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  • From Hashtags to Health Outcomes: Using Social Media Listening to Complement Real-World Evidence

    From Hashtags to Health Outcomes: Using Social Media Listening to Complement Real-World Evidence

    From Hashtags to Health Outcomes: Using Social Media Listening to Complement Real-World Evidence

    Real-world evidence (RWE) provides insights about the treatment efficacy in real-world setting and is crucial for patient-focused drug development. Traditionally, RWE is generated using real-world data (RWD) sourced from patient registries, electronic health records, or claims data. In addition, social media listening (SML) is an emerging frontier in RWE offering spontaneous, unfiltered patient expressions through platforms such as X (formerly Twitter), Facebook, Reddit, YouTube, and several health-related and disease-related forums (such as WebMD, AskAPatient, and PatientsLikeMe, etc). If harnessed responsibly, SML can complement traditional sources and provide insights otherwise overlooked.(1)

    The U.S. Food and Drug Administration (FDA) has acknowledged SML as an important avenue for generating patient experience data and incorporating insights into drug development. Similarly,  other global regulators including European Medicines Agency (EMA) and Medicines and Healthcare products Regulatory Agency (MHRA) have also supported the integration of patient experiences in clinical research, although specific SML guidelines are not yet available. Unlike surveys or interviews, SML captures patient narratives in their own words in real-time without influencing responses or burdens on the participants, offering authentic insights into emotional state, unmet needs and symptom burden.(1,2) Patient experiences captured in SML are often underrepresented in clinical research, especially in case of rare diseases or mental illness. Moreover, by collecting vast volume of narratives, SML enables monitoring of health trends.(1-3) Pharmacovigilance (PV) has been a key application of SML, since patients frequently post about adverse drug reactions and other drug experiences on different social media platforms.

    SML often involves three steps: identifying relevant social media data sources (including blogs, forums, platforms such as X, Facebook, etc); selecting content based on inclusion/exclusion criteria, and; coding patient experience such as social context, symptom impact, or treatment narratives. These methodological steps can be handled manually or algorithmically. Of late, artificial intelligence (AI) protocols involving machine learning (ML) and natural language processing (NLP) are improving scalability and precision of SML efforts.(1) The usage of AI has been perceived to improve the accuracy in identifying drug-event relationships while taking care of slangs, informal phrases or misspellings.(4,5). These tools can improve post-marketing surveillance in case of underreporting with traditional methods.

    Despite its potential, SML faces challenges such as skewed trends due to higher number of younger and tech-savvy users on social media, inducing selection bias. Demographic information of patients is not widely and accurately available on social media sources. Relevant social media posts might be fewer, and may contain false-positives. Additionally, data privacy and ethical concerns persist especially regarding commercial use of patient data. Most ethical frameworks insist SML data to be anonymized, publicly available, and used for public benefit.(1,2,4)

    To fully realize the potential of SML, stakeholders must collaborate with regulators and patient advocacy groups to establish clear guidelines and standardize analytical frameworks. Additionally, SML can be integrated into patient-reported outcomes to improve the quality of insights.(1) Thus, SML presents a powerful yet underutilized component of RWE, serving as a valuable component to improve patient-centricity, pharmacovigilance and bridging gaps in drug development.(3,5)

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    References:

    1. Cimiano P, Collins B, De Vuono MC, Escudier T, Gottowik J, Hartung M, Leddin M, Neupane B, Rodriguez-Esteban R, Schmidt AL, Starke-Knäusel C, Voorhaar M, Wieckowski K. Patient listening on social media for patient-focused drug development: a synthesis of considerations from patients, industry and regulators. Front Med (Lausanne). 2024 Mar 6;11:1274688.
    2. Cook NS, Kostikas K, Gruenberger JB, Shah B, Pathak P, Kaur VP, Mudumby A, Sharma R, Gutzwiller FS. Patients’ perspectives on COPD: findings from a social media listening study. ERJ Open Res. 2019 Feb 11;5(1):00128-2018.
    3. Wessel D, Pogrebnyakov N. Using Social Media as a Source of Real-World Data for Pharmaceutical Drug Development and Regulatory Decision Making. Drug Saf. 2024 May;47(5):495-511.
    4. Lavertu A, Vora B, Giacomini KM, Altman R, Rensi S. A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring. Clin Pharmacol Ther. 2021 May;109(5):1197-1202
    5. Sarker A, Ginn R, Nikfarjam A, O’Connor K, Smith K, Jayaraman S, Upadhaya T, Gonzalez G. Utilizing social media data for pharmacovigilance: A review. J Biomed Inform. 2015 Apr;54:202-12.

