Nationwide Data Lake Consolidation at a US Healthcare Provider — 3 Warehouses Unified, Real-Time Operational Analytics
AWS-based data lake consolidating three warehouses, EMR migration and a real-time operational analytics layer for cross-state hospital operations.
The challenge
The client — a leading US healthcare provider operating across multiple states with a substantial network of hospitals, outpatient clinics, rehabilitation centres, skilled-nursing facilities and assisted-living locations — was experiencing 47% year-over-year volume growth across its five operating sectors. The growth had outpaced the data infrastructure. The provider was running three distinct AWS-based data warehouses (the legacy heritage of historical M&A activity), with overlapping-but-inconsistent data, no single source of truth, and a separate EMR system that did not integrate cleanly with any of them.
The operational consequence was acute. The executive team lacked visibility into the network-wide spend pattern, the network-wide demand pattern, and the network-wide capacity utilisation. The MIS-and-monthly-reports cadence meant that the operational decisions were reactive rather than proactive — by the time a staffing-shortfall or a capacity-utilisation issue surfaced in the monthly report, the operational quarter was already substantially impacted.
Specific structural concerns were the inability to do network-wide fraud-detection (each warehouse's fraud signals stayed within that warehouse, with no cross-warehouse pattern detection), the inability to do network-wide inventory-and-spend management (each warehouse's spend data had its own classification scheme), and the inability to support the network's growth ambitions on the existing data infrastructure.
The approach
MindMap deployed an analytics-platform engagement composed of Data Lake Builder for the consolidation work, FP&A Forecaster (Ff) for the analytics layer, Anomaly Detector (Ad) for the fraud-detection workflow, and Real-Time Visualiser for the operational dashboards.
Phase one was the discovery-and-mapping work. Through a structured process-discovery engagement, we identified roughly sixteen distinct potential consolidation areas across the three warehouses where data was being maintained redundantly or inconsistently. The discovery covered the patient-encounter data, the billing-and-revenue-cycle data, the clinical-and-quality-metrics data, the staffing-and-utilisation data, the supply-chain-and-inventory data and the financial-management data.
Phase two was the data-lake architecture-and-build. The platform consolidates the three warehouses into a single AWS-native data lake with a unified data model. ETL pipelines per source warehouse handle the schema-mapping-and-data-cleansing work; the consolidated data lake serves as the single source of truth for all downstream analytics consumption. The EMR migration runs in parallel — the new unified EMR feeds into the same data lake, replacing the previous separate EMR-system-feed.
Phase three was the analytics-layer build. FP&A Insights provides the descriptive and predictive analytics across the consolidated data. The department-head dashboard (showing outpatient charges, inpatient charges, discharges-by-service-type and physician-and-clinic visits across the country) gives the executive team the network-wide operational view that the previous warehouse-fragmentation had prevented. Predictive analytics support the demand-and-capacity-planning workflow, the staffing-shortfall identification workflow and the supply-chain-spend-optimisation workflow.
Phase four was the fraud-detection workflow. Anomaly Detector runs against the consolidated billing-and-revenue-cycle data to identify cross-warehouse fraud-patterns that had been invisible in the per-warehouse approach. The detection covers the per-provider, per-procedure, per-patient and per-line-of-business pattern-spaces with real-time alerting on identified anomalies.
The pre-built building blocks
Rather than commission a ground-up build, the engagement leaned on MindMap's pre-built accelerator library — production-tested components that compress what would otherwise be a six-to-nine-month build into weeks.
FP&A Forecaster
Healthcare-operations analytics and reporting layer
Anomaly Detector
Cross-warehouse fraud-pattern detection
Real-Time Visualizer
Operational dashboards with near-real-time refresh
Data Lake Architect
AWS-native data-lake consolidation across heritage warehouses
The architecture
The platform runs on the provider's existing AWS environment with HIPAA-eligible infrastructure across the relevant AWS services. The data lake is hosted on S3 with the analytical-query layer on Athena and the dimensional-model on Redshift for the high-frequency dashboard queries.
