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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.


Author: Matthew Flores MS, RRT, CHCA  

Before we assess whether Natural Language Processing (NLP) could benefit HEDIS® reporting, it is important to look at the history of HEDIS as well as some of the information surrounding trends in quality reporting from a regulatory and operational standpoint to put the question into perspective.

The Setting

The Healthcare Effectiveness Data Information Set (HEDIS) is an important set of healthcare quality indicators developed and administered by the National Committee for Quality Assurance (NCQA) with the goal of improving the triple aim in healthcare. This is accomplished by measuring care provision at the payer level which has historically relied heavily on claims and other administrative data as the primary means for measuring clinical activities.

When HEDIS started, administrative (e.g. claims) data was the primary type of clinical information most health plans received for their patients. Over time, Hybrid measures were added using Medical Record Review (MRR) to bridge the gap of information not received in administrative data for some measures. HEDIS evolved to incorporate supplemental data from various other data sources such as immunization registries and eventually EHRs.


How the Medical University of South Carolina (MUSC) is using Natural Language Processing to improve clinical care

Social determinants of Health (SDoH) are a top priority of agencies globally such as the World Health Organization (WHO), as well as back here in the U.S. where the Center for Disease Control (CDC) has its own variation of goals per Healthy People 2020. The exact definition of what is included in SDoHs varies - but what remains clear is that they are social factors which impact the health of individuals. These may include a myriad of components, such as: stress, social isolation, employment (or lack of), social support, addiction, food insecurity, transportation issues, etc. SDoHs are primarily found within the clinician narrative in electronic health records (EHR), and are difficult to find when trying to identify individuals to ensure proper care.

Sometimes physicians focus excessively on the ‘medical’ problems and don’t pay enough attention to the context that people live in and the social aspects that influence their health. Our study [utilizing Linguamatics NLP] once again highlights the importance of knowing this information in order to provide patients our very best care.

- Leslie Lenert, M.D., MS, Chief Research Information Officer for MUSC and director of MUSC’s Biomedical Informatics Center (BMIC) 1


Novo Nordisk uses Linguamatics NLP in groundbreaking project to mine real world data insights

Novo Nordisk has received one of the three Bio-IT World Innovative Practices Awards for their workflow to integrate NLP to generate actionable insights from real world data, during the Bio-IT World Conference and Exposition 2019 in Boston. Linguamatics, the leading Natural Language Processing (NLP) solution provider, is the Novo Nordisk NLP technology partner. Linguamatics and Novo Nordisk were among the 9 projects and 15 organizations selected as finalists for this prestigious award.

Bio-IT World’s Innovative Practices Award recognizes partnerships and projects pushing the life science industry forward, by highlighting examples of how technology innovations and strategic initiatives can be powerful forces for change. After winning Bio-IT World’s Best of Show Judges’ Prize in 2018 with iScite 2.0, the Linguamatics team is extremely proud of this new accomplishment.

Novo Nordisk is a global healthcare company with more than 90 years of innovation and leadership in diabetes care. This heritage has given them experience and capabilities that also enable them to help people defeat other serious chronic conditions: diabetes, haemophilia, growth disorders and obesity.