*Names in bold indicate Presenter
The first case study describes the efforts of the Centers for Medicare & Medicaid (CMS) Office of the Medicare Ombudsman (OMO) to identify systemic beneficiary issues and unintended consequences using social media data analysis. According to Pew Research Center, as of April 2012, over half of American adults ages 65 and older reported using the internet or email. The goal of the OMO environmental scanning demonstration was to develop mechanisms to gather feedback from Medicare beneficiaries, caregivers, and advocates to make recommendations to Medicare to improve the beneficiary experience.
The second case study describes the methodology and results of an Office of National Drug Control Policy-sponsored study that gauged the influence of drugs and alcohol associated online content on youth. Most youth use social media daily and teens are increasingly multitasking across multiple media with estimates as high as10 hours a day (Kaiser 2010; Common Sense Media 2012). Automated social media content scans and virtual observations of blog conversations and social media postings were employed to collect and analyze drug- and alcohol- related interactions occurring online.
A synthesis of limitations and subsequent lessons learned includes:
- Neither the social media data collected through automated scanning tools nor virtual observations can be considered representative of a specific population or subgroup. Therefore, it is difficult it is to attribute content to specific individuals—oftentimes engagement with social media occurs with near anonymity—and the corresponding lack of information on the coverage of these data, prevent construction of accurate population-based estimates. This results in exploratory and descriptive findings.
- People use different terms to describe issues and concerns online than policymakers, healthcare providers, or researchers.
- In content analysis, specific keywords yielded more relevant or precise data. Keyword based searches, thus, evolve in real-time based on findings.
- Automated scanning must be paired with manual research – and supporting data analysis if available – to understand the context and scope of the information.
- Automated scanning identifies innovative sources of information that may not be otherwise identified through manual searches.