JMIR Publications

ABSTRACT

Background:

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.

Objective:

The aim of this study was to use natural language processing (NLP) and machine learning techniques along with an interactive web application to provide unique insights into NHS services from patient experience feedback.

Methods:

This study details the development of a text analysis tool that utilises contemporary NLP and machine learning models to analyse free-text clinical service reviews to develop a robust classification model, and interactive visualisation web application using multiple technologies such as; Vue.js, C# serverless API, and SQL server all hosted on the Microsoft Azure Platform, which facilitates exploration of the data, designed for the use by all stakeholders.

Results:

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. 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.

Conclusions:

This study generated insights into the patient experience of NHS services by using modern NLP, machine learning and visualisation techniques. This study can help future efforts to find and visualise useful, actionable and unique information from free-text patient reviews.