As we approach ASCO—the Super Bowl of oncology—pharma brand teams are operating in a starkly different climate than in years past.
Budgets are tighter. ROI is under the microscope. And the emphasis is squarely on on-market brands, not just future pipelines.
But here's a provocative thought: what if the data you need to improve field effectiveness, personalize your digital strategy, and drive ROI is already available—and you’re just not using it?
Lab and genomic data, when applied through a multimodal AI framework, could be the missing piece in your commercial strategy. Not just for precision medicine, but across broad “all-comer” indications too.
The Lab: Where 70% of Clinical Decisions Begin
Let’s start with a foundational truth:
70% of clinical decisions are influenced by laboratory data. (Source: CDC, 2022)
Why does that matter to commercial teams?
Because the lab is where the diagnostic journey starts. Not at the EMR. Not at the prescription pad. But often weeks earlier, when a patient first enters the diagnostic funnel.
Lab data—particularly high-frequency, low-latency feeds like complete blood counts (CBCs), tumor markers, and next-generation sequencing (NGS) results—provide a real-time signal of clinical activity. These are not retrospective; they are predictive.
And yet, too many commercial teams are still relying on lagging indicators like claims and EHR pulls that arrive weeks or months after a diagnosis or treatment decision is made.
The Power of Low-Latency Data in Sales & Marketing
Imagine a world where:
- Your field reps know which community oncologists just ordered NGS for lung cancer patients.
- Your digital campaigns dynamically retarget when new biomarker-positive patients are likely to be discussed in tumor boards this week—not next quarter.
- Your KAMs are alerted when a physician at a community practice has a patient who is in the process of progressing from 1st line therapy in multiple myeloma based on an increase in serum free light chains
This is not science fiction. It’s what low-latency lab and genomic data, integrated through AI-driven segmentation and prioritization tools, can deliver today.
Use Case:
A U.S. oncology brand targeting oncogenic fusion in NSCLC integrated de-identified lab and genomic feeds with AI triage models. This allowed reps to prioritize accounts where diagnostic activity suggested imminent treatment decisions. The result? A 22% increase in sales call productivity and a 14% uplift in market share within high-signal regions—without expanding the team.
Beyond Biomarkers: Unlocking Commercial Value in All-Comer Indications
There’s a persistent myth in oncology commercial strategy: lab and genomic data are only useful for biomarker-driven therapies.
Not true.
In fact, some of the most powerful commercial use cases for lab data come in all-comer indications—where the therapy is broadly indicated, but timing and context determine commercial success.
At the center of this opportunity is pathology and diagnostic lab data, which serve as the first clinical breadcrumbs on a patient’s cancer journey.
Pathology: The Earliest Signal of New Patient Diagnosis
The pathology report is often the first formal confirmation of a cancer diagnosis. It typically arrives days or even weeks before any treatment is prescribed, making it a critical window for identifying:
- Which physicians are actively diagnosing new cancer patients
- Which physicians are at the inflection point of clinical decision-making
When this data is aggregated and de-identified across a network of diagnostic labs and interpreted with AI, it provides a real-time map of patient flow—before treatment begins.
This insight is invaluable for commercial teams.
Use Case: Identifying Diagnostic Activity in All-Comer Breast Cancer
A national breast cancer brand—approved for use in early-stage, HER2-negative patients—built a field engagement model using real-time pathology feeds. These feeds tracked increases in confirmed IDC (invasive ductal carcinoma) diagnoses, without requiring HER2 or genomic information.
Using AI models layered on top of the pathology signal, the team could identify physicians and practices showing a surge in new diagnoses, even though no biomarker data was needed.
Reps were routed to these practices within hours of diagnostic confirmation—well before treatment decisions were made.
Result:
The team saw a 20% increase in first-line brand preference in targeted practices, and digital engagement open rates improved by double-digit percentages when emails were triggered by recent diagnostic activity.
The AI Multimodal Framework: Why It Matters
Data in isolation is just noise.
The key is multimodality—blending structured lab/genomic signals with unstructured data (e.g., Path notes, Scanning reports, digital physician behaviors) to generate actionable insights.
AI models that learn across modalities can:
- Detect early diagnostic clusters in underserved geographies
- Predict where guideline-based care is more or less likely to be followed
- Optimize channel mix by matching clinical signals with marketing performance data
This is not just operationally elegant—it is commercially necessary in an ROI-driven environment.
Why This Matters Now
With ASCO weeks away, there will be an avalanche of scientific breakthroughs. But commercial teams should not lose sight of what drives today’s impact:
- Maximizing the yield of on-market brands
- Driving smarter allocation of sales and marketing spend
- Capturing earlier moments of clinical intent
Lab and genomic data—applied through AI—support all three.
And in an era where every marketing dollar must earn its keep, earlier insight means better timing, more relevant messaging, and ultimately, stronger ROI.
Limitations & Considerations
- Data Access: Not all lab or genomic feeds are created equal. Commercial use requires de-identification, compliance with HIPAA and state laws (like CCPA), and payer-approved pathways.
- Interpretation Risk: Without clinical context, raw lab values can be misleading. Multimodal integration helps mitigate this.
- Adoption Lag: Field teams need training to act on AI-powered signals, not just dashboards.
These are solvable challenges—but only if addressed proactively.
Final Thought
If you’re heading to ASCO looking for innovation, don’t overlook your own commercial model.
Your data can be just as transformative as your molecule—if you use it right.
Now is the time to harness lab and genomic intelligence not just to personalize care, but to optimize how we commercialize it.
Let’s talk at ASCO. Schedule a meeting with our team here
