Relationships
Meaning emerges from relationships. Patients are not isolated data points. Care unfolds across time, people, systems, and obligations.
What relationships means to us
Healthcare data is relational by nature. A diagnosis is connected to symptoms, tests, and treatments. A medication is connected to a prescriber, a condition, and potential interactions. An insurance claim is connected to a procedure, a policy, and an outcome.
When you flatten these relationships into documents or lose them in silos, you lose the meaning. The fact exists, but the context that makes it actionable disappears.
Our Claims Graph encodes health as a network of relationships so nothing meaningful is lost. We preserve the connections that matter because understanding requires structure, not just data.
Why relationships matters
Data without context is noise. The same lab value means something entirely different depending on what medications the patient is taking, what conditions they have, what happened last week.
Context
Isolated facts are ambiguous. Relationships provide the context that transforms data into understanding—the "why" behind the "what."
Causality
Understanding cause and effect requires tracking how events connect over time. Relationships encode the causal chains that explain why things happen.
Completeness
Fragmented views lead to fragmented care. Preserving relationships ensures nothing falls through the cracks when information flows between systems.
Care is inherently relational
A patient's health is not a collection of independent facts. It is a story unfolding over time, connecting symptoms to diagnoses, treatments to outcomes, specialists to primary care, clinical reality to financial access.
Current EHR systems treat data as rows in tables. They store facts but lose relationships. When a clinician opens a chart, they must mentally reconstruct the connections that the system failed to preserve.
Silos destroy relationships
Healthcare is fragmented by design—different EHRs, different institutions, different systems that never reconcile. Each silo holds a partial view. The relationships that connect them are lost in translation.
This fragmentation is not just inefficient; it is dangerous. Critical connections are missed because no single system holds the complete picture. Serelora is built to reconnect what fragmentation has torn apart.
How we build relationships into the product
Claims Graph
We model patient information as a graph where clinical, administrative, and financial data are represented as connected entities. Diagnoses, medications, labs, providers, encounters, claims, and benefits are linked through explicit relationships that preserve temporal order, causality, and dependency.
- Every entity connected to its relevant relationships
- Temporal ordering preserved across all data
- Causal chains explicitly encoded as edges
Claims Graph demo
EHR-agnostic architecture
We aggregate data from Epic, Cerner, Athenahealth, uploaded documents, and wearables into one unified graph. The source does not matter—what matters is that relationships are preserved and connected across systems.
- Unified view across all data sources
- Reconciliation of conflicting records
- Provenance tracking for every data point
Data integration demo
Dynamic Action Graphs
Workflows are not rigid pipelines—they are graphs of actions connected by dependencies. When a step fails or conditions change, the system adapts by traversing alternative paths rather than breaking entirely.
- Antifragile workflows that adapt to failures
- Dependency tracking between actions
- Automatic rerouting when conditions change
Action Graph demo
We do not just store data. We preserve the connections that give it meaning.