RT Journal Article
SR Electronic
T1 Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review
JF BMJ Health & Care Informatics
JO BMJ Health Care Inform
FD BMJ Publishing Group Ltd
SP e100262
DO 10.1136/bmjhci-2020-100262
VO 28
IS 1
A1 Mustafa Khanbhai
A1 Patrick Anyadi
A1 Joshua Symons
A1 Kelsey Flott
A1 Ara Darzi
A1 Erik Mayer
YR 2021
UL http://informatics.bmj.com/content/28/1/e100262.abstract
AB 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.