Challenges in adapting existing clinical natural language processing systems to multiple, diverse health care settings

Abstract

Abstract Objective: Widespread application of clinical natural language processing (NLP) systems requires taking existing NLP systems and adapting them to diverse and heterogeneous settings. We describe the challenges faced and lessons learned in adapting an existing NLP system for measuring colonoscopy quality. Materials and Methods: Colonoscopy and pathology reports from 4 settings during 2013–2015, varying by geographic location, practice type, compensation structure, and electronic health record. Results: Though successful, adaptation required considerably more time and effort than anticipated. Typical NLP challenges in assembling corpora, diverse report structures, and idiosyncratic linguistic content were greatly magnified. Discussion: Strategies for addressing adaptation challenges include assessing site-specific diversity, setting realistic timelines, leveraging local electronic health record expertise, and undertaking extensive iterative development. More research is needed on how to make it easier to adapt NLP systems to new clinical settings. Conclusions: A key challenge in widespread application of NLP is adapting existing systems to new clinical settings.