{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:38:44Z","timestamp":1761176324882,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>The National Health Service (NHS) uses the Friends and Family Test (FFT) to gather patient feedback aimed at improving service quality and satisfaction. The current workflow involves FFT feedback processed manually by the Patient Experience team to identify sentiment and theme, which is a time-consuming and unsustainable approach given the large volume of data. To address this, we proposed and implemented a two-phase solution that is adaptable with limited computational resources that has no access to GPUs: (1) training and evaluation of lightweight, fine-tuned transformer-based models using routinely collected, expert-reviewed feedback data, and (2) deployment of these models within an automated pipeline for sentiment prediction and theme classification, involving a human-in-the-loop verification process. Our experiments and results validated on expert-annotated data collected over a period of 3.5 years achieve the best performance with an F1-score of 88.14% in sentiment classification and 71.47% in theme classification. This paper details the implementation, testing, and monitoring of our proposed solution in a paediatric healthcare setting, highlighting the ability to generate near-real-time results. Our deployed solution has substantially decreased the time needed to manually process approximately 2000 feedback pieces each month by delivering automated outcomes daily, a task that previously required several days for completion. In addition, we discuss the broader implications of our findings and potential enhancements for scalable deployment across NHS trusts. Source code will be open-sourced and published here: https:\/\/github.com\/gosh-dre\/FFT-NLP-Pipeline.<\/jats:p>","DOI":"10.3233\/faia251485","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:04:10Z","timestamp":1761127450000},"source":"Crossref","is-referenced-by-count":0,"title":["Context Beyond Simple Rules: Deploying an Automated NLP-Based Pipeline for Sentiment and Theme Prediction in Paediatric Healthcare Setting"],"prefix":"10.3233","author":[{"given":"Caroline","family":"Baumgartner","sequence":"first","affiliation":[{"name":"DRIVE, Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Ewart Jonny","family":"Sheldon","sequence":"additional","affiliation":[{"name":"DRIVE, Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Sebin","family":"Sabu","sequence":"additional","affiliation":[{"name":"DRIVE, Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Jaskaran Singh","family":"Kawatra","sequence":"additional","affiliation":[{"name":"DRIVE, Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Pavithra","family":"Rajendran","sequence":"additional","affiliation":[{"name":"DRIVE, Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Shiren","family":"Patel","sequence":"additional","affiliation":[{"name":"DRIVE, Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Mark","family":"Harris","sequence":"additional","affiliation":[{"name":"Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Suzanne","family":"Collin","sequence":"additional","affiliation":[{"name":"Great Ormond Street Hospital NHS Foundation Trust"}]},{"given":"Taraben","family":"Kapadia","sequence":"additional","affiliation":[{"name":"Great Ormond Street Hospital NHS Foundation Trust"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251485","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:04:10Z","timestamp":1761127450000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251485"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251485","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}