The Earth is not the center of the universe. We know this thanks to Copernicus. Few scientific discoveries have changed our perspective more profoundly than realizing that our planet revolves around the sun, not the other way around.
Healthcare may be approaching a similar moment of reflection.
Today, much of our understanding of healthcare delivery is built around physicians, provider networks, referral patterns, and treatment decisions. These are undeniably important. Yet in focusing on how care is delivered, we sometimes risk underrepresenting how care is actually experienced.
The patient journey remains the common thread connecting every diagnosis, treatment decision, outcome, and interaction with the healthcare system. As healthcare continues its shift toward more patient-centered and value-based models, understanding that journey in greater depth becomes increasingly important.
One way to bring the patient perspective into sharper focus is to listen directly to patients themselves, not just through surveys and solicit environments but through unhinged, unbiased, and unfiltered voices. Increasingly, those conversations are happening in digital communities and social platforms, where individuals (patients and caregivers) openly share their experiences, challenges, concerns, and treatment decisions in their own words.
The data deficit
Social media listening in healthcare isn’t necessarily new. Researchers have studied the topic dozens of times. Some studies suggest as many as 82% of patients believe they are not sufficiently involved in drug development research design. And if healthcare is to experience a Copernican shift, patients must feel their voices are being heard.
Between EHR, claims, imaging, genomic sequencing, and wearables data, across providers, payers, and pharmaceuticals, it’s impossible to truly measure how much data there is in healthcare. And yet, there is a need for more. These previously mentioned data sets are exceptional at mapping out the clinical story of healthcare, especially answering the questions of who, what, where, and when something happened. Where healthcare experiences a data deficit is answering the more human-level questions, like why and how. Why did adherence drop? Why did a patient switch therapies? How did a patient feel months after starting a new treatment plan? These answers are commonly shared across social platforms – upwards of 63% of surveyed Facebook users posted at least one health-related post over the past year.
It’s not to say healthcare hasn’t tried to capture this data. Plenty have been asked the question, “On a scale of 1-10, how much does it hurt?” Or “Was your experience a happy face or a slightly sad face?” Asking a patient to rate an abstract concept like these degrades the human context when using this collective data to make an informed decision.
Scanning the feeds
When data is aggregated from public forums like social media channels, a more complete picture emerges. This data provides context and truly answers the whys and hows of healthcare. On social media, patients are more inclined to voice unsolicited realities. Context is added to complex subjects such as treatment burden, societal stigmas, financial struggles, and logistical difficulties. It can paint a clearer picture of why a patient chooses to skip chemotherapy treatments or puts off their annual exam. When clinicians are informed that a patient is struggling with side effects or simply can’t find parking at the hospital, they can help the patient overcome these challenges.
Beyond the posts
Much like gossip on social media should always be taken with a grain of salt, so should any data from social media used for healthcare decision-making. Social media data should never be a standalone evidence layer. Instead, it must be used with conventional analytics to complement traditional real-world data. Used correctly, there are several possibilities to improve care for patients, especially when this data is used by pharmaceutical companies:
- Creates a better regulatory process: The FDA is actively seeking more patient feedback during drug reviews. Using social media listening as a real-world data source alongside traditional clinical statistics could provide a competitive advantage for drug companies and improve understanding of patient interactions.
- Improves adherence: As many as 8% of patients skip medications when the cost is too much. Social media listening can provide a better understanding of why patients make these decisions, beyond cost.
- Fixes mistimed physician engagement: Pharmaceutical companies often target physicians based on historical revenue and prescribing patterns, by which point many patients are already well into their care journey. Social media listening can help illuminate earlier patient signals, enabling more timely and informed interventions.
- Improves patient support programs: Pharmaceutical companies invest heavily in patient support programs, yet critical patient challenges often go unseen. Social media can reveal recurring barriers around access, onboarding, treatment administration, and affordability that traditional feedback channels may miss.
- Provides a stronger understanding of caregiver burden: In many rare diseases, cancers, and neurological conditions, caregiver burden is often overlooked in traditional data sources. Social media can uncover the emotional, logistical, and financial challenges that shape the real-world disease experience.
- Challenging assumptions held by healthcare stakeholders: Social media listening can expose gaps between what healthcare organizations think patients care about and what patients actually discuss. Often, concerns around convenience, affordability, and quality of life outweigh clinical outcomes in real-world conversations.
Structuring the chaos
Pharmaceutical companies can’t simply turn Facebook posts and TikTok videos into usable data. Social media listening must be conducted using a highly structured, protocol-driven methodology. Using natural language processing and large language models, companies can extract clinically meaningful data from raw text and ultimately categorize concepts such as treatment burden and emotional distress. When paired with conventional data, pharmaceutical companies can better triangulate the patient story.
To scale this process, challenges need to be overcome:
- Privacy: To comply with privacy laws, social media listening must be done with tools that de-identify patient information.
- Gatekeeping: Large tech organizations often monetize social media data, making access difficult. Any social media listening will be focused on public sites and profiles.
- Reporting: Pharmaceutical organizations are required to report adverse events. But this reporting is only necessary if a post meets a strict minimum criterion (which is never met in a social post after de-identification), and the benefits of the new data outweigh the hesitancy.
- Trial integrity: Patients in oncology or rare disease spaces may inadvertently leak proprietary information online; any social media listening will require preprocessing to filter out these signals.
- Representativeness and algorithmic amplification: Social media data is inherently biased toward more digitally engaged patients, and platform algorithms often amplify emotionally charged or highly engaging content. To generate meaningful evidence-grade insights, organizations must account for these biases through statistical normalization, weighting methodologies, and careful validation against broader patient populations.
- Governance and trust: As social media becomes more integrated into healthcare decision-making, robust governance and methodological rigor will be essential to ensure insights are trustworthy, transparent, and responsibly derived.
Healthcare decision-makers, especially at pharmaceutical companies, must prioritize patient input. Data collected from social media listening is a significant step in the right direction. Healthcare has historically optimized for clinical observability through claims, EHRs, registries, and patient-reported outcomes. But many of the most important patient signals were never designed to be captured in those systems. Treatment burden, stigma, caregiver exhaustion, financial toxicity, fear of switching, and daily quality-of-life disruption are increasingly being expressed digitally.
The opportunity with social media listening is not replacing traditional evidence. It is introducing experiential observability into evidence generation. The scientific challenge is whether we can transform noisy, emotionally charged, unstructured narratives into analytically disciplined and reproducible evidence
Photo: elenabs, Getty Images
Rohit Marwah, AVP of Advanced Data Science and Analytics of Definitive Healthcare, has nearly two decades of experience in life sciences, healthcare analytics, and consulting. Rohit specializes in real-world evidence generation, health economics and outcomes research (HEOR), and advanced analytics to support decision-making across pharmaceutical, payer, and provider organizations. His work spans the integration of claims, electronic health records, laboratory data, affiliations, social determinants of health, and emerging digital health signals into scalable analytical frameworks that improve understanding of patient journeys, treatment patterns, healthcare utilization, and clinical and economic outcomes. He has led numerous technology-enabled clinical and population health initiatives and has a particular interest in advancing patient-centered evidence generation. More recently, he has developed and continues to refine innovative, peer-reviewed methodologies that combine traditional real-world data with social and digital patient signals, enabling the generation of scientifically rigorous evidence that captures dimensions of disease burden often overlooked by conventional data sources.
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