Evidence map›Paper›PMID 35562661›Full record

ArticleBMC medical research methodology2022

Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review.

Julia Walsh, Christine Dwumfour, Jonathan Cave, Frances Griffiths

Abstract readScoping Review
In one paragraph

Article in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Julia WalshWarwick Medical School, University of Warwick, Coventry, UK. j.walsh.5@warwick.ac.uk.ORCID 0000-0002-9787-0349
Christine DwumfourWarwick Medical School, University of Warwick, Coventry, UK.
Jonathan CaveDepartment of Economics, University of Warwick, Coventry, UK.
Frances GriffithsWarwick Medical School, University of Warwick, Coventry, UK.ORCID 0000-0002-4173-1438

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSocial 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.

methodsA 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.

resultsData 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.

conclusionTools, 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.

Indexed as

Social MediaData CollectionHumansInformation Storage and RetrievalMachine LearningPatient Outcome AssessmentHealth researchMachine learningMethodsNatural language processingSocial mediaText analysisUmbrella review

Identifiers

PMID35562661
PMCPMC9106384

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.