A Pilot Study Using Natural Language Processing to Explore Textual Electronic Mental Healthcare Data - Gayathri Delanerolle; Yassine Bouchareb; Suchith Shetty; Heitor Cavalini; Peter Phiri (Informatics)
Mental health illness is the single biggest cause of inability within the UK, contributing up to 22.8% of the whole burden compared to 15.9% for cancer and 16.2% for cardiovascular disease. The more extensive financial costs of mental ailments in Britain have been evaluated at British Pound Sterling (GBP) 105.2 billion each year. This burden could be decreased with productive forms and utilization of computerized innovations. Electronical health records (EHRs), for instance, could offer an extraordinary opportunity for research and provide improved and optimized care. Consequently, this technological advance would unburden the mental health system and help provide optimized and efficient care to the patients. Using natural language processing methods to explore unstructured EHR text data from mental health services in the National Health Service (NHS) UK brings opportunities and technical challenges in the use of such data and possible solutions. This descriptive study compared technical methods and approaches to leverage large-scale text data in EHRs of mental health service providers in the NHS. We conclude that the method used is suitable for mental health services. However, broader studies including other hospital sites are still needed to validate the method.