Realizing the full potential of electronic health records: the role of natural language processing

Abstract

Meaningful use of electronic health records (EHRs) for patient care or for research requires data to be comparable. Many portions of EHRs continue to be unstructured, presenting significant challenges for biomedical informatics. This issue of the journal displays several solutions to this problem that are based on natural language processing (NLP) techniques. A high-level review by Nadkarni ( see page 544 ) is intended to introduce the main components of NLP for the novice, and to briefly describe machine learning methods that are successfully being employed in the field. It includes a discussion on Watson, a contestant on ‘Jeopardy!,’ a popular question-and-answer TV show, and the ensuing speculations about its potential extensions to medical NLP. However, despite some notable examples of successful NLP applications in clinical care, progress in the field has been relatively slow. Chapman and colleagues ( see page 540 ) discuss the need to steer current NLP research efforts so that new developments can be accelerated, and research products can become readily usable in healthcare applications. The authors advocate for a concerted, collaborative effort to develop open-source software components and infrastructure to share annotated data and tools. The guest editorial raises important questions …