Q&A: How can a multi-modal approach using lab data simplify precision medicine?
Sarah Heming
In our latest blog, Scott Phillips, VP of Real-World Data at Diaceutics, shares insights on the transformative potential of multi-modal data in precision medicine and its critical role in enhancing patient outcomes and treatment strategies.
1. What is multi-modal data, and why is it important in precision medicine?
A multi-modal approach starts with recognising that every patient's treatment journey is complex. It also addresses the limitations of relying on single data types, which can introduce biases and gaps in a patient’s experience. By combining different types of data, we can build a more complete picture. But connecting data isn't enough, the real value comes from turning it into meaningful, actionable insights. That's what will drive the future of precision medicine and help ensure every patient receives the right treatment at the right time.
2. How can lab data be leveraged to improve the adoption of biomarkers and therapies?
At Diaceutics, we start with genetic and lab data because biomarker insights are central to precision medicine. But lab data alone doesn't tell the whole story. We can build a better picture of the patient journey by combining data sources. Ultimately helping teams make better-informed decisions.
3. How does a multi-modal approach to lab data help simplify the complexities of precision medicine?
Patients receiving precision medicine often see many healthcare providers. This makes it difficult for a single lab data source to capture the full patient journey. By combining data sources, we create a clearer picture that helps more patients get the right therapy sooner.
4. What are the limitations of traditional claims aggregation, and how does a multi-modal strategy overcome them?
The focus of claims data is billing, so it doesn't always reflect a patient's full experience. It shows what happened, but often misses why. A multi-modal approach combines data sources to fill those gaps. Teams then have a more complete view of the patient journey leading to better decisions.
5. How can pharma and biotech companies use multi-modal data to enhance their commercialization strategies?
Success isn't just about combining different types of data. It's about working with a partner who knows how to turn that data into meaningful insights. Bringing together multi-modal data, AI, data science and clinical expertise helps drive better commercialization decisions.
6. What are some examples of multi-modal data driving better patient outcomes?
In our data, we sometimes see claims indicating that a patient has lung cancer. But when we look at lab data and electronic medical record (EMR) data this is not the case. We also see cases where patients may be overlooked had a single data source been used. In these cases patients would miss opportunities to engage physicians and improve outcomes. Every patient deserves the best chance to receive the most effective therapy.
Discover more in our next webinar
Join our webinar on Monday, March 10, from 12–1 PM EDT / 5–6 PM CET. Scott Phillips will explore how genetic and lab data can improve physician engagement and help patients access treatment sooner. The session will also include a live Q&A, giving you the chance to ask questions and hear from an industry expert.
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 Sarah Heming
