Causal AI Solutions for Healthcare, Real-World Evidence & Life Science

Unlock the true drivers behind key factors – such as health outcomes, treatment decisions, and disease progression based on Real World Data – with Xplain Data’s Causal AI solution, tailored for the healthcare & life sciences sectors.

Proven Technology for Real-World Impact

Xplain Data’s technology is already trusted in large-scale applications — including by German statutory health insurers analyzing medical histories of over 60 million patients. At the core of our solution is ObjectAnalytics®. This patented, object-centered data model transforms fragmented, longitudinal real-world data (RWD) from multiple sources into a single, unified 360° patient view, there­by accel­er­at­ing the dis­cov­ery of causal relationships while simul­ta­ne­ous­ly reduc­ing costs, time, and fail­ure rates in research studies. A gamechanger for data analytics. This unique approach overcomes the limitations of traditional relational databases and forms the essential foundation for powerful, precise Causal Discovery. The result: accurate prediction of patient needs, optimized resource allocation, and clinically meaningful RWE: Causal Real World Evidence.

Understand Therapy Journeys Through a Causal Lens

Causal Drivers in the Therapy Journey

From therapy initiation to treatment changes and discontinuations, every step in the patient journey holds critical insights. But understanding “what happened” is no longer enough. Xplain Data Causal Discovery algorithms uncovers the causal reasons behind:

  • Therapy Starts
    Why do patients begin treatment with a specific product over others in the same market? Identify the underlying drivers influencing new therapy starts and re-starts.

  • Therapy Switches
    Monitor market dynamics and uncover why patients switch to or away from your product. Understand the clinical and behavioral factors influencing these transitions.

  • Therapy Stops
    See how many patients drop out of therapy and explore the causal factors leading to discontinuation – from side effects to socioeconomic barriers.

This module integrates your diagnoses, prescription data, and patient-level demographics. With additional clinical or behavioral data, causal accuracy improves significantly—delivering actionable insights for medical, market access, and commercial teams alike.

Decode the Disease Journey, Not Just the Diagnosis

Causal Pathways in Disease Progression

Track and understand the full disease lifecycle—beyond mere correlations. With our disease journey module, you can:

  • Analyze typical prior conditions and comorbidities.

  • Map how a disease evolves over time: progression, escalation, relapse (e.g., MS flares).

  • Discover direct and indirect causal factors that drive disease outcomes—revealing not only the symptoms, but the full chain of cause and effect.

This enables you to detect early warning signs, anticipate progression, and design targeted interventions with confidence.

Confounder Detection at Scale

Deep Confounder Search for Real-World Studies

There is growing interest in using observational data to assess causal effects; however, such studies rely heavily on strong assumptions—especially the absence of uncontrolled confounding. Traditional methods are inadequate for confounder selection. Since a 2004 study highlighted weak confounder justification in observational research, the STROBE (STrengthening the Reporting of OBservational studies in Epidemiology) guidelines emphasize clearly defining confounders.

Because accurately estimating the causal effect of a treatment in real-world evidence (RWE) settings requires rigorous adjustment for confounding factors. Failure to account for key variables can result in biased and potentially misleading conclusions.

Our advanced Causal Discovery engine performs deep, automated confounder search (without prior assumptions) across millions of combinations, ensuring reliable effect estimates and compliance with the highest methodological standards. With Xplain Data Causal AI, generally applicable potential confounding factors with regard to a specific target can be identified at the click of a button—using your own or publicly available healthcare data. Direct and indirect influencing factors are visually presented using causal graphs.

Why Xplain Data?

By shifting from correlation to causation, Xplain Data empowers healthcare stakeholders to make smarter, faster, and more confident decisions—from product positioning to improving patient or research outcomes. Whether you’re working in RWE, market access, medical affairs, or clinical development, our domain-specific solutions deliver the “why” behind the “what”. With a patented solution that is unique on the market.

Let Causality be your competitive edge.

Check out the intro video on Causal Discovery on HealthCare data here.

Get in touch to learn more!