Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care

Background: Quality of cancer care may greatly impact upon patients’ health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining ap...

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Main Authors:	Wagland, Richard, Recio Saucedo, Alejandra, Simon, Michael, Bracher, Michael, Hunt, Katherine, Foster, Claire, Downing, Amy, Glaser, Adam W., Corner, Jessica
Format:	Article
Language:	English
Published:	BMJ Publishing Group 2015
Subjects:	RC 254 Neoplasms. Tumors. Oncology (including Cancer) RT Nursing
Online Access:	http://eprints.nottingham.ac.uk/32437/ http://eprints.nottingham.ac.uk/32437/ http://eprints.nottingham.ac.uk/32437/ http://eprints.nottingham.ac.uk/32437/3/REVISED_ColorectalPROMsQualitativefree-textanalysispaper_Accepted.pdf

id	nottingham-32437
recordtype	eprints
spelling	nottingham-324372018-07-02T09:04:29Z http://eprints.nottingham.ac.uk/32437/ Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care Wagland, Richard Recio Saucedo, Alejandra Simon, Michael Bracher, Michael Hunt, Katherine Foster, Claire Downing, Amy Glaser, Adam W. Corner, Jessica RC 254 Neoplasms. Tumors. Oncology (including Cancer) RT Nursing Background: Quality of cancer care may greatly impact upon patients’ health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients’ experiences of care and develop an explanatory model illustrating impact upon HRQoL. Methods: Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients’ specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. Results: The survey response rate was 63.3% (21,802/34,467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments), and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted upon HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Conclusions: Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that perceived care quality directly impacts upon HRQoL. BMJ Publishing Group 2015-10-28 Article PeerReviewed application/pdf en http://eprints.nottingham.ac.uk/32437/3/REVISED_ColorectalPROMsQualitativefree-textanalysispaper_Accepted.pdf Wagland, Richard and Recio Saucedo, Alejandra and Simon, Michael and Bracher, Michael and Hunt, Katherine and Foster, Claire and Downing, Amy and Glaser, Adam W. and Corner, Jessica (2015) Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care. BMJ Quality & Safety . pp. 1-26. ISSN 2044-5423 http://qualitysafety.bmj.com/content/early/2015/10/28/bmjqs-2015-004063 doi:10.1136/bmjqs-2015-004063 doi:10.1136/bmjqs-2015-004063
repository_type	Digital Repository
institution_category	Local University
institution	University of Nottingham Malaysia Campus
building	Nottingham Research Data Repository
collection	Online Access
language	English
topic	RC 254 Neoplasms. Tumors. Oncology (including Cancer) RT Nursing
spellingShingle	RC 254 Neoplasms. Tumors. Oncology (including Cancer) RT Nursing Wagland, Richard Recio Saucedo, Alejandra Simon, Michael Bracher, Michael Hunt, Katherine Foster, Claire Downing, Amy Glaser, Adam W. Corner, Jessica Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
description	Background: Quality of cancer care may greatly impact upon patients’ health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patients’ experiences of care and develop an explanatory model illustrating impact upon HRQoL. Methods: Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patients’ specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored. Results: The survey response rate was 63.3% (21,802/34,467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments), and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted upon HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens. Conclusions: Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that perceived care quality directly impacts upon HRQoL.
format	Article
author	Wagland, Richard Recio Saucedo, Alejandra Simon, Michael Bracher, Michael Hunt, Katherine Foster, Claire Downing, Amy Glaser, Adam W. Corner, Jessica
author_facet	Wagland, Richard Recio Saucedo, Alejandra Simon, Michael Bracher, Michael Hunt, Katherine Foster, Claire Downing, Amy Glaser, Adam W. Corner, Jessica
author_sort	Wagland, Richard
title	Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
title_short	Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
title_full	Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
title_fullStr	Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
title_full_unstemmed	Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
title_sort	development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
publisher	BMJ Publishing Group
publishDate	2015
url	http://eprints.nottingham.ac.uk/32437/ http://eprints.nottingham.ac.uk/32437/ http://eprints.nottingham.ac.uk/32437/ http://eprints.nottingham.ac.uk/32437/3/REVISED_ColorectalPROMsQualitativefree-textanalysispaper_Accepted.pdf
first_indexed	2018-09-06T12:14:56Z
last_indexed	2018-09-06T12:14:56Z
_version_	1610860301020823552

Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care

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