Imperial College London
Alternate

Professor the Lord Darzi of Denham PC KBE FRS FMedSci HonFREng

Faculty of Medicine, Department of Surgery & Cancer

Co-Director of the IGHI, Professor of Surgery
 
 
 
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Contact

 

+44 (0)20 3312 1310a.darzi

 
 
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Location

 

Queen Elizabeth the Queen Mother Wing (QEQM)St Mary's Campus

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Summary

 

Publications

Citation

BibTex format

@article{Khanbhai:2021:10.1136/bmjhci-2020-100262,
author = {Khanbhai, M and Anyadi, P and Symons, J and Flott, K and Darzi, A and Mayer, E},
doi = {10.1136/bmjhci-2020-100262},
journal = {BMJ Health & Care Informatics},
title = {Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review},
url = {http://dx.doi.org/10.1136/bmjhci-2020-100262},
volume = {28},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
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.
AU  - Khanbhai,M
AU  - Anyadi,P
AU  - Symons,J
AU  - Flott,K
AU  - Darzi,A
AU  - Mayer,E
DO  - 10.1136/bmjhci-2020-100262
PY  - 2021///
SN  - 2632-1009
TI  - Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review
T2  - BMJ Health & Care Informatics
UR  - http://dx.doi.org/10.1136/bmjhci-2020-100262
UR  - http://hdl.handle.net/10044/1/86838
VL  - 28
ER  -
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