Medical Science
For Medical Science
Researchers and clinical faculty run into a reproducibility problem with decision-support-augmented studies: the system used in a study is often a black box, its version, logic, and inputs are not cited in the paper and cannot be independently verified. Such trials are methodologically weak and hard to replicate.
The problem we solve
Medical science needs decision-support infrastructure that:
Supports precise citation
In peer-reviewed work.
Supports multicenter deployment
With identical decision logic.
Provides a data-provenance chain
That survives peer review.
Has a knowledge-creation methodology
That is itself publishable.
Our answer
Citation-ready traceability
Every recommendation issued by the platform is backed by an evidence-of-fact receipt (proof of provenance) — a cryptographic chain of digests from the approved medical knowledge graph store (source of truth) through SHACL validation, the knowledge compiler (the rule-into-module assembler), and runtime execution. This receipt can be cited in a methods section.
Closed truth-distillation cycle as methodology
The closed knowledge-distillation cycle is a formal methodology for creating, validating, and compiling clinical knowledge into a deterministic decision artifact. This methodology is itself publishable. Co-authorship on methodology papers is available.
Multicenter readiness
The Compute core (the bedside execution module) runs the same compiled bundle (rule version) identically at every site. No site-specific AI tuning, no drift between centres. This is the foundation for a multicenter study with a shared, versioned intervention.
Refinement candidate pipeline
Post-study, the audit event log provides a structured corpus of decisions and outcomes. The refinement candidate pipeline formalises how this evidence feeds the next knowledge version — closing the loop from study to updated clinical rule.
Formula for a methods section. “Decision support provided by HealthOS ICU v1.2, bundle digest
sha256:abc…, knowledge graph commitsha256:def….”
Collaboration model
Methodology co-authorship
Contribution to a paper on the closed knowledge-distillation cycle.
Pivotal study design
Joint design of a multicenter trial using the platform as the shared intervention.
Knowledge authorship
Subject-matter experts participate as knowledge authors (expert clinicians who formulate a rule) for their specialty domain.
Documents and training
Full methodology
The closed knowledge-distillation cycle.
Supporting research
Basis and literature.
Glossary
Platform terms.
Medical Science Program
Methodology, evidence-of-fact receipts, refinement candidates, preparation for a multicenter pivotal study.
Research aggregation delivery
Delivery options for research.
P-SCI readiness can support scientific review and research delivery paths, but entitlement and fulfillment are still evaluated independently.
Discuss methodology co-authorship or a pivotal study.