The test of an EHR warehouse is simple. Does the number match what the EHR itself reports, and does it
The refresh window governs everything
History that stops at go-live
Conversion carried a subset. The legacy system holds the rest and is still licensed only for retention.
Multiple instances after every merger
Upgrades that change meaning without breaking anything
Reporting logic that became institutional memory
Production load from analytics
EHR contracts vary on what data may be extracted, where it may be stored, which surfaces may be used at volume, and what happens to that data if you leave. It is far cheaper to answer this before the platform is built.
Step 1
Confirm what you may extract
Step 2
Choose the surface per data type
You get the right latency and fidelity per domain.
Step 3
Profile the actual data
Step 4
Build incremental extraction
Step 5
Land raw with fidelity preserved
Step 6
Reuse the vendor model where it is sound
Step 7
Resolve identity across instances and history
Step 8
Define and certify the measures
Step 9
Migrate and retire
Move consumers off direct-source queries and switch the old assets off.
Step 10
Operate through the upgrade cycle
| Capability | Query the reporting DB | Vendor analytics module | copy everything to a lake | Governed warehouse and lakehouse |
|---|---|---|---|---|
| Speed to a simple answer | Fast | Fast | Slow | Fast within a live domain |
| Combines multiple instances | No | Limited | Possible | Yes |
| Carries retired system history | No | No | If loaded | Yes, by design |
| Non-EHR data alongside | No | No | Yes | Yes |
| Survives a vendor upgrade | No | Yes | No | Yes, regression tested |
| Load on production | High | Low | Moderate | Low |
| Reconciles to source | By definition | Yes | Rarely tested | Tested and reported |
Do not recreate your existing reporting estate on newer technology
Modernize the model, the definitions, the governance and the operating model at the same time, or you have bought a faster version of the problem. And do not leave the vendor analytics layer too early either.
Most provider organizations need both: dimensional modeling for structured reporting and a lakehouse for notes, high-volume observations, event data and ML, sharing one identity spine, one definitions registry and one governance model.
Extract and Land
Multi-Surface Extraction
Reporting databases, vendor dimensional models, vendor and FHIR APIs including bulk export, HL7 feeds, and file-based extracts selected per data type.
Incremental and Change-Based Loading
Historical Backload and Legacy Archive
Reconciliation to Source
Model and Serve
Layered Modeling
Vendor Model Extension
Longitudinal Patient Record
Unstructured and ML-Ready Data
Operate and Govern
Upgrade Regression Testing
Freshness and Load Monitoring
Published refresh commitments, alerting and visible staleness.
Lineage and Certification
Trace published measures back to source table and extraction run.
Cost and Workload Management
Storage tiering, refresh cadence, materialization and workload isolation designed for cost.
We are not platform resellers and we are not aligned to one EHR vendor or one cloud. We assess what your existing EHR analytics layer already does well before proposing anything beside it, build on the cloud platform you have already committed to, and keep identity, definitions, lineage and access control out of platform-specific implementations.
| Domain | What it unlocks | Where teams lose time |
|---|---|---|
| Encounters and visits | Volume, throughput, access, and the denominator under most other measures | Encounter definition varies by setting, and cancelled, no-show, converted and merged encounters are counted differently by every downstream consumer |
| Registration and scheduling | Access, template utilization, no-show, waitlist, referral conversion | Appointment status changes over its life. Point in time reconstruction requires history the reporting layer may not retain |
| ADT and census | Occupancy, patient flow, length of stay, transfers, capacity | Transfers and bed swaps produce event sequences that only reconstruct correctly if processed in order |
| Orders and results | Turnaround, utilization, care gaps, quality measures, ML features | Results are amended, corrected and superseded. Taking the latest without handling the amendment chain silently misstates history |
| Medications | Prescribing patterns, adherence proxies, stewardship, safety analytics | Ordered, dispensed and administered are three different facts and are frequently conflated into one |
| Problems and diagnoses | Risk, quality, cohorting, population analysis | Problem list versus encounter diagnosis versus billing diagnosis rarely agree, and each is right for a different question |
| Clinical documentation | Text analysis, abstraction support, AI features, chart review | Notes are versioned, addended and sometimes retracted. Volume and access control both need deliberate design |
| Flowsheets and observations | Vitals, assessments, device data, deterioration models | Extremely high volume with sparse, template-dependent structure. The most common cause of a cost overrun |
| Charges and professional billing | Revenue integrity, charge lag, missing charge analysis | Charges move after posting. A snapshot taken before the close reports a different world than one taken after |
| Claims, denials and payment | Denial performance, yield, AR, payer behavior | The claim rarely matches the encounter one to one, and reconciling them is a modeling decision that has to be made explicitly |
| HIM, coding and abstraction | Coding accuracy, DNFB, case mix, quality abstraction | Coding is finalized on its own timeline. Analysis run before finalization looks like a data quality problem and is not |
| Quality and registry | Regulatory reporting, value based contracts, improvement work | Measure specifications change annually and rarely match the vendor supplied version exactly |
Documentation → coding → charge → claim
Appointment → encounter → procedure → claim
Patient → diagnosis → order → result → outcome
Provider → schedule → encounter → procedure
Encounter → quality measure → outcome
Every extraction surface has a purpose and a limit. Using several appropriately is better than forcing everything through one.
| Surface | Best for | Limits to design around |
|---|---|---|
| Vendor reporting database | Broad historical coverage, detail the models do not carry, bespoke analysis | Large partly documented schema, refresh-window bound, schema changes on vendor calendar |
| Vendor dimensional model | Standard operational and financial reporting, already conformed and reconciled | Vendor-defined semantics, gaps for local build, less useful for ML |
| FHIR and vendor APIs | Standardized clinical elements, application integration, near-current reads | Resource coverage varies, throughput limits, not designed for bulk historical extraction |
| Bulk FHIR export | Population-scale standardized extraction and external exchange obligations | Batch oriented; scope and performance vary by implementation |
| HL7 interface feeds | Event-driven and near-real-time ADT, orders, results and scheduling | Message-level rather than record-level; state reconstruction requires ordered processing |
| File and database extracts | Legacy systems, departmental apps and anything without a modern surface | Manual dependency, fragile ownership, usually first to break |
Warehouse and lakehouse, not one or the other
Extraction and modeling separated by a raw layer
Multi-instance resolved deliberately
Legacy archive is a separate architecture
Common models where they earn their place
Platform note.
