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

Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care
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 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.
text-mining, PROMs, quality of life, colorectal cancer, machine learning, machine learning algorithms, thematic analysis, thematic content analysis, qualitative methods
2044-5415
604-614
Wagland, Richard
16a44dcc-29cd-4797-9af2-41ef87f64d08
Recio Saucedo, Alejandra
d05c4e43-3399-466d-99e0-01403a04b467
Simon, Michael
40c7fa62-277a-469d-993e-6be6c6714896
Bracher, Michael
e9e2fbd6-af5f-4f6e-8357-969aaf51c52e
Hunt, Katherine
5eab8123-1157-4d4e-a7d9-5fd817218c6e
Foster, Claire
00786ac1-bd47-4aeb-a0e2-40e058695b73
Downing, Amy
f4ff289d-68c6-466b-bbc4-f920e10506b5
Glaser, Adam
47f40b4c-2ff7-4c0e-a137-67564d0c29bc
Corner, Jessica
eddc9d69-aa12-4de5-8ab0-b20a6b5765fa
Wagland, Richard
16a44dcc-29cd-4797-9af2-41ef87f64d08
Recio Saucedo, Alejandra
d05c4e43-3399-466d-99e0-01403a04b467
Simon, Michael
40c7fa62-277a-469d-993e-6be6c6714896
Bracher, Michael
e9e2fbd6-af5f-4f6e-8357-969aaf51c52e
Hunt, Katherine
5eab8123-1157-4d4e-a7d9-5fd817218c6e
Foster, Claire
00786ac1-bd47-4aeb-a0e2-40e058695b73
Downing, Amy
f4ff289d-68c6-466b-bbc4-f920e10506b5
Glaser, Adam
47f40b4c-2ff7-4c0e-a137-67564d0c29bc
Corner, Jessica
eddc9d69-aa12-4de5-8ab0-b20a6b5765fa

Wagland, Richard, Recio Saucedo, Alejandra, Simon, Michael, Bracher, Michael, Hunt, Katherine, Foster, Claire, Downing, Amy, Glaser, Adam and Corner, Jessica (2016) Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care. BMJ Quality and Safety, 25 (8), 604-614. (doi:10.1136/bmjqs-2015-004063). (PMID:26512131)

Record type: Article

Abstract

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.

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Accepted/In Press date: 26 September 2015
e-pub ahead of print date: 28 October 2015
Published date: 19 July 2016
Keywords: text-mining, PROMs, quality of life, colorectal cancer, machine learning, machine learning algorithms, thematic analysis, thematic content analysis, qualitative methods
Organisations: Faculty of Health Sciences

Identifiers

Local EPrints ID: 382011
URI: http://eprints.soton.ac.uk/id/eprint/382011
ISSN: 2044-5415
PURE UUID: 2e17a9ae-5a0f-40dc-9b94-53bc9e57af32
ORCID for Richard Wagland: ORCID iD orcid.org/0000-0003-1825-7587
ORCID for Alejandra Recio Saucedo: ORCID iD orcid.org/0000-0003-2823-4573
ORCID for Michael Bracher: ORCID iD orcid.org/0000-0001-5861-2657
ORCID for Katherine Hunt: ORCID iD orcid.org/0000-0002-6173-7319
ORCID for Claire Foster: ORCID iD orcid.org/0000-0002-4703-8378

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Date deposited: 20 Oct 2015 15:49
Last modified: 11 Mar 2022 02:44

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Contributors

Author: Richard Wagland ORCID iD
Author: Michael Simon
Author: Michael Bracher ORCID iD
Author: Katherine Hunt ORCID iD
Author: Claire Foster ORCID iD
Author: Amy Downing
Author: Adam Glaser
Author: Jessica Corner

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