Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review
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 proces...
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doaj-4fd9099df1674d77b0a018cde58670e42021-07-31T09:30:16ZengBMJ Publishing GroupBMJ Health & Care Informatics2632-10092021-07-0128110.1136/bmjhci-2020-100262Applying natural language processing and machine learning techniques to patient experience feedback: a systematic reviewKelsey Flott0Joshua Symons1Erik Mayer2Mustafa Khanbhai3Patrick Anyadi4Patient Safety Translational Research Centre, Imperial College of Science Technology and Medicine, London, UKBig Data and Analytical Unit, Imperial College of Science Technology and Medicine, London, UKPatient Safety Translational Research Centre, Imperial College of Science Technology and Medicine, London, UKPatient Safety Translational Research Centre, Imperial College of Science Technology and Medicine, London, UKPatient Safety Translational Research Centre, Imperial College of Science Technology and Medicine, London, UKObjectives 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.https://informatics.bmj.com/content/28/1/e100262.full |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kelsey Flott Joshua Symons Erik Mayer Mustafa Khanbhai Patrick Anyadi |
spellingShingle |
Kelsey Flott Joshua Symons Erik Mayer Mustafa Khanbhai Patrick Anyadi Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review BMJ Health & Care Informatics |
author_facet |
Kelsey Flott Joshua Symons Erik Mayer Mustafa Khanbhai Patrick Anyadi |
author_sort |
Kelsey Flott |
title |
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
title_short |
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
title_full |
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
title_fullStr |
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
title_full_unstemmed |
Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
title_sort |
applying natural language processing and machine learning techniques to patient experience feedback: a systematic review |
publisher |
BMJ Publishing Group |
series |
BMJ Health & Care Informatics |
issn |
2632-1009 |
publishDate |
2021-07-01 |
description |
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. |
url |
https://informatics.bmj.com/content/28/1/e100262.full |
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