How Health-Related Social Media Can Complement Traditional Real-World Evidence Approaches to Offer Unique Patient Insights

FALL 2020, THE EVIDENCE FORUM, WHITE PAPER

Alison Booth, MSc Research Associate Real-World Evidence Evidera, a PPD business

Traditional real-world evidence (RWE) is based on data collected in normal clinical practice outside of randomized controlled trials. It is used to complement clinical data in regulatory and health technology assessment submissions. This data is most often generated through retrospective or prospective observational studies using electronic health records, medical claims, disease registries, etc. As more emphasis is placed on the importance of RWE, other sources of useful real-world data have been identified, specifically social media. Online technology platforms have allowed patients to interact with each other and provide unique insight into specific diseases, conditions, and treatments. Mining this health-related social media data provides invaluable real-world data.

Social Media Listening as a Source of Real-World Data

Health-related social media is constantly growing and gaining momentum as a complementary source of real-world data for both payers and regulators. For example, in June 2018, the United States Food and Drug Administration (FDA) encouraged the use of social media to shed light on the patient’s perspective, illustrating the increasing importance being placed on incorporation of the patient experience.¹ In addition, the FDA has its own programs for and perspectives on collecting information about adverse events (AEs) from social media.²

Another example illustrating the increasing importance placed on the patient experience is the proposed outcome-based payment model to link the price that the National Health Service (NHS) pays for a cancer drug to patient outcomes³ (See Figure 2). Certain outcomes evaluated are clinical in nature, and can typically be leveraged from traditional sources such as those mentioned earlier; however, many of the other outcomes such as emotions or social functioning can be challenging or even impossible to capture using traditional sources. These represent areas where health-related social media can be used to
complement traditional studies to provide a comprehensive picture of patient outcomes and experiences.

When Should Health-Related Social Media Be Considered?

It is important to think about when it is appropriate and impactful to use health-related social media. These are typically situations when the patient or caregiver perspective and experience are of interest. Health-related social media can be appropriate to gain these insights as the content is driven by patients and caregivers themselves and is therefore more likely to represent topics important to patients that may not have been considered clinically or by the research team. Furthermore, health-related social media can be particularly insightful in the case of emerging conditions where little is known about the patient experience and perspective, such as COVID-19. In the case of rare diseases where large groups of patients can be hard to find, health-related social media forums may be a place where patients from many locations come together. The incorporation of health-related social media should be considered when conducting studies investigating the following:

Once data is extracted from health-related social media sites, it needs to be analyzed in a way that produces useful insights that can be used to inform future clinical studies. There are several techniques that can be used to analyze the data (See Figure 3). Natural language processing (NLP) can be used to subset data to populations and discussions of interest, in addition to extracting frequently mentioned terms and topics. For example, Apache cTAKES is a natural language processing tool developed to extract clinical terms, such as disease symptoms or drugs, from text data. Custom lexicons can be derived using NLP to capture lay terms used by patients in addition to clinical terms. Machine learning (ML) has many applications but for social media data it can be particularly helpful to filter out noise (text that is not relevant to the study objective). Other applications of machine learning can also be explored based on specific study objectives. Qualitative data analysis is also important to social media data. The manual, in-depth analysis helps pull the full potential out of the data, allowing for deeper understanding of concepts and topics being discussed by patients.

Figure 3. Common Types of Analyses

Health-Related Social Media and Ethics

Ethical considerations are extremely important when using any data, especially patient privacy and appropriate use of the data. While there are no specific guidelines on the use of health-related social media data, we believe it is important to follow specific rules to protect the privacy of patients and caregivers. For example, it is important to read the terms and conditions for each site and check whether there are any restrictions around extracting the content. We also look at the robot files, which may indicate whether elements of text can be programmatically retrieved. Any forum where text elements cannot be retrieved would not
be extracted for a study. Additionally, only public, open-access forums should be used, as opposed to a closed forum that requires a login to view content, and researchers should not post to sites. This passive role is important for the integrity of the study and respect of patient privacy.

When using social media websites, seeking informed consent is often not feasible since it is not possible or practical to directly contact users. Per the ethics framework developed by the University of Sheffield,⁴ steps should be taken to protect patient privacy and retain anonymity. All data should be de-identified and anonymized and posts or post content should not be reproduced verbatim or in a manner that allows the original post to be identified from study outputs.