Posts from June 2019

Data is now a vital part of the healthcare industry, and structured data alone will not be sufficient for better member outcomes. However, much of it is still inaccessible to analysts and experts, as precious information hidden within electronic health records (EHRs). Natural Language Processing (NLP) technology can unearth the 80% of unstructured information stored within the electronic health record.

NLP provides deeper, vital insights into members

NLP can help us to bring valuable missing pieces of information to the surface to cover risk and monitor patient’s health more closely. Disease severity is just one of those areas - for example cancer stage, is critical for proper member management, but the information isn’t always available consistently in structured claims, but is included in the unstructured data. Claims data gives a lot of detailed structured information about the individual, such as their medications, diseases they're suffering from, as well as procedures and treatments that they've had, but that only provides part of the full member picture.


NLP for FAIRification of unstructured data

Data is the lifeblood of all research, and pharmaceutical research is no exception. Clean accurate integrated data sets promise to provide the substrate for artificial intelligence (AI) and machine learning (ML) models that can assist with better drug discovery and development. Using data in the most effective and efficient way is critical - and improving scientific data management and stewardship through the FAIR (findable, accessible, interoperable, reusable) principles will improve pharma efficiency and effectiveness.

What is FAIR and how can NLP contribute?

The FAIR principles were first proposed in 2016, and this initial paper triggered not just discussions (see the recent Pistoia review paper), but in many organizations, the paper triggered action.

We are seeing applications of natural language processing (NLP) in FAIRification of unstructured data. Around 80% of information needed for pharma decision-making is in unstructured formats, from scientific literature to safety reports, clinical trial protocols to regulatory dossiers and more.

NLP can contribute to FAIRification of these data in a number of ways. NLP enables effective use of ontologies, a key component in data interoperability. Ensuring that life science entities are referred consistently across different data sources enables these data to be integrated and accessible for machine operations. Using a combination of strategies (e.g. ontologies, linguistic rules) NLP can transform unstructured data into formal representations, whether individual concept identifiers, or relationships such as RDF.


Spring has sprung!

Spring has always felt like a magical season to me. The dormancy of winter is set aside to allow for new growth, and the strategically placed butterfly garden outside my window is active with metamorphosis. Flowers transform overnight. As the transformation happens outside, I find many inner opportunities for growth taking place as well. I have personally experienced and observed in others that Spring brings out curiosity, the thirst of new discoveries, and the desire for inner growth.

The season of inspiration - even in healthcare

In healthcare, I attribute this to those striving for discoveries for better patient care. Luckily this “spring fever” has proven to be bountiful, and we have been able to witness this first hand. Throughout the country this Spring, Linguamatics has had the honor to host several one day seminars (and another in San Francisco yet to come!). These seminars have focused on how Artificial Intelligence, through Natural Language Processing (NLP), has inspired new discoveries covering a wide spectrum of applications in healthcare and pharmaceuticals. Proving once again that the talents from both industries work together for one common goal - to improve human health.

How are healthcare and pharmaceutical organizations using NLP?

Alyssa Hahn, University of Iowa, “Performance of NLP-Based Phenome Extraction from the EMR


Making effective use of available data. And prioritizing!

Healthcare is a business. A business with a huge, important task: to provide quality care to patients, while also dealing with ever higher patient loads and facing their own increasing physician burnout rates. Clinicians are faced with both administrative and clinical priorities - some of which conflict. So how do you prioritize what should come first? Like the old adage “which came first: the chicken or the egg?”, one can’t exist without the other. The provider can’t exist if the healthcare establishment goes bankrupt - and the healthcare business is nothing if it doesn’t have providers. Data and technology are potential ways to help ease this burden on clinicians and provider teams, enabling them to understand their patients better and streamline access to payer approvals.

Five ways natural language processing can help healthcare providers

You may be aware of Natural Language Processing (NLP) and its potential to augment the intelligence of the clinical workforce. Below we list just a few areas where NLP technology can help improve patient care while reducing administrative and reporting burdens:


Precision medicine focuses on disease treatment and prevention, at the clinical level (in healthcare organisations), and within drug discovery and development (in pharma companies). Treatments are developed and delivered, taking into account the variability in genes, environment, and lifestyle between individual patients. Within the clinical arena, in order to understand the best treatment pathway for a particular patient or group of patients, it is important to be able to access and analyze detailed information from the medical records of patients, and ideally broader aspects beyond their medical history.

A great example of precision medicine within the clinical arena was presented at the Linguamatics seminar in Chicago in March 2019. At the University of Iowa, scientists at the Stead Family Children’s Hospital are working on a precision medicine research project. Alyssa Hahn (Graduate Student, Genetics) described how they are using Linguamatics Natural Language Processing (NLP) to extract phenotype details from electronic medical records of patients with suspected genetic disorders.