When Christine Lee, PharmD, PhD, a health scientist with the US Food and Drug Administration’s (FDA’s) Center for Drug Evaluation and Research, wanted information about caring for her newborn baby beyond what was captured in parenting books, she turned to social media. She joined a forum for new mothers on Facebook to ask the crowd for advice.
Soon after, she wondered how social media platforms such as Facebook, Twitter, Instagram, and Reddit can be mined for data that could help the FDA’s pharmacovigilance efforts. “I thought, ‘Could I apply the same qualitative research methods traditionally used for focus groups and cognitive interview data to research involving unstructured narrative data in social media?’ ” explained Dr Lee during her grand rounds presentation, “Structuring Unstructured Data: Using New Data Sources to Understand the Needs of Underserved Populations,” presented May 9, 2019, in Washington, DC.
How the FDA Uses Patient Experience Data
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The agency’s regulatory mission relies on sourcing new data and methodologies to increase its understanding of patients’ perspectives. The FDA uses patient experience data to inform:
• Clinical trial design
• Trial end point development and selection
• Regulatory issues, including benefit-risk assessments
To meet patients’ needs, the FDA strives to engage patient stakeholders throughout the life cycle of a medical product. Social media platforms provide a method for capturing meaningful, unfiltered patient insights, according to Dr Lee. It can give the FDA a more comprehensive picture of how medical products function beyond controlled, randomized clinical trials.
The FDA has 2 objectives for engaging patients:
1. Support the FDA’s and the Center for Drug Evaluation and Research’s goals of understanding the patient’s voice, including their perspectives on conditions and treatments.
2. Refine qualitative research methods to explore tapping unstructured data with high repeatability.
Social Media Monitoring: Gaining Insight Into Diabetes Therapies
The US Department of Health and Human Services’ Office of Minority Health funded a pilot study to analyze social media posts for useful data about minority patients with diabetes and their treatment.
During the last 2 years, the agency used data-mining software to collect, monitor, and analyze more than 100,000 conversations on Twitter. The effort targeted a variety of keywords, including diabetes, glucose, diabetic, and blood sugar. Demographic filters were added to narrow the query. To separate signal from noise, tweets that were classified as cheeky, advertisements, or spam were removed, leaving about 73,000 tweets for analysis. The remaining tweets were scrubbed and formatted, and then natural language processing and machine-based learning were used to help researchers identify trends. This process was also used to uncover trends in FDA Advisory Committee data, and then the 2 data sets were examined.
