SocialMediaMonitoring in pharmacovigilance you need to know

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What is Social media monitoring in Pharmacovigilance

General introduction about Social media:

Social media has been used by healthcare stakeholders, in order to distribute information about diseases and their treatment, medicines and announcements.

Currently, the potential of SNS as a source of insight is increasingly recognized. Social media mining is becoming an integral part of public health monitoring and surveillance.

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Social media presents new channels and methods that can enable pharmacovigilance to move away from traditional safety reporting methods towards more patient-centric models for reporting, analyzing and monitoring

Traditionally, pharmacovigilance has mainly relied on post-marketing spontaneous reporting systems such as (EMA), (AERS), and (FDA). However, the reporting rate of such systems is low, causing delays in the detection of ADRs.

It was noticed that, many adverse reactions are missed due to lack of interest, willingness, availability or awareness of stakeholders to report.

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With the increasing use of social media and social networks, social data are increasingly recognized as a valid source of real-time information on drug-related adverse events.

Regulators and pharmaceutical companies are also starting to monitor social media posts for potential ADR signals.

Q: Why to monitor social media in pharmacovigilance?

A: As per the regulatory authorities, social media is one of the sources for tracing of adverse drug reactions. Every pharmaceutical company need to screen the media reports also for their products.

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Q: Why that much importance for social media monitoring?

A: Social media serving as a platform that allows patients to exchange information about their health condition with others who are battling with the same health issues, and receive peer-to-peer support (online patient communities).

Q: Types of social media networks?

A: Social support is deemed extremely beneficial in combating health concerns like depression and mental illness. Such networks can be classified mainly in terms of two categories.

1. Generic SNS (Social Net-working sites)
2. Specialised healthcare social networks and forums

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Q: What are Generic SNS?

A: Generic SNS: This category can includes big public platform SNS such as Facebook, Twitter, Flicker and Tumblr, which host health-related communities/groups, and also contain big volumes of posts by individual users related to health issues.

SNS privacy policies may hinder the availability of user-generated data for data mining purposes. For example, special dispensation is required for the use of data from Facebook, which is the worlds’ largest social network.

Twitter content remains publicly available. But, the Twitter data (short sentences, fragmented sentences, use of abbreviations, misspelling errors) can significantly impact the performance of classification methods.

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Compared to the specialized healthcare social media, generic SNS contain larger volumes of data. The specialized healthcare SNS, however, contain higher proportions of relevant data.

Furthermore, the trustworthiness of social data from generic SNS is questioned. Since data quality control is lacking and data authenticity cannot be verified. This implies that, while social media mining can reveal early signs of potential ADRs, the information is not sufficient for the proper processing of the identified suspected cases and for the establishment of causality (signal verification).

Domain experts of regulatory authorities still need to employ other instruments in order to assess potential drug safety risks.

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Q: What are specialized health social networks and forums?

A: Specialised healthcare social networks and forums: This category can includes generic networking sites on general health topics and disease support, usually requiring user profiles) where users discuss their health-related experiences, including use of prescription drugs, side effects and treatments.

An important source of user-generated content on the Internet is specialized healthcare social networks and forums. These platforms allow for the collection of health-related data focused on either a specific disease or multiple disease areas. User comments in health-related social networks contain extractable information relevant to pharmacovigilance.

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Research efforts focused on both general health discussion forums and disease-specific discussion forums have demonstrated that it is possible to extract complex medical concepts, with relatively high performance, from informal, user-generated content. Recognizing the potential of health-related social networks and forums, the FDA has engaged PatientsLikeMe in a research partnership to generate more AE data. PatientsLikeMe claims to have collected more than 110,000 adverse event reports on 1000 different medications, data that the FDA will now be able to access and analyse in addition to its existing sources of information. Those forms such as

PatientsLikeMe , DailyStrength, MedHelp , WebMD, CureTogether 

Medicine-focused sharing platforms (patient forums), like Ask a Patient, Medications.com,

Disease-specific online health forums focused on specific diseases, like The TalkStroke forum, Australia’s ReachOut.com

Cohort discovery and metadata platforms have also emerged like Dementias Platform UK

In the above SNS, users tend to share their views with others facing similar problems/conditions or health outcomes, and this makes such social networks unique and robust sources of information about drugs, health effects and treatments, which can significantly augment the evidence base of research studies and provide additional insight on the needs of specific populations.

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Cure Together specifically promotes patient-driven research, by establishing research partnerships with universities, research organisations and self-experimenters.

SNS can further promote medication adherence, enhance the effectiveness of therapies and contribute to secondary prevention against recurrence of disease and chronic pain management.

In the case of mental illness, SNS can serve for the identification of signals of mental disorders and users at risk of self-harm. The rapidly growing popularity of such networks and the abundance of data available through them have recently enabled new research on public health monitoring, including ADR monitoring and formal clinical trial procedures. Researchers have considered a range of motivations for disclosure in social network sites. The fact that users publish in SNS a considerable amount of information which is not otherwise available makes social data one of the most important potential sources of knowledge in the pharmacovigilance field

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Q: How social (networking) data can be categorized according to the context and purposes of data disclosure?

A: Below are the categories of data from social media.

Service data: data that users need to give to an SNS in order to use it. This might include the persons legal name, age, credit card number, etc.

Disclosed data: data that are posted by SNS users on their own pages (e.g. blog entries, photographs, videos, messages, comments, etc.).

Entrusted data: data that are posted by SNS users on the profile pages of other SNS users (e.g. a wall post, comment). Similarly to disclosed data, entrusted data appear on the users own pages, but they do not have control over the data—someone else does.

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Incidental data: data about an SNS user which has been uploaded by another SNS user (e.g. a picture). Similarly to disclosed and entrusted data, incidental data appear on the users own pages, but they do not have control over it, and they did not create it in the first place.

Derived data: data that are inferred from (other) SNS data (e.g. membership of group X implies attribute Y).Behavioural data: data regarding the activities of SNS users within the SNS (e.g. user habits, who they interact with and how).

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Q: Types of reporting in social media?

A: Two types of reporting can be distinguished—namely solicited and unsolicited.

Type 1: solicited reporting (use of social media as a reporting channel)

Direct patient reporting of suspected ADRs has the potential to add value to pharmacovigilance.

Type 2: unsolicited reporting (social media monitoring)

By mining the relationships between drugs and ADRs from data reported by online users on health-related issues, we can speed up the process of detecting and confirming ADRs.

Social data mining, however, may have limited application to orphan drugs.

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Q: What is search logs in media?

A: Search logs: Search for health information on the web is growing, with an increasing number of people considering the Internet as an important source of knowledge. Tapping on back-office social data, several scholars have demonstrated how search logs can be used to detect new ADRs.

Good approaches for ADR detection from social media from the MEDLINE, Embase, Scopus and Web of Science databases and the Google Scholar search engine.

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