The ETL pipeline is built on AWS Glue with per-source-warehouse extraction-jobs handling the schema-mapping and the data-cleansing work. The pipeline produces a curated zone in the data lake with the consolidated source-of-truth data; the raw zone retains the original-format extracts for the data-lineage and audit-trail purposes.
FP&A Insights' analytics layer runs against the curated zone with the dimensional-model accessed for the high-frequency dashboard queries and Athena accessed for the ad-hoc exploration work. The platform supports both deterministic report templates (the recurring management-and-operational reports) and LLM-driven self-service analytics (the ad-hoc executive questions that translate into structured queries through natural-language interfaces).
Real-Time Visualiser provides the operational dashboards with the country-wide spend map, the per-region demand-and-capacity view, the per-service-type discharge analysis and the executive-level operational summaries. Dashboard refresh runs on a near-real-time cadence (typically under five minutes) for the most operationally-critical metrics.
Anomaly Detector's fraud-detection workflow uses a combination of rule-based detection (the known fraud-patterns from the provider's compliance team), statistical-anomaly detection (the per-provider and per-procedure outlier analysis) and predictive-modelling (the supervised-learning models trained on the provider's confirmed-fraud history). Identified anomalies route to the compliance team's investigation workflow with the supporting evidence preserved.
The numbers behind the story
Customer satisfaction improved 97% across the operational period through the combination of the personalised-service-pattern visibility (the analytics surfaces the customer-specific service preferences and history) and the inventory-management improvements (the demand-driven inventory positions support consistent service availability rather than the previous reactive stocking patterns).
Fraud detection improved 78% through the cross-warehouse pattern detection that the per-warehouse approach had been structurally incapable of supporting. The predictive-model-driven fraud-alerting catches several million dollars annually of potentially-fraudulent activity that the previous workflow would have missed.
Manual-work in the data-management-and-reporting workflow reduced 89% through the data-consolidation and the analytics-automation. The data-management team's capacity has been redirected to the data-governance-and-quality work that the consolidated environment now supports at scale.
Staffing efficiency improved substantially. The platform's analytics surfaced the geographic and service-line areas with structural staffing-imbalance; the resulting hiring-and-training programmes have produced measurable per-location and per-service-line staffing-improvements, with the per-employee productivity also improving as the data-driven matching of staffing to demand has reduced the operational pressure on the over-staffed locations.
An unexpected outcome: the consolidated data lake has become the foundation for the provider's expanding automation portfolio. The downstream automation initiatives (the revenue-cycle automation, the appointment-scheduling automation, the patient-access automation) all leverage the consolidated data infrastructure that this platform established.
“Our forty-seven per cent year-over-year growth had outpaced our data infrastructure. MindMap consolidated three AWS warehouses into a single source of truth, migrated our EMR onto the consolidated platform and gave our executives the real-time operational visibility we had been lacking. Customer satisfaction up ninety-seven per cent, fraud detection up seventy-eight per cent, manual work down eighty-nine per cent.”— Chief Information Officer· US Healthcare Provider
Why MindMap was chosen
The provider had evaluated two AWS-specialist data-platform vendors. Both had strong AWS-infrastructure capabilities but limited healthcare-specific analytics-and-fraud-detection capability, which was the structural requirement beyond the infrastructure build.
MindMap's accelerator-composition approach — bringing data-lake architecture together with FP&A Insights, Anomaly Detector and Real-Time Visualiser around the healthcare-operations domain — was the structural differentiator. We could demonstrate the healthcare-specific analytics-and-fraud-detection capability working at a comparable US healthcare provider during the bid.
Our embedded healthcare-analytics expertise on the delivery team (two former healthcare-provider analytics leads and a former healthcare-fraud-compliance specialist) was the third factor. The provider's CIO felt that the team understood the operational reality of healthcare data — the per-state regulatory variations, the per-service-type clinical-coding nuances, the per-payer billing-and-fraud patterns.
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