) less technical. Alex Richard-Hoyling ( Senior Solutions Developer) explained how he helps ensure reliable data extraction in large healthcare systems via the Linguamatics Community. Below, I take the subject a step further to cross the chasm of where tech meets med.


How do you ensure your healthcare company outshines the competition with so many choices out there? There’s an app for that! Well no - not yet, at least there wasn’t at the time I wrote this blog- I double checked. There is however, the National Committee for Quality Assurance (although no app, they do have a very informative Twitter account.)

The committee’s mission is to help continually ensure quality in health from all parties involved. For insurance companies, they use the Healthcare Effectiveness Data and Information Set (HEDIS) as it is “one of the most widely used sets of health care performance measures in the United States.”[1]. So rather than trying to compare two things that may sound like they are certainly similar, such as ‘pineapples to apples’, people now have a true method of payer comparison.

Download the PDF: Case Study on Big Data Analytics for Population Health

HEDIS consists of a set of measures around patient care and service. Measures vary from simple documentation of an adult Body mass index (BMI), a calculation involving only height and weight; to the more complicated documentation of comprehensive diabetes care.