Natural Language Processing and the Promise of Big Data

Abstract

The promise of big data has captured healthcare’s imagination. Although the term lacks a consensus definition, it generally refers to electronic health data sets characterized by the 3 Vs: volume, variety, and velocity.1,2 Volume refers to the sheer amount of healthcare data currently generated by clinical operations, administration, and patients themselves. By one estimate, ≈25 000 petabytes of healthcare data will be available by 2020—an amount that could fill 500 billion file cabinets.2 Variety refers to the wide range of healthcare data formats. For example, electronic health records (EHRs) contain both structured and unstructured (or free-text) data, diagnostic images come in a variety of multimedia formats, and patient data are generated from wearables, mobile devices, medical devices, and social media—each with its own format. Velocity refers to the rapidity with which new data are generated, and thus the speed at which it needs incorporation into data sets and analyses to provide real-time insights into health care.Article see p 477The potential of such data is enormous. Insights from big data could fuel innovation and improvement in clinical operations, research and development, and public health.1 However, the potential of big data to realize these lofty aspirations is matched by the challenge of organizing, analyzing, and generating actionable insights from it.One of the biggest challenges in realizing the potential of big data is in abstracting it. With the passage of the HITECH (The Health Information Technology for Economic and Clinical Health) Act in 2009, the adoption of EHRs in clinical practice has accelerated, and now over half of office-based practices and hospitals are using some form of EHR.3,4 As a result, more point-of-care clinical data, previously inaccessible in its paper format, is potentially available. However, the variety aspect of EHR data—its mix …