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It is well known that the drug discovery and development process is lengthy, expensive and prone to failure. Starting from the selection of a novel target in discovery, through the multiple steps to regulatory approval, the overall probability of success is less than 1%.

One factor is that the majority of diseases are multifaceted, hence the challenge is identifying the most appropriate patient populations who will respond to specific interventions. A stratified approach has proven beneficial in a number of cancers and genetic diseases, and pharmaceutical companies have a strong interest in understanding how to find the sub-populations of patients to ensure the most appropriate therapies are tested in clinical trials, and applied in broader clinical use.

The ultimate aim of a stratified approach to medicine is to enable healthcare professionals to provide the “right treatment, for the right person, at the right dose, at the right time”; and there are many research initiatives (governmental, private, public) on-going to develop the appropriate knowledge and models.


Physicians at breaking point

Unsurprisingly, physicians who are constantly under peak pressure have the highest rate of burnout with an average of 45.8%. However, the source states emergency physicians claim a whopping 60% burnout rate. I also recently received an unverified Tweet about the life expectancy for physicians in this specialty, and the news just gets worse. It’s almost 20 years less than other specialties. I am unsure if it’s that much however, if you have ever ventured into an emergency department you can see for yourself why this may be true.


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


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