Revealing patient-reported experiences in healthcare from social media using the design-acquire-process-model-analyse-visualise framework
Murray, Curtis, Mitchell, Lewis, Tuke, Jonathan, and Mackay, Mark (2024) Revealing patient-reported experiences in healthcare from social media using the design-acquire-process-model-analyse-visualise framework. Digital Health, 10.
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Abstract
Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centred care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients’ journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this article, we introduce the design-acquire-process-model-analyse-visualise framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework’s value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses.
| Item ID: | 87390 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 2055-2076 |
| Keywords: | narrative analysis, natural language processing, Patient experience, prostate cancer, sentiment analysis, social media, topic modelling |
| Copyright Information: | © The Author(s) 2024. Creative Commons CC BY: This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
| Date Deposited: | 02 Dec 2025 01:00 |
| FoR Codes: | 42 HEALTH SCIENCES > 4203 Health services and systems > 420308 Health informatics and information systems @ 100% |
| SEO Codes: | 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280112 Expanding knowledge in the health sciences @ 100% |
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