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  • The Role of Sentiment Analysis in Predicting Market Shifts and Driving Early Market Access Strategy

    The Role of Sentiment Analysis in Predicting Market Shifts and Driving Early Market Access Strategy

    The Role of Sentiment Analysis in Predicting Market Shifts and Driving Early Market Access Strategy

    Market access has evolved into a multidisciplinary strategy and is no longer confined to traditional pricing and reimbursement. It ensures rapid and sustained availability of therapies to the right patients at the right price.(1) As market access continues to evolve and become more dynamic with rising healthcare costs and growing stakeholder diversity, pharmaceutical companies need to look beyond conventional tools. Sentiment analysis (SA), a branch of social media listening (SML) that uses natural language processing (NLP) to classify texts based on opinion-based or emotional content, offers a compelling solution. SA enables early detection of mindset shifts in perception of treatments by mining real-time data from digital platforms.(2,3)

    SA or opinion mining works by classifying text to detect the contextual polarity of the written content as positive, neutral, or negative.(2) Apart from social media which contributes most data for SA, data is also extracted from other sources such as news articles, blogs, forums, or even clinical documents.(3) In pharmaceutical industry, SA provides early insights into how treatments or disease areas are perceived by patients, clinicians and policymakers. It captures real time emotions, doubts and hidden support which are crucial to anticipate product uptake and shaping market access decisions.(4)

    SA has various applications in early market access planning. It can enable competitive intelligence by analysing stakeholder sentiment around drug launches of competitive interventions, revealing enthusiasm or scepticism. It can help in stakeholder mapping by identifying early adopters, digital key opinion leaders (KOLs), and patient advocates, by reading their online presence. It can also detect pricing and reimbursement cues through reactions from patients and payers through policy pushback, public outrage, or unexpected support. Additionally, SA can gauge regulatory and HTA sentiment by analysing tone in consultations, policy drafts, or legislative debates, providing early advantage in navigating access challenges.(1,2)

    SA is already extensively utilized in the real-world setting in the healthcare industry. For instance, online scepticism delayed biosimilar uptake in parts of Europe despite having a regulatory approval. Feedback was captured through clinician commentary and forums.(2) SA has also been used to detect adverse drug reactions, offering earlier risk signals which may not be captured through official surveillance systems.(5,6) SA can add geographic sensitivity revealing how messaging can be pivoted or economic models can be adjusted across countries or subpopulations. This supports early stakeholder engagement in launch planning.(4)

    Despite the promising scope, SA has a few limitations including bias in source data due to overrepresentation of digitally active users, giving skewed insights.(2) Also, sentiment does not always reflect the behaviour or intent, requiring triangulation with other research methods. Ethically, SA needs to responsibly use public content ensuring transparency and privacy.(2)

    In conclusion, SA provides insight into real-world perceptions that influence access outcomes and can be used to transform market access strategies. By understanding early signs of resistance or unmet needs, SA can enable planning and better alignment with key stakeholders. In an increasingly digital healthcare environment, market access teams should embrace SA as a strategic monitoring tool to strengthen early-phase decision-making to ensure long term success.

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    References:

    1. Kumar A, Juluru K, Thimmaraju PK, Reddy J, Patil A. Pharmaceutical market access in emerging markets: concepts, components, and future. J Mark Access Health Policy. 2014 Dec 1;2.
    2. Sharma C, Whittle S, Haghighi PD, Burstein F, Keen H. Sentiment analysis of social media posts on pharmacotherapy: A scoping review. Pharmacol Res Perspect. 2020 Oct;8(5):e00640.
    3. Devika MD, Sunitha C, Ganesha A. Sentiment analysis: A comparative study on different approaches. Procedia Computer Science 2016;87:44–49.
    4. Sorayaie Azar A, Babaei Rikan S, Naemi A, Bagherzadeh Mohasefi J, Wiil UK. Predicting patients’ sentiments about medications using artificial intelligence techniques. Sci Rep. 2024 Dec 30;14(1):31928.
    5. Tricco AC, Zarin W, Lillie E, Jeblee S, Warren R, Khan PA, Robson R, Pham B, Hirst G, Straus SE. Utility of social media and crowd-intelligence data for pharmacovigilance: a scoping review. BMC Med Inform Decis Mak. 2018 Jun 14;18(1):38. 
    6. Coloma PM, Becker B, Sturkenboom MC, van Mulligen EM, Kors JA. Evaluating Social Media Networks in Medicines Safety Surveillance: Two Case Studies. Drug Saf. 2015 Oct;38(10):921-30. 

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