Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK. Issue 3 (25th March 2021)

• Record Type: Journal Article
• Authors: Chilman, Natasha; Song, Xingyi; Roberts, Angus; Tolani, Esther; Stewart, Robert; Chui, Zoe; Birnie, Karen; Harber-Aschan, Lisa; Gazard, Billy; Chandran, David; Sanyal, Jyoti; Hatch, Stephani; Kolliakou, Anna; Das-Munshi, Jayati
• Is Part Of: BMJ open. Volume 11:Issue 3(2021)
• Publication Date: 2021-03-25
• Subjects: mental health -- health informatics -- epidemiology -- adult psychiatry; Medicine -- Research -- Periodicals; 610.72
• DOI: 10.1136/bmjopen-2020-042274
• Languages: English
• ISSNs: 2044-6055

Abstract

Abstract : Objectives: We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes. Design: Development and validation of a natural language processing application using General Architecture for Text Engineering software to extract occupations from de-identified clinical records. Setting and participants: Electronic health records from a large secondary mental healthcare provider in south London, accessed through the Clinical Record Interactive Search platform. The text mining application was run over the free-text fields in the electronic health records of 341 720 patients (all aged ≥16 years). Outcomes: Precision and recall estimates of the application performance; occupation retrieval using the application compared with structured fields; most common patient occupations; and analysis of key sociodemographic and clinical indicators for occupation recording. Results: Using the structured fields alone, only 14% of patients had occupation recorded. By implementing the text mining application in addition to the structured fields, occupations were identified in 57% of patients. The application performed on gold-standard human-annotated clinical text at a precision level of 0.79 and recall level of 0.77. The most common patient occupations recorded were 'student' and 'unemployed'. Patients with more service contact were more likely to have an occupation recorded, as were patients of a male gender, older age and those living in areas of lower deprivation. Conclusion: This is the first time a natural language processing application has been used to successfully derive patient-level occupations from the free-text of electronic mental health records, performing with good levels of precision and recall, and applied at scale. This may be used to inform clinical studies relating to the broader social determinants of health using electronic health records.