INTEGRATING PATIENT-CENTRICITY INTO REAL-WORLD EVIDENCE GENERATION: INSIGHTS FROM SOCIAL MEDIA LISTENING
Author(s)
Aamir Bashir, PhD1, Maureen H. Carlyle, MS2, Phani Veeranki, MD, DrPH3.
1Optum Global Solutions, Gurugram, India, 2Optum, Saint Paul, MN, USA, 3Vice President, Optum Life Sciences, Eden Prairie, MN, USA.
1Optum Global Solutions, Gurugram, India, 2Optum, Saint Paul, MN, USA, 3Vice President, Optum Life Sciences, Eden Prairie, MN, USA.
OBJECTIVES: Traditional real-world evidence (RWE) sources, such as administrative claims and electronic health records (EHRs), often lack comprehensive capture of patient-centric outcomes, including patient-reported outcomes, quality of life (QoL), and lived treatment experiences. Social media listening (SML) has emerged as a novel source of patient-generated health data with potential to address these gaps. This study aimed to synthesize current evidence on SML use in healthcare, and assess its integration with RWE to inform decision-making.
METHODS: A targeted literature review (TLR) was conducted following PRISMA-aligned principles to identify relevant studies published during January 2015 - May 2026. Eligible sources included systematic reviews, observational studies, methodological papers, and industry reports. Searches focused on four domains: pharmacovigilance, patient experience, disease surveillance, and integration of SML with traditional RWE sources. Extracted data included social media platforms, analytical methodologies, and approaches for integration into RWDs.
RESULTS: Eighteen studies met inclusion criteria. SML demonstrated strong utility in pharmacovigilance, particularly in detecting mild-to-moderate adverse events often underreported in traditional systems. In disease surveillance, SML enabled near real-time monitoring of symptoms and health trends. Across platforms such as Twitter, Reddit, and online patient communities, SML consistently captured patient experience insights, including treatment adherence, symptom burden, and QoL. Methodologically, approaches have evolved from rule-based natural language processing (NLP) to advanced techniques, including transformer-based models (e.g., BERT). Integration with conventional RWE occurred primarily through cohort-level alignment, with limited patient-level linkage and applications in pharmacovigilance systems. Key challenges included data heterogeneity, signal-to-noise variability, lack of standardization, and evolving regulatory uncertainty.
CONCLUSIONS: SML represents a high-potential complementary data source that enhances RWE by capturing patient voice and contextualizing clinical outcomes. While its utility is established in pharmacovigilance, scalable integration into HEOR and regulatory-grade evidence generation remains limited. Advancing standardized frameworks, methodologies, and regulatory alignment will enable adoption and integration of SML into RWE and healthcare decision-making.
METHODS: A targeted literature review (TLR) was conducted following PRISMA-aligned principles to identify relevant studies published during January 2015 - May 2026. Eligible sources included systematic reviews, observational studies, methodological papers, and industry reports. Searches focused on four domains: pharmacovigilance, patient experience, disease surveillance, and integration of SML with traditional RWE sources. Extracted data included social media platforms, analytical methodologies, and approaches for integration into RWDs.
RESULTS: Eighteen studies met inclusion criteria. SML demonstrated strong utility in pharmacovigilance, particularly in detecting mild-to-moderate adverse events often underreported in traditional systems. In disease surveillance, SML enabled near real-time monitoring of symptoms and health trends. Across platforms such as Twitter, Reddit, and online patient communities, SML consistently captured patient experience insights, including treatment adherence, symptom burden, and QoL. Methodologically, approaches have evolved from rule-based natural language processing (NLP) to advanced techniques, including transformer-based models (e.g., BERT). Integration with conventional RWE occurred primarily through cohort-level alignment, with limited patient-level linkage and applications in pharmacovigilance systems. Key challenges included data heterogeneity, signal-to-noise variability, lack of standardization, and evolving regulatory uncertainty.
CONCLUSIONS: SML represents a high-potential complementary data source that enhances RWE by capturing patient voice and contextualizing clinical outcomes. While its utility is established in pharmacovigilance, scalable integration into HEOR and regulatory-grade evidence generation remains limited. Advancing standardized frameworks, methodologies, and regulatory alignment will enable adoption and integration of SML into RWE and healthcare decision-making.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
PT25
Topic
Patient-Centered Research, Real World Data & Information Systems, Study Approaches
Topic Subcategory
Patient Behavior and Incentives
Disease
No Additional Disease & Conditions/Specialized Treatment Areas