Translating Natural Language Processing into Mainstream Schizophrenia Assessment

Abstract

Natural language processing (NLP) is a multidisciplinary field that involves objectifying aspects of language. Specifically, it understands human language by leveraging statistical and linguistic knowledge. NLP’s potential for enhancing the administration, accuracy, and objectivity of clinical assessments in psychiatry has been touted, as well as its potential for promoting equity in health care. This can be achieved through large-scale administration/automation, which in turn can improve the quality and frequency of services, and better connect people to their support teams; particularly those from underserved and marginalized communities. However, implementing NLP for clinical assessment is a complex endeavor that requires robust systems for ensuring reliability, validity, transparency, human oversight, and legal regulation of the resulting algorithmic and technological solutions. Adoption of any technology has both intended and unintended consequences, and this will probably be the case when leveraging NLP technology within schizophrenia assessment. The excitement around NLP’s potential in assessment in schizophrenia research has an almost frenzied feel to it. This can be seen in the steady increase in scientific articles and editorials1–4 and healthcare applications (eg,5–7). This seems like a good moment for calm reflection to consider the need for explicit research frameworks and trustworthy roadmaps for the journey ahead for both research purposes and for the eventual implementation of NLP-based tools in clinical practice. This themed issue of Schizophrenia Bulletin intends to provide such a moment of thoughtful reflection; and in doing so, contribute to a pathway for implementation in mainstream schizophrenia assessment. To do so we consider what realistically we should be expecting from machines and how we can meet this goal.