User feedback on the NHS test & Trace Service during COVID-19: The use of machine learning to analyse free-text data from 37,914 England adults

Paulina Bondaronek · Trisevgeni Papakonstantinou · Chryssa Stefanidou · Tim Chadborn

8 citations0/0 files storedPublic Health in Practice

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Computational and Text Analysis Methods Sentiment Analysis and Opinion Mining Data-Driven Disease Surveillance

Abstract

Objectives: The UK government's approach to the pandemic relies on a test, trace and isolate strategy, mainly implemented via the digital NHS Test & Trace Service. Feedback on user experience is central to the successful development of public-facing Services. As the situation dynamically changes and data accumulate, interpretation of feedback by humans becomes time-consuming and unreliable. The specific objectives were to 1) evaluate a human-in-the-loop machine learning technique based on structural topic modelling in terms of its Service ability in the analysis of vast volumes of free-text data, 2) generate actionable themes that can be used to increase user satisfaction of the Service. Methods: We evaluated an unsupervised Topic Modelling approach, testing models with 5-40 topics and differing covariates. Two human coders conducted thematic analysis to interpret the topics. We identified a Structural Topic Model with 25 topics and metadata as covariates as the most appropriate for acquiring insights. Results: Results from analysis of feedback by 37,914 users from May 2020 to March 2021 highlighted issues with the Service falling within three major themes: multiple contacts and incompatible contact method and incompatible contact method, confusion around isolation dates and tracing delays, complex and rigid system. Conclusions: Structural Topic Modelling coupled with thematic analysis was found to be an effective technique to rapidly acquire user insights. Topic modelling can be a quick and cost-effective method to provide high quality, actionable insights from free-text feedback to optimize public health Services.

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2006Using thematic analysis in psychology 2014Structural Topic Models for Open‐Ended Survey Responses 2019stm : An R Package for Structural Topic Models 2018quanteda: An R package for the quantitative analysis of textual data 2014stm: R Package for Structural Topic Models 2022Towards digital health equity - a qualitative study of the challenges experienced by vulnerable groups in using digital health services in the COVID-19 era 2018Augmenting Qualitative Text Analysis with Natural Language Processing: Methodological Study 2020Online Information Exchange and Anxiety Spread in the Early Stage of the Novel Coronavirus (COVID-19) Outbreak in South Korea: Structural Topic Model and Network Analysis

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2025Using machine‐assisted topic analysis to expedite thematic analysis of free‐text data: Exemplar investigation of factors influencing health behaviours and wellbeing during the COVID ‐19 pandemic 2026Machine-Assisted Topic Analysis of Large-Scale Health Experience Data: Identifying Sociodemographic Differences and Evaluating Bias in Large Language Models

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