Lumina turns your revenue cycle, clinical, and workforce data into structured reasoning that surfaces root causes, not just dashboards. Everything runs locally. No PHI ever leaves your environment.
No sign-up required. Your data stays in your environment. Local-first architecture.
The Strategic Gap
Health systems generate millions of data points daily across EHRs, billing platforms, and workforce tools. Yet most organizations still rely on static reports and retrospective analysis. The result: $935 billion in annual waste (JAMA, 2019), rising denials, and clinician burnout costing $4.6 billion per year in turnover alone.
Average claim denial rates of 5-10% cost hospitals over $5M per year in rework, appeals, and lost revenue. Most denial management is reactive, not predictive.
Processing each claim costs an average of $8.70. Multiply that across millions of claims and the cost-to-collect becomes a margin problem hiding in plain sight.
Readmission penalties, quality measure gaps, and infection trends are identified weeks or months after they emerge. Retrospective reporting cannot drive real-time intervention.
Clinician burnout drives $4.6 billion in annual turnover costs. Scheduling inefficiencies, overtime spikes, and misaligned staffing ratios accelerate attrition.
Siloed EHR, billing, and operational data prevent cross-functional analysis. Revenue cycle teams, clinical leaders, and workforce planners operate from disconnected views.
CMS regulations, HEDIS measures, and HIPAA requirements create a moving target. Manual compliance tracking increases audit risk and diverts clinical resources.
What Lumina Does
Lumina does not replace your engineers or your existing systems. It adds a reasoning layer on top, turning the data you already collect into intelligence your teams can act on, verify, and trust.
Lumina's Exploration engine continuously runs hypothesis tests: anomaly clustering, correlation analysis, severity scoring. It delivers findings proactively, the way an experienced operator spots patterns that 'don't look right'.
Through the Dialogue Intelligence Framework™, a six-layer cognitive architecture, agents reason across all your data sources simultaneously. Fragmented data becomes connected understanding.
Configured by your engineers, trained on your procedures, compounding context over time. Each interaction builds a knowledge base that scales.
Tensions that take days in meetings are resolved in seconds. Agents vote, alert, and challenge each other, with every position grounded in data and rationale visible.
Every query is valid SQL. Every calculation executed by a database, not an LLM. When someone asks 'how did you calculate that?', the agent shows the query, the data, and the method.
Your data does not move to us. SaaS, private cloud, on-premises, or air-gapped. Bring your own LLM through Ollama, or export agents for Claude, Copilot, or any MCP-compatible tool.
How It Works
The Dialogue Intelligence Framework™ transforms how healthcare organizations reason about operational, clinical, and financial data. Instead of static dashboards that show what happened, DIF enables structured dialogue with your data to understand why denials spike, where throughput bottlenecks emerge, and how staffing patterns correlate with quality outcomes. All processing happens locally within your environment, ensuring HIPAA compliance by architecture, not by policy.
Ask questions in plain language. Agents respond with reasoned answers, suggest follow-up questions, and guide you toward the decision you need to make.
Proactive scan engine for your data: cluster analysis, correlation, Benford's law, severity scoring. Proactive hypothesis testing that surfaces what you did not know to look for.
Raw findings are synthesized into composites that connect cause, context, and consequence. Not just 'this is anomalous' but 'here is what it means for your operation'.
A six-engine pipeline (DSP, Policy, Semantic Guard, LLM, Classifier, Memory, Audit) judges what agents can access, say, and do. Per-agent configuration. Zero trust by default.
Agents build context over time through a three-tier memory architecture: Local knowledge, team-level awareness, and organizational intelligence that compounds with use.
Reasoning and tools are separated by design. LLMs plan the query. DuckDB executes the calculation on your data. No black-box results. Every step visible.
This is not a chatbot with a database connection. It is a complete cognitive architecture where every layer serves a purpose and every output is traceable.
Explore the Full DIF ArchitectureUse Cases by Domain
Denial Management, Charge Capture, A/R Aging, Contract Modeling
Healthcare revenue cycle inefficiency costs the US system over $265 billion annually in administrative complexity (CAQH, 2022). With average denial rates between 5-10% and each claim costing $8.70 to process, health systems need predictive intelligence that identifies revenue leakage before it reaches the A/R aging report.
