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Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete. arXiv: 2007.15153, 2020; Greenbaum et al., Contextual autocomplete: a novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department, 2017) that have implemented contextual autocompletion in electronic medical records and their promising results with regards to time saved for clinicians. The goals of this chapter are to introduce to the curious healthcare provider the basics of natural language processing, zoom into the use case of contextual autocomplete for electronic medical records, and provide a hands-on tutorial that introduces the basic NLP concepts required to build a model for predictive suggestions.

Natural language processing (NLP) is the task of converting unstructured human language data into structured data that a machine can understand. While its applications are far and wide in healthcare, and are growing considerably every day, this chapter will focus on one particularly relevant application for healthcare professionals-reducing the burden of clinical documentation. More specifically, the chapter will discuss two studies (Gopinath et al., Fast, structured clinical documentation via contextual autocomplete. arXiv: 2007.15153, 2020; Greenbaum et al., Contextual autocomplete: a novel user interface using machine learning to improve ontology usage and structured data capture for presenting problems in the emergency department, 2017) that have implemented contextual autocompletion in electronic medical records and their promising results with regards to time saved for clinicians. The goals of this chapter are to introduce to the curious healthcare provider the basics of natural language processing, zoom into the use case of contextual autocomplete for electronic medical records, and provide a hands-on tutorial that introduces the basic NLP concepts required to build a model for predictive suggestions.