Computational infrastructure for longitudinal cancer care - modeling disease state across time, treatment, and outcomes to transform fragmented records into continuous clinical intelligence
The structural gap between longitudinal disease progression and episodic documentation creates ambiguity across transitions of care. Three fundamental failures define this infrastructure gap.
Current EHRs store events and documents, but do not represent actual disease state. The patient's trajectory lives solely in the physician's mind — a structural failure in computational representation.
NCCN, ASCO, and ASTRO guidelines remain static documents disconnected from patient timelines. Physicians manually translate guideline logic to individual cases without computational support.
Unlike aviation (flight state systems) or finance (risk models), oncology has no standardized computational representation of disease evolution. Infrastructure, not intelligence, is the bottleneck.
Dovanix defines a new category of healthcare technology. CSIS builds computational models of disease state — continuously interpreting data to generate longitudinal trajectory intelligence.
A computational infrastructure that transforms fragmented oncology data into continuous disease state intelligence. The CSE pipeline processes raw data through structured events to trajectory risk signals.
Labs, imaging, pathology, genomics, notes
Machine-readable clinical timeline
Continuous state modeling
NCCN/ASCO/ASTRO integration
Staging, eligibility, response
Pattern analysis
Deviation detection
The platform embeds four foundational principles that define Clinical State Intelligence Systems.
Embeds NCCN, ASCO, and ASTRO logic directly into the disease model. Guidelines map to state transitions, eligibility conditions, and treatment contexts — ensuring audit-ready, explainable reasoning.
Real-time alerts identifying deviations in patient progression. Not autonomous decisions — signals that enhance physician awareness and support clinical authority.
Activated when risk thresholds are crossed. Automated triggers identify responsible roles, assign response SLAs, and generate audit artifacts for clinical escalation.
Core architectural principle: structured clinical representation (machine-readable events and canonical state models) must exist before AI inference.
Designed to integrate across the oncology stack — serving as the canonical disease-state intelligence layer for health systems.
Structured longitudinal intelligence directly within clinical workflow. Reduces cognitive burden while preserving physician judgment and clinical authority.
Standardizes oncology disease state representation across patients, providers, and care phases. Foundation for system-wide analytics and quality improvement.
AI-powered educational layer built on the same disease state model. Explains pathology, staging, and treatment rationale — reducing clinician workload.
Foundation for clinical trial matching, treatment outcome prediction, and population oncology analytics.
Dovanix defines a new category. While competitors focus on diagnostics, datasets, or predictions — Dovanix builds the computational disease-state infrastructure layer.
Proprietary structured representations of oncology disease progression — built on years of clinical expertise and guideline integration. Deep institutional integration creates switching costs.
NCCN, ASCO, and ASTRO logic embedded directly into computational models. Requires continuous curation, clinical validation, and expert knowledge to maintain.
Network effects from institutional deployment. Each integrated health system contributes to refined models and trajectory pattern recognition.
Becomes the foundational layer for tumor boards, treatment planning, and clinical documentation — creating institutional dependency on the platform.
Imaging, genomics, pathology, and treatment data have reached volumes that make manual cognitive integration impossible. Infrastructure is now the rate-limiting factor.
Large language models and multimodal AI have proven capable of clinical data interpretation at scale. Technology is ready for structured disease modeling.
NCCN guidelines now include hundreds of decision points. Computational guideline integration is no longer optional — it's necessary for quality care.
Join leading healthcare institutions building the future of clinical intelligence. Initial deployment focused on breast oncology with expansion to comprehensive cancer care.
Questions? Contact us directly at admin@dovanix.com