We build on Microsoft Fabric, Azure, Databricks, Snowflake, AWS and Google Cloud, and work across the major EHR and practice management platforms in use across acute, ambulatory and specialty settings. Platform selection follows your existing enterprise commitment and workload profile rather than our preference.
When the same measure is produced from the warehouse and from the EHR, the numbers must match or the difference must be understood and documented.
Continuous reconciliation to source
Certified measures compared against EHR equivalents on a schedule, with variance thresholds and steward alerting.
Upgrade regression testing
Schema, row count and certified-measure comparison run in non-production against every vendor upgrade.
Amendment and correction propagation
Amended results, corrected documentation, retracted notes and reversed charges must flow through..
Chart correction and merge propagation
Merges, unmerges and corrections in the EHR must be reflected in the warehouse.
Distribution and volume monitoring
Volume and distribution shifts reveal silent upstream changes early.
Point-in-time integrity
Models support asking what was true on a given date, not only what is true now.
| Metric | What it means | How it is treated |
|---|---|---|
| Duplicate rate | One person existing as multiple records | A quality target, worked down systematically |
| Overlay rate | Two different people merged into one record | A safety metric with a zero tolerance target and mandatory root cause review |
| Cross-instance linkage | Share of patients successfully linked across instances and legacy systems | Determines what longitudinal analysis can be trusted |
| Unresolved match queue | Records the matching process could not confidently link | A stewardship queue with a service level, never auto-resolved |
Certification tiers
Definitions owned in the domain
Definitions versioned with effective dates
Each product declares its EHR context
Included instances, period covered, build era and source-change behavior are explicit.
Vendor-supplied metrics documented
Impact analysis before change
Inside the EHR, a decade of access control, break-glass rules, VIP handling and sensitive record segmentation governs who sees what. None of that travels with an extract. The warehouse starts with no controls at all and inherits only what you deliberately rebuild, which is why access design belongs at the beginning of the project rather than before go-live.
Access & minimum necessary
- Row and column level security enforced in the platform
- EHR access model reviewed and deliberately reconstructed
- De-identified and limited-data provisioning as a standard path
- Complete access logging
Sensitive & restricted records
- Segmentation for substance use disorder, behavioral health, HIV, genetic, reproductive health and minors' records
- Employee and VIP records handled under break-glass-equivalent controls
- Patient-requested restrictions carried through
- Segmentation designed at ingestion
Records & retention
- The warehouse is an analytical copy, not the legal medical record
- Retention and disposition aligned to legal-record definition and state requirements
- Legacy archive scoped separately for retrieval, legal hold and analytics
- Environment separation with controlled production-data use
Platform & contractual
- BAA with cloud and platform providers
- Encryption, key management and fixed data residency where required
- EHR licensing position confirmed on extraction, storage, use and exit rights
- AI access governed by permissions, minimum necessary, retention, training use and traceability
RReconstructing an equivalent control model is a design activity with a cost, and it should be in the plan from the first domain.
Platform programs are usually reported on inputs: sources connected, tables created, volume loaded. None of those tell you whether the numbers are trusted, whether production is under less strain, or whether anybody got an answer faster.
| Category | What we measure | Why it matters |
|---|---|---|
| Reconciliation | Variance to the EHR equivalent by certified measure, and the share of measures reconciling within threshold | The single measure that determines whether the warehouse is trusted |
| Production relief | Reporting queries removed from the EHR environment, and load reduction during clinical hours | A concrete win the EHR team will support, and often the easiest business case |
| Time to answer | Time to answer a new question within a live domain, analyst time assembling versus analyzing | The reason the platform exists |
| Upgrade resilience | Reports broken per upgrade, breakage found in test versus in production | The recurring cost most programs never measure |
| Identity quality | Duplicate rate, overlay rate, cross-instance linkage, unresolved match queue age | Overlay is reported as a safety metric with a zero target |
| Estate reduction | Direct source queries retired, duplicate extracts removed, legacy systems decommissioned | Whether you replaced something or added a layer |
| Cost | Cost by domain and workload, cost per refresh, trend against volume growth | Consumption platforms succeed technically and then get challenged in budget |
Both are high-volume and structurally awkward. If either is in the first phase, plan the time and cost accordingly rather than discovering it later.
We will trace it to the source tables it depends on, show you why it is fragile, reconcile it against what the EHR itself reports, and tell you what it would take to make it a certified product that survives the next upgrade. That exercise exposes the extraction, modeling, identity and ownership issues the wider programme has to address, and it does it in weeks rather than in a discovery phase.
Bring us a specialty or clinical setting where clinicians are spending too much time creating notes. We will assess where ambient documentation fits, what must remain clinician controlled, how it should integrate with your EHR, and how to measure whether it is actually reducing burden.
- AI Agents and Workflow Automation
- Voice and Conversational AI
- Document AI and Intelligent Processing
- Generative AI and Enterprise Copilots
- AI Strategy and Governance
- HCC and Risk Adjustment Analytics
Security & Compliance
