New drugs are thoroughly studied in clinical trials before they get market access but the limited, selected study population will not express all possible adverse drug reactions (ADR) or side effects of the new drug. Rare ADR will pass the radar and might potentially cause harm to the patient.
Patients don't always inform their treating physicians with the minor inconveniences they experience when taking medication. But like it or not, the digital doctor is hot, and patients try to find answers on different forums and chats. They trust everything to social media, from the food they order in the restaurant to the itch they have since using their meds.
When we have computers scraping the internet for certain combinations of product names and mesh terms for side effects they come up with a lot of data that can possibly identify rare ADR's. More and more pharmaceutical companies are looking into social listening as a supplemental data source for insight into post-marketing benefit-risk from the patient's perspective as well.
But as the internet is a source of fake news and algorithms and posts are not always validated great caution must be required when interpreting and concluding on these data.
In this session, we will explore the possible use of social listening data for outcomes research primarily within safety and benefit-risk, presenting viewpoints from industry, social media site administrators, data vendors, and regulatory bodies.
Learning objectives After the seminar, participants should be able to: • describe how social media can be used to support pharmacovigilance; • critically appraise the quality and value of the used methodology and the results; • discuss this relatively new healthcare technology based on a case.
Educational need addressed
Hospital pharmacists have to understand the power of "social listening" from social media in finding post-marketing benefit-risk data. They have to be aware that a critical appraisal of data scraped from the web is essential.
Keywords: pharmacovigilance, social media, big data, datamining, patient safety, critical appraisal.