Healthcare & Life Sciences
Causality, Not Coincidence.
See the Whole Patient.
ObjectAnalytics™ brings together the collective experience of millions of patients. CausalDiscoverer turns it into causal insights – at the fingertips of every clinician and researcher.
The challenge
The future of medicine is hidden in real‑world data.
Classic analytics can't unlock it.
Scattered across systems and time
Diagnoses, medication, lab values and treatments sit in separate tables – spread over years.
Correlations that look like effects
Hidden confounders make associations look causal – and keep the true drivers out of sight.
You only find what you expect
Classic studies test what you already suspected. Black-box AI can't tell you why.
The Xplain Data way
ObjectAnalytics
unites each patient's full history in one 360° view.
CausalDiscoverer
finds what truly drives an outcome – confounders accounted for, no hypothesis needed.
Step 1 · ObjectAnalytics
Every patient.
A lifetime of events.
One object.
ObjectAnalytics assembles each patient's complete history into one holistic object – in the right time order, across every source. So your causal analysis sees what flattened rows and columns never show: the big picture of every patient.
Patented technology
Step 2 · CausalDiscoverer
From flat shadow to full tomography.
CausalDiscoverer scans the patient object from millions of perspectives – like a CT instead of a flat X-ray – and derives the causal graph directly from your data.
Uncover hidden confounders
Searches the entire patient object – no manual feature engineering – and surfaces confounders no one thought to look for.
Test millions of causal factors
No hypothesis needed: every candidate factor is tested and adjusted for confounders. You find drivers you'd never have guessed.
See the full causal pathway
All factors and confounders come together in one graph – showing how the target causally develops.
See it in action
From ICU records to a causal graph.
Watch a real analysis step by step: CausalDiscoverer explores MIMIC-IV intensive care data to uncover what drives hospital-acquired pressure injuries.
Example from MIMIC-IV · 3 min
Proven in practice
ObjectAnalytics powers Insight Health's Patient Insights Analytics (PIA) – a nationwide pharma analytics platform covering about 77% of all statutory health insurance patients in Germany.
With ObjectAnalytics, we can analyze care holistically across all service areas. No other technology has given us a comparable overall view of our insured members.”
Object-Centric Causal Discovery in MIMIC-IV
Xplain Data with University of Utah and Boise State University. Hypoalbuminemia identified as a candidate upstream driver of hospital-acquired pressure injuries – established risk factors rediscovered without manual feature engineering.
Turn Healthcare
Complexity Into
Actionable Insight
Discover how Causal AI helps uncover the true drivers behind patient outcomes.