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Spontaneously generated online patient experience data - how and why is it being used in health research : an umbrella scoping review
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Walsh, Julia, Dwumfour, Christine, Cave, Jonathan and Griffiths, Frances (2022) Spontaneously generated online patient experience data - how and why is it being used in health research : an umbrella scoping review. BMC Medical Research Methodology, 22 (1). 139. doi:10.1186/s12874-022-01610-z ISSN 1471-2288.
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WRAP-Spontaneously-generated-online-patient-experience-data-how-and-why-is-it-being-used-in-health-research-Walsh-22.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2658Kb) | Preview |
Official URL: http://dx.doi.org/10.1186/s12874-022-01610-z
Abstract
Purpose
Social media has led to fundamental changes in the way that people look for and share health related information. There is increasing interest in using this spontaneously generated patient experience data as a data source for health research. The aim was to summarise the state of the art regarding how and why SGOPE data has been used in health research. We determined the sites and platforms used as data sources, the purposes of the studies, the tools and methods being used, and any identified research gaps.
Methods
A scoping umbrella review was conducted looking at review papers from 2015 to Jan 2021 that studied the use of SGOPE data for health research. Using keyword searches we identified 1759 papers from which we included 58 relevant studies in our review.
Results
Data was used from many individual general or health specific platforms, although Twitter was the most widely used data source. The most frequent purposes were surveillance based, tracking infectious disease, adverse event identification and mental health triaging. Despite the developments in machine learning the reviews included lots of small qualitative studies. Most NLP used supervised methods for sentiment analysis and classification. Very early days, methods need development. Methods not being explained. Disciplinary differences - accuracy tweaks vs application. There is little evidence of any work that either compares the results in both methods on the same data set or brings the ideas together.
Conclusion
Tools, methods, and techniques are still at an early stage of development, but strong consensus exists that this data source will become very important to patient centred health research.
| Item Type: | Journal Article | ||||||||
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| Subjects: | H Social Sciences > HM Sociology Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > R Medicine (General) |
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| Divisions: | Faculty of Social Sciences > Economics Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School |
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| Library of Congress Subject Headings (LCSH): | Social media in medicine, Social media, Public health -- Data processing, Machine learning, Natural language processing (Computer science), Patient satisfaction | ||||||||
| Journal or Publication Title: | BMC Medical Research Methodology | ||||||||
| Publisher: | BioMed Central Ltd. | ||||||||
| ISSN: | 1471-2288 | ||||||||
| Official Date: | 14 May 2022 | ||||||||
| Dates: |
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| Volume: | 22 | ||||||||
| Number: | 1 | ||||||||
| Number of Pages: | 41 | ||||||||
| Article Number: | 139 | ||||||||
| DOI: | 10.1186/s12874-022-01610-z | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||||
| Date of first compliant deposit: | 19 July 2022 | ||||||||
| Date of first compliant Open Access: | 20 July 2022 | ||||||||
| RIOXX Funder/Project Grant: |
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