Clinical narratives are used as a key communication stream within healthcare and associated research. In comparison to structured elements of electronic health records, free text conveys individualized patient history and assessments and provides a rich context for clinical decision making. Natural language processing (NLP), a foundational element of text analytics, has repeatedly demonstrated its feasibility to explore information described in free text efficiently and effectively. The development of text analytics beyond the basic NLP has been facilitated by increased adoption of machine learning techniques. In particular, recent years have witnessed the greatest leap in performance in the history of computer science following the arrival of deep learning. Human-like performance in a wide range of text analytics tasks has opened the gate to its routine use in various clinical care settings. Unfortunately, clinical narratives are yet to be routinely analysed on a large scale. This is largely associated with accessibility of clinical narratives in relation to privacy concerns. Another reason for the slow adoption of text analytics in routine clinical care is the scarcity of evidence that clearly demonstrates its utility in the wider context of clinical applications. The objective of this special issue is to improve the evidence base of such utility by evaluating text analytics methods in the context of their clinical applications. Therefore, this special issue will focus on integration of text analytics into clinical practice. In addition to standard evaluation of text analytics methods in terms of their performance, we would like to encourage careful consideration of their effects on the clinical environment and, where appropriate, provide secondary evaluation. We cordially invite investigators to contribute their original research articles with an emphasis on real-life applications in this area. Topics to be covered include, but are not limited to a combination of the following tasks, methods, data types and applications: