Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Apr 5, 2021
Date Accepted: Dec 4, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Development of an Automated Solution for Large Scale Health Service Feedback: Using NLP and Topic Modelling techniques
ABSTRACT
Obtaining patient feedback is an essential mechanism for healthcare service providers to assess their quality and effectiveness. Unlike assessments of clinical outcomes, feedback from patients offers insights into their lived experience. The Department of Health and Social Care in England via NHS Digital operates a patient feedback web service through which patients can leave feedback of their experiences into structured and free-text report forms. Free-text feedback compared to structured questionnaires may be less biased by the feedback collector thus more representative; however, it is harder to analyse in large quantities and challenging to derive meaningful, quantitative outcomes for better representation of the general public feedback. This study details the development of a text analysis tool that utilises contemporary natural language processing (NLP) and machine learning models to analyse free-text clinical service reviews to develop a robust classification model, and interactive visualisation web application based on a Vue.js application with NodeJS, working with a C# serverless API and SQL server all hosted on Microsoft Azure Platform, which facilitates exploration of the data, designed for the use by all stakeholders. Of the 11,103 possible clinical services that could be reviewed across England, 2030 different services had received a combined total of 51,845 reviews between 1/10/2017 and 31/10/2019; these were included for analysis. Dominant topics were identified for the entire corpus and then negative and positive sentiment topics in turn. Reviews containing high and low sentiment topics occurred more frequently than less polarised topics. Time series analysis can identify trends in topic and sentiment occurrence frequency across the study period. This tool automates the analysis of large volumes of free text specific to medical services, and the web application summarises the results and presents them in an accessible and interactive format. Such a tool has the potential to considerably reduce administrative burden and increase user uptake.
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