Could Natural Language Processing be Useful in HEDIS® Reporting?
Linguamatics
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.