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Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care

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Title: Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care
Authors: Khanbhai, M
Warren, L
Symons, J
Flott, K
Harrison-White, S
Manton, D
Darzi, A
Mayer, E
Item Type: Journal Article
Abstract: Background Patient 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). Methods Free-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. Results The 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. Conclusions NLP can be used to identify themes and sentiment from patient experience survey comments relating to transitions of care in all four healthcare settings. With the help of a quality improvement framework, findings from our analysis may be used to guide patient-centred interventions to improve transitional care processes.
Issue Date: Jan-2022
Date of Acceptance: 7-Nov-2021
URI: http://hdl.handle.net/10044/1/92882
DOI: 10.1016/j.ijmedinf.2021.104642
ISSN: 1386-5056
Publisher: Elsevier
Start Page: 1
End Page: 7
Journal / Book Title: International Journal of Medical Informatics
Volume: 157
Copyright Statement: © 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
The Health Foundation
National Institute for Health Research
Imperial College Healthcare NHS Trust
NHS England
Imperial College Healthcare NHS Trust- BRC Funding
The Health Foundation
Funder's Grant Number: RDB04
n/a
RDB18 79650
RDF03
FR775
WSSY_P85168
RDF01
452981
Keywords: Natural language processing
Patient experience
Real-time feedback
Sentiment analysis
Transitions of care
08 Information and Computing Sciences
09 Engineering
11 Medical and Health Sciences
Medical Informatics
Publication Status: Published
Online Publication Date: 2021-11-11
Appears in Collections:Department of Surgery and Cancer
Institute of Global Health Innovation
School of Public Health



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