The data layer your clinical, claims, and AI workloads actually depend on - engineered for HIPAA, FHIR, and the integration depth healthcare requires.

Book an Intro Call Common Questions

200+

Source systems in a typical health system

3 engagements

Ways healthcare orgs engage Logiciel

5 layers

Layers of the healthcare data stack

What we build

The Layer Where Healthcare Data Engineering Starts.

EHR systems

Epic, Cerner/Oracle Health, Meditech, Athenahealth, NextGen, eClinicalWorks, and dozens of specialty-specific EHRs.

Claims and payer systems

internal RCM platforms, payer feeds, clearinghouse data, X12 837/835/834.

Lab, imaging, and ancillary systems

LIS, RIS/PACS, pharmacy, medication management, vital signs telemetry.

Operational systems

scheduling, registration, supply chain, HR, financial systems.

Patient-generated and device data

patient portals, RPM devices, wearables, mobile applications.

Reference and external data

public health, social determinants, payer rate transparency, drug databases.

Trust & security

Moving Healthcare Data Without Losing Fidelity, Identity, or Compliance Posture.

Streaming and batch ingestion - Kafka, Kinesis, change data capture from EHR replicas, FHIR-based event streams.

HL7 v2 and FHIR R4 integration - production-grade HL7 parsing and FHIR ingestion pipelines, not academic prototypes.

Claims integration - X12 parsing, payer feed normalization, denial and remittance reconciliation.

Master Patient Index integration - preserving patient identity across systems, deduplication, identity resolution.

Governance from day one - PHI tagging at ingest, lineage tracking, access logging, encryption in transit and at rest.

Under the hood

The Healthcare Data Models That Make the Layers Above Possible.

Lakehouse on Databricks or Snowflake

most common pattern for mid-to-large health systems with both BI and ML/AI workloads.

Included

Data lake architecture on AWS / Azure / GCP

appropriate for high-volume clinical streaming and unstructured data (notes, imaging metadata).

Included

Data mesh architecture

domain-oriented data ownership patterns increasingly adopted by larger payers and health systems with mature data engineering organizations.

Included

Healthcare-specific data models

OMOP, i2b2, FHIR-based clinical data models, claims canonical models, payer-provider exchange models.

Included

PHI segmentation and de-identification

separate logical layers for identified PHI versus de-identified or limited-data-set environments for research and ML.

Included

Technology

The Layer That Determines Whether Your Data Platform Earns Continued Investment.

Pipeline reliability engineering

SLOs on data freshness, completeness, and quality. Alerting routed to the right on-call. Postmortems.

Technology

Data observability

schema change detection, anomaly detection, lineage-aware impact analysis, freshness monitoring. (See also our Data Observability Solutions page.)

Technology

Data quality framework

testing, data contracts, expected-value monitoring, quality SLAs tied to downstream consumers.

Technology

Cost discipline

compute and storage FinOps applied to healthcare data workloads, which run distinctively cost-sensitive at scale.

Technology

Compliance operations

access logging, PHI audit trails, BAA execution, periodic access review, evidence collection for HIPAA, HITRUST, SOC 2, and state-level audits.

Technology

What the Healthcare Data Platform Feeds.

Operational reporting and BI

Tableau, Power BI, Looker, Sigma; standardized clinical, financial, and operational dashboards.

↳ Technology

AI and ML workloads

feature stores, training datasets, retrieval indexes for generative AI workflows, eval ground truth.

↳ Technology

Operational AI workflows

see our AI Implementation Services for Healthcare page for the workflows the data layer enables.

↳ Technology

External data products

payer-provider data exchange, research datasets, health information exchange (HIE) feeds.

↳ Technology

Regulatory and quality reporting

HEDIS, MIPS, ACO, CMS quality measures, public health reporting.

↳ Technology

In focus

Three Ways Healthcare Organizations Engage Logiciel for Data Engineering.

DE Scoping Call (free, 60 minutes). A senior Logiciel data engineer walks your layers with you. Output: a current-state assessment and a recommended engagement shape - sometimes us, sometimes a vendor, sometimes internal hiring.

Healthcare Data Platform Sprint (12–20 weeks). Stand up or materially upgrade one or more layers - typically integration + storage + operations - against a defined workload mix. The most common starting engagement.

Dedicated Healthcare DE Squad (6+ months). Embedded data engineering team owning ongoing platform evolution. Right model when data engineering is a continuous program, not a project.

Why Logiciel

Why "Generic Data Engineering" Underperforms in Healthcare.

Logiciel's healthcare data engineering practice operates inside these constraints by design.

Identity is harder.

Patient identity resolution across systems is a first-class engineering problem in healthcare. Generic identity-stitching patterns don't survive contact with MPI complexity, duplicate records, and HL7 message-level identity inconsistencies.

Governance is concurrent, not retrofitted.

PHI handling, audit logging, access policy, and BAA structure have to be designed into the platform from layer 1. Generic data engineering practices typically retrofit governance after the platform is built - and most of those retrofits are partial.

The consumption layer has higher consequences.

A wrong dashboard in retail is embarrassing. A wrong clinical or quality metric is a regulatory or clinical safety event. Data quality and lineage are non-negotiable engineering disciplines, not nice-to-haves.

Selected work

Tailored engineering for your industry.

Zeme · Real Estate Cut development costs 50% and launched 3× faster with dedicated dev teams.

Real Estate

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Leap · Construction Scaled to 7-figure ARR with AI-augmented software teams.

Construction

Scaled to 7-figure ARR with AI-augmented software teams.

KW · Real Estate 56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Real Estate

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Resources

Insights for tech leaders.

Whitepaper · PDF

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Whitepaper · PDF

Building AI-Ready Data Foundations in Healthcare

Framework · PDF

80% Of Engineering Organisations Will Run A Platform Team. The Ticket Queue Survives.

