VIEWER: an extensible visual analytics framework for enhancing mental healthcare

Abstract: Objective: A proof-of-concept study aimed at designing and implementing VIEWER, a versatile toolkit for visual analytics of clinical data, and systematically evaluating its effectiveness across various clinical applications while gathering feedback for iterative improvements. Materials and Methods: VIEWER is an open-source and extensible toolkit that employs natural language processing and interactive visualisation techniques to facilitate the rapid design, development, and deployment of clinical information retrieval, analysis, and visualisation at the point of care. Through an iterative and collaborative participatory design approach, VIEWER was designed and implemented in one of the UK's largest NHS mental health Trusts, where its clinical utility and effectiveness were assessed using both quantitative and qualitative methods. Results: VIEWER provides interactive, problem-focused, and comprehensive views of longitudinal patient data (n=409,870) from a combination of structured clinical data and unstructured clinical notes. Despite a relatively short adoption period and users' initial unfamiliarity, VIEWER significantly improved performance and task completion speed compared to the standard clinical information system. More than 1,000 users and partners in the hospital tested and used VIEWER, reporting high satisfaction and expressed strong interest in incorporating VIEWER into their daily practice. Conclusion: VIEWER was developed to improve data accessibility and representation across various aspects of healthcare delivery, including population health management and patient monitoring. The deployment of VIEWER highlights the benefits of collaborative refinement in optimizing health informatics solutions for enhanced patient care.