Mine denial patterns across payers, CPT codes, and service lines. Surface root causes, prioritize appeals by recovery probability, and track payer-specific denial trends over time.
Analyze CDI effectiveness, detect undercoding and DRG discrepancies, and audit coding compliance. Surface opportunities to improve documentation accuracy and reduce compliance risk.
Analyze aging bucket distributions, score payer performance by payment velocity, and identify accounts at risk of moving to bad debt. Enable proactive collections prioritization.
Compare contracted rates against Medicare benchmarks and peer performance. Model value-based care scenarios and identify underpayments across fee schedules.
CAQH Index (2022): US healthcare administrative spending.
Readmission Risk, Patient Flow, Quality Metrics, Population Health
CMS penalized over 2,500 hospitals for excess readmissions in 2023, with penalties reaching up to 3% of Medicare reimbursements. Meanwhile, health systems struggle to connect quality metrics, patient flow data, and population health insights into a unified operational picture.
Analyze 30-day readmission patterns using LACE score components, diagnosis cohorts, and discharge disposition data. Identify care gaps and high-risk patient segments before discharge.
Track ED boarding times, OR utilization rates, bed turnover metrics, and discharge planning efficiency. Surface bottlenecks that extend length of stay and reduce capacity.
Monitor HEDIS measures, CMS Star ratings, hospital-acquired infection rates, and Patient Safety Indicator (PSI) performance. Correlate quality trends with operational drivers.
Stratify patient populations by risk, analyze chronic disease cohorts, and correlate social determinants of health with utilization patterns. Enable proactive care management.
CMS Hospital Readmissions Reduction Program (2023).
Staff Scheduling, Capacity Forecasting, Physician Productivity
The Association of American Medical Colleges projects a shortage of up to 124,000 physicians by 2034. Combined with nursing turnover rates exceeding 25% nationally, healthcare organizations must optimize every dimension of workforce planning to maintain quality and access.
Analyze nurse-to-patient ratios against acuity levels, identify overtime patterns, and optimize float pool deployment. Connect staffing decisions to quality outcomes and retention metrics.
Predict census levels using historical trends, seasonal demand models, and community health indicators. Plan for surge scenarios with data-driven bed and staffing projections.
Analyze wRVU production by specialty, optimize panel sizes for access and quality, and surface referral pattern intelligence. Enable data-informed provider capacity planning.
AAMC Physician Workforce Projections (2021).
Across the Enterprise
Lumina connects financial, compliance, workforce, and technology data across your organization. Every analysis runs locally within your infrastructure, ensuring HIPAA compliance by architecture.
Analyze margin per service line, cost-to-collect ratios, payer mix trends, and capital allocation scenarios. Connect financial performance to operational and clinical drivers.
Monitor HIPAA compliance posture, track CMS regulatory changes, audit billing patterns for fraud indicators, and manage risk across clinical and operational domains.
Track retention trends, identify burnout indicators across departments, analyze compensation benchmarks, and forecast recruitment needs by specialty and role.
Assess EHR integration maturity, measure interoperability across systems, monitor data quality metrics, and plan infrastructure investments aligned to operational priorities.
Deployment
Five deployment levels from public cloud to fully air-gapped. Bring your own LLM or use ours. Export agents to operate inside Claude, Copilot, or any MCP-compatible tool.
See how Lumina transforms revenue cycle, clinical, and workforce data into structured reasoning. Local-first architecture means no PHI ever leaves your environment.
Learn how DIF transforms static healthcare data into structured reasoning. Understand the architecture that enables local-first intelligence over clinical, financial, and operational data.
Deep DiveWhy healthcare organizations need local-first AI architecture. Explore the case for keeping PHI, clinical data, and operational intelligence within your own infrastructure.
InsightHealthcare dashboards show what happened. They rarely explain why, and they never recommend what to do next. Explore the shift from retrospective reporting to structured reasoning.
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