Browse through all publications from the Institute of Global Health Innovation, which our Patient Safety Research Collaboration is part of. This feed includes reports and research papers from our Centre. 

Citation

BibTex format

@article{Khanbhai:2022:10.1016/j.ijmedinf.2021.104642,
author = {Khanbhai, M and Warren, L and Symons, J and Flott, K and Harrison-White, S and Manton, D and Darzi, A and Mayer, E},
doi = {10.1016/j.ijmedinf.2021.104642},
journal = {International Journal of Medical Informatics},
pages = {1--7},
title = {Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care},
url = {http://dx.doi.org/10.1016/j.ijmedinf.2021.104642},
volume = {157},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - BackgroundPatient centred care necessitates that healthcare experiences and perceived outcomes be considered across all transitions of care. Information encoded within free-text patient experience comments relating to transitions of care are not captured in a systematic way due to the manual resource required. We demonstrate the use of natural language processing (NLP) to extract meaningful information from the Friends and Family Test (FFT).MethodsFree-text fields identifying favourable service (“What did we do well?”) and areas requiring improvement (“What could we do better?”) were extracted from 69,285 FFT reports across four care settings at a secondary care National Health Service (NHS) hospital. Sentiment and patient experience themes were coded by three independent coders to produce a training dataset. The textual data was standardised with a series of pre-processing techniques and the performance of six machine learning (ML) models was obtained. The best performing ML model was applied to predict the themes and sentiment from the remaining reports. Comments relating to transitions of care were extracted, categorised by sentiment, and care setting to identify the most frequent words/combinations presented as tri-grams and word clouds.ResultsThe support vector machine (SVM) ML model produced the highest accuracy in predicting themes and sentiment. The most frequent single words relating to transition and continuity with a negative sentiment were “discharge” in inpatients and Accident and Emergency, “appointment” in outpatients, and “home’ in maternity. Tri-grams identified from the negative sentiments such as ‘seeing different doctor’, ‘information aftercare lacking’, ‘improve discharge process’ and ‘timing discharge letter’ have highlighted some of the problems with care transitions. None of this information was available from the quantitative data.Conc
AU  - Khanbhai,M
AU  - Warren,L
AU  - Symons,J
AU  - Flott,K
AU  - Harrison-White,S
AU  - Manton,D
AU  - Darzi,A
AU  - Mayer,E
DO  - 10.1016/j.ijmedinf.2021.104642
EP  - 7
PY  - 2022///
SN  - 1386-5056
SP  - 1
TI  - Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care
T2  - International Journal of Medical Informatics
UR  - http://dx.doi.org/10.1016/j.ijmedinf.2021.104642
UR  - https://www.sciencedirect.com/science/article/pii/S1386505621002689?via%3Dihub
UR  - http://hdl.handle.net/10044/1/92882
VL  - 157
ER  -
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