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Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review

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Title: Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review
Authors: Khanbhai, M
Anyadi, P
Symons, J
Flott, K
Darzi, A
Mayer, E
Item Type: Journal Article
Abstract: Objectives Unstructured free-text patient feedback contains rich information, and analysing these data manually would require a lot of personnel resources which are not available in most healthcare organisations.To undertake a systematic review of the literature on the use of natural language processing (NLP) and machine learning (ML) to process and analyse free-text patient experience data. Methods Databases were systematically searched to identify articles published between January 2000 and December 2019 examining NLP to analyse free-text patient feedback. Due to the heterogeneous nature of the studies, a narrative synthesis was deemed most appropriate. Data related to the study purpose, corpus, methodology, performance metrics and indicators of quality were recorded. Results Nineteen articles were included. The majority (80%) of studies applied language analysis techniques on patient feedback from social media sites (unsolicited) followed by structured surveys (solicited). Supervised learning was frequently used (n=9), followed by unsupervised (n=6) and semisupervised (n=3). Comments extracted from social media were analysed using an unsupervised approach, and free-text comments held within structured surveys were analysed using a supervised approach. Reported performance metrics included the precision, recall and F-measure, with support vector machine and Naïve Bayes being the best performing ML classifiers. Conclusion NLP and ML have emerged as an important tool for processing unstructured free text. Both supervised and unsupervised approaches have their role depending on the data source. With the advancement of data analysis tools, these techniques may be useful to healthcare organisations to generate insight from the volumes of unstructured free-text data.
Issue Date: 2-Mar-2021
Date of Acceptance: 12-Jan-2021
URI: http://hdl.handle.net/10044/1/86838
DOI: 10.1136/bmjhci-2020-100262
ISSN: 2632-1009
Publisher: BMJ Publishing Group
Journal / Book Title: BMJ Health & Care Informatics
Volume: 28
Issue: 1
Copyright Statement: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
The Health Foundation
Imperial College Healthcare NHS Trust
NHS England
Imperial College Healthcare NHS Trust- BRC Funding
Imperial College Healthcare NHS Trust- BRC Funding
The Health Foundation
Imperial College Healthcare NHS Trust- BRC Funding
Funder's Grant Number: RDB04
RDB18 79650
Keywords: BMJ health informatics
computer methodologies
information management
patient care
Publication Status: Published
Article Number: ARTN e100262
Online Publication Date: 2021-03-02
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation

This item is licensed under a Creative Commons License Creative Commons