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Home Uncertainty & Complexity

When the Data Lags Behind the Pain: Racial Disparities in Early-Onset Colorectal Cancer Take Center Stage

New findings presented at ASCO 2025 reveal that Black and Hispanic patients with colorectal cancer are being diagnosed later—and louder—than their white counterparts, as social media activism fills the silence left by clinical blind spots.

Edebwe Thomas by Edebwe Thomas
June 7, 2025
in Uncategorized
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The silence wasn’t in the data—it was in the delay. That was the message that reverberated across the oncology world following this year’s ASCO Annual Meeting in Chicago, where new real-world data revealed a troubling and persistent disparity: younger Black and Hispanic patients with early-onset colorectal cancer (CRC) continue to present at later stages than their white counterparts.

Though these findings echo patterns long observed in community clinics and emergency departments, their formal spotlight at ASCO 2025—one of the largest and most influential cancer conferences in the world—gave them the imprimatur of institutional legitimacy. And the online world responded accordingly. #CRCawareness surged to the top of LinkedIn, X, and Instagram, as physicians, patient advocates, and influencers began sharing personal narratives and calling out systemic failures with urgent clarity.

Yet the resonance of this moment lies in more than numbers. It lies in what the numbers missed.

The ASCO data, drawn from multi-center observational studies and real-world clinical practice registries, show that Black and Hispanic individuals under 50 are not only more likely to be diagnosed with colorectal cancer at more advanced stages, but also less likely to receive guideline-concordant screening or timely diagnostic follow-up after initial symptoms. The causes are multifactorial—delays in referrals, insurance barriers, underestimation of symptoms by clinicians—but the common thread weaving through them all is implicit bias.

Implicit bias in medicine is rarely overt. It manifests in delays, dismissals, and unspoken assumptions about patient reliability, health literacy, and risk factors. As Dr. Liana Forbes, an oncologist at Montefiore Medical Center, stated during a post-presentation panel, “Bias is not always about who gets care—it’s about who gets taken seriously. For too long, younger Black and Latino patients reporting symptoms have been told it’s just hemorrhoids or stress. That diagnostic inertia is killing people.”

While clinical data finally catch up to these lived realities, it is social media—not peer-reviewed journals—that has become the primary amplifier of the patient voice. In the days following ASCO’s publication of the CRC disparities data, dozens of patient advocacy organizations and individual survivors took to digital platforms to share their delayed diagnosis stories, many of which echoed a common refrain: “No one believed me until it was Stage III.”

These viral testimonials underscore the gap between empirical evidence and experiential knowledge—a gap that social media, for all its limitations, is uniquely positioned to bridge. Platforms like X and TikTok provide spaces where stories of dismissal and delay, often filtered out of academic abstracts, find resonance and accountability. In this sense, the digital backlash isn’t just reactionary—it’s a form of real-time peer review, one driven by patients rather than professionals.

The timing is significant. Colorectal cancer is increasingly affecting younger adults, a trend documented in recent years by the American Cancer Society and the CDC. Yet screening guidelines and risk algorithms remain anchored in outdated demographic assumptions. While the USPSTF did lower the starting age for routine CRC screening from 50 to 45 in 2021, this move has yet to translate equitably across racial and socioeconomic lines. For many minority patients, systemic barriers—lack of insurance, transportation, provider bias—still push effective screening out of reach.

Moreover, the medical system’s reliance on population-level data often obscures the urgency of individual-level disparities. Risk models based on age and family history fail to account for social determinants of health, environmental exposures, and access to care—all of which disproportionately affect Black and Hispanic populations. The result is a persistent clinical blind spot: a system that assumes uniformity while patients experience variability.

Social media, in contrast, elevates the variability. Posts tagged with #CRCawareness often contain intimate accounts of misdiagnosis, emergency room visits turned colonoscopies, and stage IV revelations after years of ignored symptoms. These narratives are not statistical anomalies; they are the connective tissue that gives abstract data its visceral meaning.

Yet this grassroots awareness campaign also raises challenging questions: Can social media-driven virality translate into systemic change? Or does it merely create visibility without altering the infrastructure that produced the disparities in the first place?

There are signs of potential. Following the digital traction of #CRCawareness post-ASCO, several major academic medical centers announced task forces to reassess their internal screening protocols and implicit bias training. Public health departments in cities like Philadelphia and Atlanta have also initiated community outreach programs specifically targeting early-onset CRC education in high-risk populations.

Still, experts warn against assuming awareness equals resolution. As Dr. Jamal Harris, a public health sociologist at UCLA, notes, “We’ve seen this cycle before: spike in visibility, spike in promises, and then inertia. Until data collection, clinical algorithms, and reimbursement policies center racial equity—not just mention it—we will see these disparities persist.”

The ASCO moment matters not because it revealed something new, but because it validated what many already knew. The disparities in early-onset colorectal cancer were never invisible. They were simply ignored until the convergence of data and digital outcry made them impossible to dismiss.

In the end, the path forward lies not in choosing between data or narrative, but in integrating them. Clinical trials must prioritize racially diverse enrollment. Electronic health records must capture social determinants alongside lab values. And yes, conferences like ASCO must not only present disparities but demand accountability in how institutions respond to them.

Until then, patients will keep telling their stories—not in waiting rooms, but online, where their voices are finally heard.

The question is whether the healthcare system is ready to listen—and act.

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Edebwe Thomas

Edebwe Thomas

Edebwe Thomas explores the dynamic relationship between science, health, and society through insightful, accessible storytelling.

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In this episode, the host discusses the significance of large language models (LLMs) in healthcare, their applications, and the challenges they face. The conversation highlights the importance of simplicity in model design and the necessity of integrating patient feedback to enhance the effectiveness of LLMs in clinical settings.

Takeaways
LLMs are becoming integral in healthcare.
They can help determine costs and service options.
Hallucination in LLMs can lead to misinformation.
LLMs can produce inconsistent answers based on input.
Simplicity in LLMs is often more effective than complexity.
Patient behavior should guide LLM development.
Integrating patient feedback is crucial for accuracy.
Pre-training models with patient input enhances relevance.
Healthcare providers must understand LLM limitations.
The best LLMs will focus on patient-centered care.

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00:00 Introduction to LLMs in Healthcare
05:16 The Importance of Simplicity in LLMs
The Future of LLMs in HealthcareDaily Remedy
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AI Regulation and Deployment Is Now a Core Healthcare Issue

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Ambient Artificial Intelligence Clinical Documentation: Workflow Support with Emerging Governance Risk

Ambient Artificial Intelligence Clinical Documentation: Workflow Support with Emerging Governance Risk

by Daily Remedy
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Health systems are increasingly deploying ambient artificial intelligence tools that listen to clinical encounters and automatically generate draft visit notes. These systems are intended to reduce documentation burden and allow clinicians to focus more directly on patient interaction. At the same time, they raise unresolved questions about patient consent, data handling, factual accuracy, and legal responsibility for machine‑generated records. Recent policy discussions and legal actions suggest that adoption is moving faster than formal oversight frameworks. The practical clinical question is...

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