Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review

Andrea C Tricco,Paul A Khan,Ba’ Pham,Sharon E Straus,Serena Jeblee,Graeme Hirst,Rachel Warren,Erin Lillie,Reid Robson,Wasifa Zarin

doi:10.1186/s12911-018-0621-y

Andrea C Tricco, Paul A Khan + Show 8 more

Open Access

https://doi.org/10.1186/s12911-018-0621-y

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Abstract

BackgroundA scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products.MethodsOur specific research questions were (1) What social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics? (2) What is the validity and reliability of data from social media for detecting these adverse events? MEDLINE, EMBASE, Cochrane Library, and relevant websites were searched from inception to May 2016. Any type of document (e.g., manuscripts, reports) that described the use of social media data for detecting health product AEs was included. Two reviewers independently screened citations and full-texts, and one reviewer and one verifier performed data abstraction. Descriptive synthesis was conducted.ResultsAfter screening 3631 citations and 321 full-texts, 70 unique documents with 7 companion reports available from 2001 to 2016 were included. Forty-six documents (66%) described an automated or semi-automated information extraction system to detect health product AEs from social media conversations (in the developmental phase). Seven pre-existing information extraction systems to mine social media data were identified in eight documents. Nineteen documents compared AEs reported in social media data with validated data and found consistent AE discovery in all except two documents. None of the documents reported the validity and reliability of the overall system, but some reported on the performance of individual steps in processing the data. The validity and reliability results were found for the following steps in the data processing pipeline: data de-identification (n = 1), concept identification (n = 3), concept normalization (n = 2), and relation extraction (n = 8). The methods varied widely, and some approaches yielded better results than others.ConclusionsOur results suggest that the use of social media conversations for pharmacovigilance is in its infancy. Although social media data has the potential to supplement data from regulatory agency databases; is able to capture less frequently reported AEs; and can identify AEs earlier than official alerts or regulatory changes, the utility and validity of the data source remains under-studied.Trial registrationOpen Science Framework (https://osf.io/kv9hu/).

Highlights

A scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products
Research questions The specific research questions were: (1) Which social media listening platforms exist to detect adverse events related to health products, and what are their capabilities and characteristics?
Eligibility criteria The eligibility criteria were any type of document that described listening to social media data for detecting adverse events associated with health products

Summary

Introduction

A scoping review to characterize the literature on the use of conversations in social media as a potential source of data for detecting adverse events (AEs) related to health products. Thousands of people die from an adverse drug reaction, defined as an undesirable health effect that occurs when medication is used as prescribed [1]. Adverse drug reactions can vary from a simple rash to more severe effects, such as heart failure, acute liver injury, arrhythmias, and even death [1]. These events have a significant impact on both patients and the health care system in terms of cost and health service utilization (e.g., frequent visits to physicians and emergency departments, hospitalizations) [2]. It is estimated that spontaneous reporting systems only capture 1–10% of all adverse drug reactions. One out of every five physicians reports an adverse drug reaction using the Canada Vigilance Database [3]

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