Note: Chapter 8.5. (formerly 7.5.) has not been updated for Revisions 10 and 11 of the Guide.
8.5.2. Use in pharmacovigilance
8.5.4. Data protection
Technological advances have dramatically increased the range of data sources that can be used to complement traditional ones and may provide compelling insights into or relevant to effectiveness and safety of health interventions such as medicines and their risk minimisation measures, benefit-risk communications and related stakeholder engagement. Such data include those from digital media that exist in a computer-readable format and can be extracted from websites, web pages, blogs, vlogs, social networking sites, internet forums, chat rooms and health portals. A recent addition to the digital media data is biomedical data collected through wearable technology (e.g., heart rate, physical activity and sleep pattern, dietary patterns). These data are unsolicited and generated in real time.
A subset of digital media data are social media data. The European Commission’s Digital Single Market Glossary defines social media as “of Web 2.0 and that allow the creation and exchange of user-generated content. It employs mobile and web-based technologies to create highly interactive platforms via which individuals and communities share, co-create, discuss, and modify user-generated content.”
Social media content analyses have been used to provide insights into patients’ perceptions of the effectiveness and safety of medicines and for the collection of patient reported outcomes, as discussed in Web-based patient-reported outcomes in drug safety and risk management: challenges and opportunities? (Drug Saf. 2012;35(6):437-46).
The IMI WEB-RADR European collaborative project explored different aspects related to the use of social media data for pharmacovigilance and summarised its recommendations in Recommendations for the Use of Social Media in Pharmacovigilance: Lessons From IMI WEB-RADR (Drug Saf 2019;42(12):1393-407). The French Vigi4Med project, which evaluated the use of social media, mainly web forums, for pharmacovigilance activities, published a set of recommendations in Use of Social Media for Pharmacovigilance Activities: Key Findings and Recommendations from the Vigi4Med Project (Drug Saf. 2020;43(9):835-51).
A further possible use of social media data would be as a source of information for signal detection or assessment. Studies including Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives (Pharm Med. 2017;31(3): 167-74) and Assessment of the Utility of Social Media for Broad-Ranging Statistical Signal Detection in Pharmacovigilance: Results from the WEB-RADR Project (Drug Saf. 2018;41(12):1355–69) evaluated whether analysis of social media data (specifically Facebook and Twitter posts) could identify pharmacovigilance signals early, but in their respective settings, found that this was not the case.
The study Using Social Media Data in Routine Pharmacovigilance: A Pilot Study to Identify Safety Signals and Patient Perspectives (Pharm Med. 2017;31(3): 167-74) also tried to determine the quantity of posts with resemblance to adverse events and the types and characteristics of products that would benefit from social media content analysis. It concludes that, although analysis of data from social media did not identify new safety signals, it can provide unique insight into the patient perspective.
From a regulatory perspective, social media is a source of potential reports of suspected adverse reactions and marketing authorisation holders are legally obliged to screen websites under their management and assess whether reports of adverse reactions qualify for spontaneous reporting (see Good Pharmacovigilance Practice Module VI, section VI.B.1.1.4.). Principles for continuous monitoring of the safety of medicines without overburdening established pharmacovigilance systems and a regulatory framework on the use of social media in pharmacovigilance have been proposed in Establishing a Framework for the Use of Social Media in Pharmacovigilance in Europe (Drug Saf. 2019;42(8):921-30).
Sentiment analyses of social media content may offer future opportunities for regulators into public perceptions about the safety of medicines and trustworthiness of regulatory bodies. This can inform and evaluate specific safety communication strategies aiming at effective and safe use of medicines. For example, a recent study provided insight into public sentiments about vaccination of pregnant women by stance, discourse and topic analysis of social media posts in ‘‘Vaccines for pregnant women?! Absurd” – Mapping maternal vaccination discourse and stance on social media over six months (Vaccine 2020;38(42): 6627-38).
While offering the promise of new research models and approaches, the rapidly evolving social media environment presents many challenges including the need for strong and systematic processes for data selection and validation, and study implementation. Articles which detail associated challenges are: Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies (Drug Saf. 2015;38(10): 921-30.) and Social media and pharmacovigilance: A review of the opportunities and challenges (Br J Clin Pharmacol. 2015;80(4): 910-20).
There is currently no defined strategy or framework in place in order to meet the standards around data selection and validity and methods for data analysis, and their regulatory acceptance may therefore be lower than for traditional sources. However, more tools and methods for analysing unstructured data are becoming available, especially for pharmacoepidemiology and pharmacovigilance research, as in Deep learning for pharmacovigilance: recurrent neural network architectures for labeling adverse drug reactions in Twitter posts (J Am Med Inform Assoc. 2017 Feb 22), Social Media Listening for Routine Post-Marketing Safety Surveillance (Drug Saf. 2016;39(5):443-54) and Social Media Research (Chapter 11 in Communicating about Risks and Safe Use of Medicines, Adis Singapore, 2020, pp 307-332). However, the recognition and disambiguation of references to medicines and adverse events in free text remains a challenge and performance evaluations need to be critically assessed as discussed in Prospective Evaluation of Adverse Event Recognition Systems in Twitter: Results from the Web-RADR Project (Drug Saf. 2020;43(8):797-808).
The EU General Data Protection Regulation (GDPR) introduces EU-wide legislation on personal data and security. It specifies that the impact of data protection at the time of study design concept should be assessed and reviewed periodically. Other technical documents may also be applicable such as Smartphone Secure Development Guidelines (2017) published by the European Network and Information Security Agency (ENISA), which advises on design and technical solutions. The principles of these security measures are found in the European Data Protection Supervisor (EDPS) opinion on mobile health (Opinion 1/2015 Mobile Health-Reconciling technological innovation with data protection.