Lumina turns your revenue cycle, clinical, and workforce data into structured reasoning that surfaces root causes, not just dashboards. Your raw data is processed locally and never uploaded, and the firewall can strip sensitive fields before any AI request.
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, turning fragmented data into 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, with each calculation executed by a database rather than 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 model, self-hosted or via any OpenAI-compatible endpoint, or export agents for Claude, Copilot, or any MCP-compatible tool.
How It Works
Most AI tools are a wrapper around a single language model. Lumina is built on DIF, so working with your data is a reasoning dialogue: a plain-language question goes in, and a traceable, computed answer comes out.
Every answer is one you can inspect, not a black box. And the memory the system builds as you work compounds over time, so each question starts from everything your team has already asked.
Use 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. Data analysis runs within your own infrastructure, an architecture designed to support HIPAA requirements.
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.
Be the first to know when new agents, connectors, and industry solutions go live.
We will keep you posted on industry insights and product updates as we ship them, and on the agents we release for your sector. You can read how we handle your information in our Privacy Policy, and you can unsubscribe whenever you like.