Democritus Labs
Auditable Analytics & Decision Intelligence

Auditable analytics and insights you can defend

Democritus Labs provides a lightweight semantic layer over your existing ERP, CRM, and operational systems—delivering auditable, explainable analytics without ripping and replacing your stack.

Traceable KPIs
Every number maps back to source + logic.
Explainable insights
Drivers and “why” — not just charts.
Typical pilot deliverables
  • Governance-ready dashboard with clear metric definitions
  • Lineage map from sources → transformations → KPIs
  • Insight layer to explain deltas, anomalies, and drivers
What “auditable” means
Explicit KPI logic • Reproducible outputs • Source traceability • Change history
Healthcare Decision Intelligence Why analytics stalls

The Shift Healthcare Organizations Must Make

Healthcare analytics has matured — but decision-making has not. Most organizations already have clean data, modern warehouses, and dashboards that report what happened. The real gap shows up when leaders ask the questions that actually matter: why an outcome happened, what alternatives existed, and what would change if a different action was taken.

The next leap forward is not more data, faster queries, or prettier dashboards. It is the ability to reason about decisions.

From Metrics to Situations

Decisions aren’t made on isolated metrics. They’re made in situations shaped by clinical context, operational constraints, timing, and uncertainty. Reasoning about situations is how data starts to match real clinical and operational reality.

From Static Scores to Probabilistic Risk

Risk is rarely binary. Instead of fixed thresholds and static scores, organizations need risk assessments that express confidence and update as evidence changes — the practical advantage of Bayesian risk estimation.

From Outcomes to Actions

Reporting outcomes is not the same as improving them. Decision intelligence evaluates actions: what options were available, what was chosen, and how different choices would have changed results.

From Dashboards to Defensible Decisions

Under leadership review, audit, or regulatory scrutiny, charts aren’t enough. Organizations need clear, traceable explanations: why a decision was made, what assumptions were considered, what alternatives were evaluated, and how uncertainty was handled.

What enables this shift

Semantic understanding Bayesian risk estimation Causal evaluation

Together, these turn analytics into a system for responsible, defensible decision-making — and set the foundation for everything we offer below.

Offering Platform layer

The Democritus Labs Platform Layer

Audit-ready decision intelligence on top of your existing systems. Democritus Labs sits above ERP, CRM, clinical, claims, finance, and operational data to produce analytics that can be explained, defended, and trusted — without rebuilding or replacing your stack.

Instead of fragmented metrics and opaque dashboards, the platform establishes a shared meaning layer and generates analytics with explicit logic, assumptions, and lineage.

What you get

A single source of truth for decisions — not just numbers.

  • Semantic mappings across ERP, CRM, and operational data
    Align concepts and metrics across systems without schema rewrites.
  • Governed KPIs with definitions and change history
    Every metric has an owner, logic, assumptions, and versioning.
  • Auditable dashboards with full lineage
    Trace every number back to its source data and transformation logic.
  • Explainable insights
    Understand drivers, anomalies, and deltas — not just trends.

How it works

Three steps. No rip-and-replace.
Step 1
Discover

Align on decisions, KPIs, data sources, and review or audit constraints.

Step 2
Define

Lock metric logic, semantic mappings, and lineage so outputs remain consistent and defensible over time.

Step 3
Deliver

Ship dashboards, evidence packs, and lightweight monitoring for change and drift.

Designed to work with your stack

Soft layer. Zero rip-and-replace. Integrates with systems of record without disrupting core workflows.

Built for review, audit, and accountability

Auditability is native. Every output includes definition, assumptions, lineage, and historical changes — so answers are ready when numbers are questioned.

Autonomous analytics workflows

Automate KPI refresh, anomaly detection, change monitoring, and insight narratives — reducing manual reporting while improving consistency and trust.

Why this is different

Most analytics platforms answer what changed. Democritus Labs answers why it changed — and whether the decision still holds up.

Data Source Clinical / Insurance / Pharmacy

Data Source

Collect, validate, and baseline trust before you analyze. If this layer is weak, everything above becomes untrustworthy.

What happens

  • Ingest pharmacy claims, dispensing records, PBM feeds, and NDC catalogs.
  • Validate fill integrity: quantity vs days-supply, refill gaps, early refills, reversals.
  • Normalize drug identity across NDC, generic, brand, and therapeutic class.
  • Align dispense events with prescriptions and encounters.
  • Track lineage for pricing, formulary rules, and PBM transformations.

Outputs

  • Longitudinal medication timelines
  • Adherence & persistence metrics (MPR, PDC)
  • Pharmacy data quality scorecards
  • Lineage metadata for drug, pricing, and coverage logic
DQ: reversals / refills Normalize: NDC → class Provenance: PBM rules

Architecture

Click a layer to see what happens inside.

Inference Semantic Reasoning Ontology Layer Data source Clinical DQ Insurance DQ Pharmacy DQ
Tip: click a layer. The panel updates instantly.

