Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer care Article Swipe
Richard Wagland
,
Alejandra Recio‐Saucedo
,
Michael Simon
,
Michael Bracher
,
Katherine Hunt
,
Claire Foster
,
Amy Downing
,
Adam Glaser
,
Jessica Corner
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1136/bmjqs-2015-004063
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.1136/bmjqs-2015-004063
Background Quality of cancer care may greatly impact on 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 on 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 on 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 on HRQoL
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1136/bmjqs-2015-004063
- OA Status
- green
- Cited By
- 60
- References
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- Related Works
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- OpenAlex ID
- https://openalex.org/W2180737626
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2180737626Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1136/bmjqs-2015-004063Digital Object Identifier
- Title
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Development and testing of a text-mining approach to analyse patients’ comments on their experiences of colorectal cancer careWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-10-28Full publication date if available
- Authors
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Richard Wagland, Alejandra Recio‐Saucedo, Michael Simon, Michael Bracher, Katherine Hunt, Claire Foster, Amy Downing, Adam Glaser, Jessica CornerList of authors in order
- Landing page
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https://doi.org/10.1136/bmjqs-2015-004063Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://researchportal.port.ac.uk/portal/en/publications/development-and-testing-of-a-textmining-approach-to-analyse-patients-comments-on-their-experiences-of-colorectal-cancer-care(72ca4641-87e4-4b17-bd67-72683d84a247).htmlDirect OA link when available
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Medicine, Colorectal cancer, Family medicine, Cancer, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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60Total citation count in OpenAlex
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2025: 8, 2024: 9, 2023: 6, 2022: 3, 2021: 13Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.verified | 112 |
| abstract_inverted_index.(n=1688), | 154 |
| abstract_inverted_index.Free-text | 15 |
| abstract_inverted_index.analysis. | 116, 251 |
| abstract_inverted_index.comments) | 176 |
| abstract_inverted_index.comments. | 146, 199 |
| abstract_inverted_index.concerned | 166 |
| abstract_inverted_index.described | 159 |
| abstract_inverted_index.developed | 33 |
| abstract_inverted_index.explored. | 128 |
| abstract_inverted_index.framework | 82 |
| abstract_inverted_index.free-text | 68, 145, 242 |
| abstract_inverted_index.indicated | 201, 268 |
| abstract_inverted_index.mechanism | 204 |
| abstract_inverted_index.perceived | 270 |
| abstract_inverted_index.practical | 237 |
| abstract_inverted_index.practice. | 265 |
| abstract_inverted_index.prevented | 218 |
| abstract_inverted_index.regarding | 70 |
| abstract_inverted_index.responses | 16 |
| abstract_inverted_index.retrieved | 120, 148, 175 |
| abstract_inverted_index.survivors | 66 |
| abstract_inverted_index.treatment | 186, 226 |
| abstract_inverted_index.Background | 0 |
| abstract_inverted_index.algorithms | 96 |
| abstract_inverted_index.alleviated | 220 |
| abstract_inverted_index.associated | 222 |
| abstract_inverted_index.challenges | 221 |
| abstract_inverted_index.colorectal | 64 |
| abstract_inverted_index.concerning | 180 |
| abstract_inverted_index.considered | 256 |
| abstract_inverted_index.experience | 72 |
| abstract_inverted_index.facilitate | 41 |
| abstract_inverted_index.strategies | 182 |
| abstract_inverted_index.techniques | 232 |
| abstract_inverted_index.Comparisons | 117 |
| abstract_inverted_index.Conclusions | 228 |
| abstract_inverted_index.Respondents | 58 |
| abstract_inverted_index.experiences | 45, 106, 151, 165 |
| abstract_inverted_index.explanatory | 51 |
| abstract_inverted_index.information | 179 |
| abstract_inverted_index.patients’ | 9, 44, 104 |
| abstract_inverted_index.qualitative | 115, 250 |
| abstract_inverted_index.text-mining | 38 |
| abstract_inverted_index.Associations | 191 |
| abstract_inverted_index.experiences. | 162 |
| abstract_inverted_index.facilitating | 248 |
| abstract_inverted_index.illustrating | 53 |
| abstract_inverted_index.insufficient | 178 |
| abstract_inverted_index.participants | 143 |
| abstract_inverted_index.Learning-based | 229 |
| abstract_inverted_index.health-related | 10 |
| abstract_inverted_index.learning-based | 37, 89 |
| abstract_inverted_index.post-treatment | 170 |
| abstract_inverted_index.self-management | 181 |
| abstract_inverted_index.Machine-learning | 95 |
| abstract_inverted_index.patient-reported | 18 |
| abstract_inverted_index.population-based | 61 |
| abstract_inverted_index.resource-efficient | 249 |
| abstract_inverted_index.algorithm-retrieved | 198 |
| abstract_inverted_index.resource-intensive. | 30 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 94 |
| corresponding_author_ids | https://openalex.org/A5076939789 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I43439940 |
| citation_normalized_percentile.value | 0.87082488 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |