AI in Pharma Marketing, Diagnostics & the Future of Pharma Engagement
Gosia Leitch
Gosia Leitch, VP of Engagement Solutions at Diaceutics, shares key highlights and takeaways from Fierce Pharma Week 2025 in Philadelphia.
Key Takeaway 1: AI’s Present Reality
What role do you see AI playing in pharma marketing?
“AI in Pharma Marketing is no longer a future promise—it’s the present reality. One thing is clear, the industry is undergoing a seismic shift in how we engage HCPs and AI in Pharma Marketing is at the heart of it.”
Key Takeaway 2: Dynamic Segmentation
What’s changing in how we segment and engage HCPs? “Instead of relying on static NPI lists, we use real-world behaviour to identify the right HCPs at the right time. AI in Pharma Marketing is enabling us to:
- Predict engagement and prescribing behavior with real-time data
- Personalize content and channel mix based on generational and contextual preferences
- Automate campaign orchestration while staying compliant”
Key Takeaway 3: Personalization & Automation
How is AI helping us personalize and automate our campaigns?
“It's about delivering the right message in the right way. AI in Pharma Marketing helps personalize content and choose the best channels for each HCP. We can automate campaigns and ensure compliance.”
Discussion Point: Diagnostics Data and AI in Pharma Marketing
You raised an important point about diagnostics data. Why isn’t it being used more widely in real-time HCP targeting?
“What strikes me is that no one is discussing the role of prospective diagnostics data like lab and genomic signals in HCP targeting. Precise targeting only works if the content is timely and relevant. When HCPs receive information about a treatment that matches the patient in front of them, engagement is far more effective.
Unlike claims data, which reflects past events, lab and genomic data offer real-time clinical relevance. Prospective diagnostics data enables:
- HCP engagement before treatment decisions are made—often within <2 days, compared to a week or more with claims data
- Higher HCP response rates 30–45% vs. 10–15%
- Greater Rx lift to 20% compared to 10% with traditional approaches
These are not just incremental gains, they’re transformative. At Diaceutics, we see the difference this makes for commercial teams and the patients they serve. AI in Pharma Marketing becomes even more powerful when it’s combined with real-time diagnostic data”
Our current situation
What do you think is holding teams back from using diagnostics data in real-time targeting?
“Great question. Here are the main barriers:
- Data Access & Integration Challenges – Lab and genomic data often reside in fragmented systems. Integrating these feeds into commercial platforms requires partnerships, infrastructure, and compliance safeguards.
- Legacy Mindsets – Pharma has long relied on claims and EMR data. These sources are familiar, standardized, and embedded in existing workflows.
- Regulatory & Privacy Concerns – Real-time diagnostics data is sensitive. Navigating HIPAA, GDPR, and internal compliance reviews can slow adoption. This is especially the case when teams lack clear frameworks.
- Misaligned KPIs – Many teams focus on metrics such as reach and impressions. Instead they should focus on timely, relevant engagement.
- Underused diagnostic data – Many therapy areas don't use diagnostic data in commercial strategies."
We need to overcome key barriers to unlock the value of diagnostics data and AI in Pharma Marketing. It's clear there is potential to transform pharma engagement.
What’s stopping your team from using real-time diagnostics data? We’d enjoy the opportunity of sharing our use cases and exploring what a difference this would make.
About Diaceutics
We’ve been with pharma and biotech companies for over 20 years, helping them to make more informed diagnostic and therapeutic decisions based on real-world data. Our platforms and tools provide global insight from a network of laboratories across 50+ countries.
This unparalleled visibility into real-world testing helps support therapy programs across oncology, cardiology, neurology, autoimmune, rare diseases, and many more.
How is AI changing HCP engagement in pharma marketing?
AI is enabling a shift from static NPI list targeting to dynamic, real-world behavioural segmentation, allowing pharma teams to predict engagement and prescribing behaviour, personalize content and channel mix, and automate campaign orchestration while staying compliant. As Gosia Leitch, VP of Engagement Solutions at Diaceutics, notes, "AI in Pharma Marketing is no longer a future promise, it's the present reality."
What is dynamic HCP segmentation and how does it differ from traditional targeting?
Dynamic segmentation uses real-world, real-time behavioural data to identify the right HCPs at the right moment, rather than relying on static NPI lists built from historical claims data. This allows commercial teams to personalize content based on generational and contextual preferences, improving both relevance and response rates.
Why is diagnostics data underused in real-time HCP targeting?
Diagnostics data, such as lab and genomic signals, is often overlooked in HCP targeting because it typically resides in fragmented systems, requiring partnerships, infrastructure and compliance safeguards to integrate. Legacy reliance on claims and EMR data, regulatory and privacy concerns, and misaligned KPIs focused on reach rather than timely engagement, all contribute to this gap.
How does prospective diagnostics data improve pharma HCP engagement results?
Prospective diagnostics data, such as lab and genomic signals, offers real-time clinical relevance compared to claims data, which reflects past events. This enables HCP engagement within less than 2 days of a relevant clinical event (versus a week or more with claims data), higher HCP response rates of 30-45% (versus 10-15%), and Rx lift of up to 20% compared to 10% with traditional approaches.
What are the main barriers to using real-time diagnostics data in pharma marketing?
Key barriers include data access and integration challenges from fragmented lab and genomic systems, legacy reliance on familiar claims and EMR data, regulatory and privacy concerns around HIPAA and GDPR compliance, misaligned KPIs focused on reach and impressions rather than timely engagement, and simply underused diagnostic data across many therapy areas.
How can combining AI and diagnostics data transform pharma commercial strategy?
Written By Gosia Leitch