Click a source system to see how it becomes decision-ready

Provider-focused: EHR, claims, finance, quality, operations

How it is converted

Same pipeline, source-specific mappings
  1. 1
    Ingest
    Pull from the source using read-only connectors (SQL, HL7/FHIR, files, APIs). No disruption to systems of record.
  2. 2
    Normalize
    Standardize timestamps, codes, identifiers, and units. Create consistent encounter, patient, provider, and service primitives.
  3. 3
    Map to shared meaning
    Convert local fields into governed concepts and KPIs with definitions, owners, and change history.
  4. 4
    Semantic reasoning
    Interpret data as real operational situations (not just rows). Make assumptions explicit so metrics remain stable and defensible.
  5. 5
    Bayesian risk estimation
    Produce probabilistic risk with confidence that updates as evidence changes (e.g., evolving deterioration risk, discharge risk).
  6. 6
    Causal evaluation
    Evaluate actions and alternatives to estimate impact on outcomes (not just correlation). Generate evidence packs for review.
  7. 7
    Deliver
    Dashboards, insight narratives, monitoring for drift, and audit-ready lineage for every number and decision.

What this looks like for:

EHR (Epic / Cerner / MEDITECH)
  • Sources: encounters, orders, meds, labs, vitals, notes metadata
  • Normalization: patient/encounter linkage, temporal alignment, code standardization
  • Meaning layer: LOS, readmission, sepsis workflow timing, discharge readiness
  • Outputs: defensible quality dashboards + risk updates + review packs
Claims (835/837, payer feeds)
  • Sources: remits, denials, authorizations, billed/paid amounts
  • Normalization: service lines, coding consistency, payer policy alignment
  • Meaning layer: denial reason taxonomy, avoidable denials, leakage drivers
  • Outputs: explainable denial analytics + policy drift alerts + audit trail
Finance / ERP (GL, charges, cost centers)
  • Sources: charges, cost centers, staffing cost, supply expense
  • Normalization: department mapping, time windows, allocation assumptions
  • Meaning layer: service line profitability, cost drivers, utilization economics
  • Outputs: defensible margin bridges + variance explanations + evidence pack
CRM / Access (referrals, outreach)
  • Sources: referral pipelines, call center activity, access delays
  • Normalization: patient identity resolution, provider attribution, timelines
  • Meaning layer: leakage risk, conversion bottlenecks, access SLA compliance
  • Outputs: explainable access insights + drift monitoring + action tracking
Supply Chain (items, POs, PAR)
  • Sources: item master, purchasing, usage, stocking, backorders
  • Normalization: item equivalence, unit conversions, location mapping
  • Meaning layer: stockout risk, utilization anomalies, standardization savings
  • Outputs: explainable variance + risk flags + audit-ready sourcing rationale
Quality & Ops (HEDIS, PSI, workflows)
  • Sources: quality measures, incidents, process timestamps, ops checklists
  • Normalization: denominator definitions, timing logic, exclusions
  • Meaning layer: measure governance, operational situations, review readiness
  • Outputs: defensible quality reporting + leadership review packets
Key point

We don’t “duplicate analytics.” We convert each source into governed meaning, risk with confidence, and decision-ready outputs — with lineage you can defend.

Why it matters SQL vs Decision Intelligence

SQL Is Necessary. It’s Not Sufficient.

SQL excels at querying tables. Healthcare leaders need systems that can explain meaning, quantify uncertainty, evaluate actions, and stay defensible under review.

Traditional SQL Analytics

Great for reporting. Fragile for decision accountability.

Descriptive
  • Answers: “What happened?”
    Aggregates, filters, joins, and trends over structured data.
  • Definitions live in code
    Logic scattered across views, BI layers, notebooks, and analyst “tribal knowledge.”
  • Weak context handling
    Hard to represent real situations, workflows, timing, and constraints without brittle engineering.
  • No uncertainty model
    Scores and thresholds don’t express confidence or update naturally with new evidence.
  • Cannot evaluate actions
    Correlation-heavy analytics struggles to answer “What would change if we acted differently?”
  • Audit is manual
    Reconstructing metric logic and changes becomes a time-consuming scramble.

Decision Intelligence Layer

Built for meaning, risk, action, and defensibility.

Decisional
  • Answers: “What does it mean?”
    Turns raw fields into governed concepts and situations that match operational reality.
  • Governed definitions, versioned
    Metrics have owners, assumptions, change history, and consistent semantics across teams.
  • Context as first-class structure
    Represents workflows, timing, constraints, and situations without endless brittle SQL layers.
  • Probabilistic risk with confidence
    Bayesian estimation updates risk as evidence changes and communicates uncertainty explicitly.
  • Evaluates actions and alternatives
    Causal evaluation estimates impact of interventions — not just associations.
  • Audit-ready by design
    Every number and decision carries lineage, assumptions, and evidence for review.
Bottom line

Keep SQL for what it does best. Add a decision intelligence layer to make analytics consistent, explainable, uncertainty-aware, and defensible under leadership review and audit.

Contact Democritus Labs

Send your use case and data context. We’ll respond with a concrete next step.

Email: info@democrituslabs.com
Based in: New York, USA

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