Objectives: Social media is increasingly used  by patient seeking information about drugs and has been analysed to understand attitudes, behaviours and perceptions. however the applicability of social-media analysis to adress specific questions in outcomes research is largely untested. We analysed the representativeness of social-media populations and employed social-intelligence methodologies to study treatment-switching patterns from/to oral therapies in multiple sclerosis (MS). Methods: A comprehensive listening and analysis process was developed which blends automated listening with filtering and analysis of social-media data by life-sciences qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs and online forums which were searched for mention of Tecfidera and Gilenya as examples of oral MS treatments. Results: A total of 10,260 extracted data points were relevant to the objectives and included in the analysis. Women aged 30-49 and diagnosed for >10 years were more active on social media platforms than other MS patients. The identified population was highly similar to that identified in MS market research, thus validating the approach. Treatment switches were most frequent among patients on injectable therapies: 927 data points described patients switching from injectable (mainly BRACE therapies) to oral therapies. Side effects were the main reason (25%) for switching to oral therapies. Switches away from oral therapies were mostly to non-BRACE injectables in a search for greater efficacy. Conclusions: This study of switching patterns in MS treatment shows social intelligence analysis to be a powerful method applicable to outcomes research. The identified population was representative of the wider population when compared with available market research data. Social intelligence was able to quantify switching patterns and to identify factors behind switching behaviour, which were mostly from injectable to oral therapies, driven by side effects from medication.