In their words

What our clients say.

Teams that needed to ship fast, and did. Here's what partnering with Logiciel felt like from the inside.

I would highly recommend them to anyone looking to scale quickly or needing support in engineering, product, or QA.

Patrick Fingles

CEO, Leap

We don't just call them Logiciel; they're part of the Zeme team. Within the first week they were contributing meaningfully to our codebase.

Elior Alayev

Founder & CEO, Zeme

The Logiciel team worked tirelessly and built everything we needed, with security and best practices across our entire platform. It allowed us to become #1 in our industry, and we couldn't have done it without them.

David Buzzelli

Co-Founder, JobProgress

Questions

Frequently asked questions.

What are healthcare data engineering services?

Healthcare data engineering services are the engineering engagements that build and operate the data platforms supporting clinical, operational, financial, and AI workloads inside healthcare organizations. The work spans source-system integration (EHR, claims, lab, devices), ingestion pipelines (HL7, FHIR, X12, streaming and batch), storage and modeling (lakehouse, lake, mesh patterns), operations and reliability, and the consumption layer that feeds BI, AI, and external data products.

How is healthcare data engineering different from generic data engineering?

Three structural differences. Patient identity resolution is a first-class engineering problem. Governance (PHI handling, BAAs, audit logging, HIPAA controls) has to be designed into the platform concurrently, not retrofitted. And the consequences of poor data quality in healthcare are clinical and regulatory, not just operational - which changes how data quality, lineage, and reliability disciplines have to be designed.

Which data platforms do you work with?

Logiciel's healthcare data engineering practice works across Databricks, Snowflake, AWS (Redshift, Glue, EMR, Lake Formation), Azure (Synapse, Data Factory, Fabric), GCP (BigQuery, Dataflow, Dataproc), and self-hosted patterns. We typically recommend a platform that matches your workload mix and team shape during the scoping call - we are not single-vendor aligned.

Do you work with FHIR, HL7, and X12?

Yes, all production-grade. Most healthcare data engineering engagements include HL7 v2 parsing, FHIR R4 ingestion, X12 claims integration, or some combination. We treat these as engineering disciplines with real edge cases, not as off-the-shelf adapters.

How do you handle PHI and HIPAA compliance?

PHI tagging, encryption in transit and at rest, access logging, BAA execution, least-privilege IAM, and audit-ready evidence collection are designed into the platform from layer 1. We map the platform's controls to HIPAA, HITRUST, SOC 2, and applicable state requirements and produce the artifacts your compliance team needs for audits.

How long does it take to stand up a healthcare data platform?

A focused platform sprint (one workload mix, one or two layers materially upgraded) typically runs 12–20 weeks. A multi-layer enterprise data platform program runs 6–18 months depending on source-system complexity and team velocity. The DE scoping call produces an indicative timeline for your specific context.

What does a healthcare data engineering engagement cost?

A platform sprint typically runs in the mid-six to low-seven figures depending on the layers in scope and source-system complexity. Dedicated squad engagements run on monthly retainer scaled to the platform size. The scoping call produces indicative pricing - we give real numbers, not "contact sales" responses.

Let's build

The Scoping Call That Walks Your Stack Layer by Layer.

Sixty minutes with a senior healthcare data engineer. We walk your layers, identify the gaps, and produce a recommended engagement shape. If the right answer is us, we'll scope. If it's not, we'll tell you what is.

Book a DE scoping call

Data Engineering Services for Healthcare.

See Logiciel in action.

The Layer Where Healthcare Data Engineering Starts.

EHR systems

Claims and payer systems

Lab, imaging, and ancillary systems

Operational systems

Patient-generated and device data

Reference and external data

Moving Healthcare Data Without Losing Fidelity, Identity, or Compliance Posture.

Streaming and batch ingestion - Kafka, Kinesis, change data capture from EHR replicas, FHIR-based event streams.

HL7 v2 and FHIR R4 integration - production-grade HL7 parsing and FHIR ingestion pipelines, not academic prototypes.

Claims integration - X12 parsing, payer feed normalization, denial and remittance reconciliation.

Master Patient Index integration - preserving patient identity across systems, deduplication, identity resolution.

Governance from day one - PHI tagging at ingest, lineage tracking, access logging, encryption in transit and at rest.

The Healthcare Data Models That Make the Layers Above Possible.

Lakehouse on Databricks or Snowflake

Data lake architecture on AWS / Azure / GCP

Data mesh architecture

Healthcare-specific data models

PHI segmentation and de-identification

The Layer That Determines Whether Your Data Platform Earns Continued Investment.

Pipeline reliability engineering

Data observability

Data quality framework

Cost discipline

Compliance operations

What the Healthcare Data Platform Feeds.

Operational reporting and BI

AI and ML workloads

Operational AI workflows

External data products

Regulatory and quality reporting

Three Ways Healthcare Organizations Engage Logiciel for Data Engineering.

Why "Generic Data Engineering" Underperforms in Healthcare.

Identity is harder.

Governance is concurrent, not retrofitted.

The consumption layer has higher consequences.

Tailored engineering for your industry.

Cut development costs 50% and launched 3× faster with dedicated dev teams.

Scaled to 7-figure ARR with AI-augmented software teams.

56M+ workflows automated, saving agents 30% time with AI-powered tasks.

Insights for tech leaders.

Building AI-Ready Healthcare Data Infrastructure: The 90-Day Roadmap from Staging Bottleneck to Production

Building AI-Ready Data Foundations in Healthcare

80% Of Engineering Organisations Will Run A Platform Team. The Ticket Queue Survives.

What our clients say.

Frequently asked questions.

The Scoping Call That Walks Your Stack Layer by Layer.