Posts from May 2019

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