Greenfield Patient 360 Analytics Platform: From Planning Uncertainty to Funded Execution in 4 Weeks
A US healthcare provider's greenfield Patient 360 analytics platform program was stalled in planning uncertainty. AI Forward Engineering analyzed source data, defined target-state designs, and generated ETL script samples in 4 weeks rather than 4 to 6 months. The program moved from indefinite planning to funded execution with complete target-state designs, dimensional models, and auto-generated ETL ready for the engineering team to extend.
Quick facts
| Industry | Healthcare |
|---|---|
| Engagement type | Greenfield platform design and planning |
| Source data | Multi-source clinical and operational systems |
| Target platform | Greenfield analytics platform |
| Engagement scope | Forward engineering, dimensional model generation, ETL script samples |
| Timeline | 4 business weeks |
| Deliverables | Target-state architecture, dimensional model, ETL scripts, project plan |
| Comparable manual effort | 4 to 6 months |
| Accelerators used | Forward Engineer, Metadata Intelligence |
Challenge
A US healthcare provider initiated a Patient 360 analytics program intended to unify clinical, operational, and patient engagement data into a single analytical layer. The program required a complete greenfield platform build. Stakeholders aligned on the business goals but planning stalled.
The data engineering team could not produce a credible target-state architecture without first analyzing the source systems, mapping data feeds, capturing KPIs, and translating business objectives into a dimensional model. Each step depended on the previous. The traditional path was 4 to 6 months of source profiling, requirements workshops, dimensional modeling, and ETL design before a single line of production code could be written.
Leadership needed a fact-based plan to secure program funding. The data engineering team was being asked to commit to a timeline and budget without the underlying analysis to support either.
Approach
3XDE deployed the Forward Engineer and Metadata Intelligence accelerators in a 4-week structured engagement.
- Source data analysis connecting to clinical and operational systems under read-only access, with schema extraction, sampling, and relationship inference
- KPI and business objective capture with clinical and operational stakeholders to translate Patient 360 use cases into measurable analytics
- Target-state architecture design with medallion layering, semantic layer, and workspace segmentation based on the source profile and KPI requirements
- Dimensional model generation producing target conformed dimensions, fact tables, grain decisions, and slowly changing dimension patterns
- Auto-generated ETL samples for the priority data domains, ready for engineering review and refinement
Implementation
Week 1
Source system discovery. Data feed analysis. Sample data profiling. Read-only connections to clinical, claims, pharmacy, lab, and patient engagement source systems.
Week 2
KPI capture workshops with clinical and operational stakeholders. Business objective mapping. Initial target-state architecture sketches.
Week 3
Dimensional model generation. Target lakehouse design with medallion architecture. Semantic layer definition. Workspace segmentation aligned to clinical, operational, and analytical domains.
Week 4
Auto-generated ETL samples for the priority Patient 360 data domains. Detailed project plan with WBS, effort estimates by phase, skills matrix, and dependency map. Funding proposal supported by the analysis.
Results
- Greenfield Patient 360 platform design completed in 4 weeks against the 4 to 6 month manual estimate
- Target-state lakehouse architecture with full medallion layering
- Complete dimensional model for priority Patient 360 KPIs
- Auto-generated ETL scripts for priority data domains
- Funded, execution-ready program approved by leadership
- Project plan with fact-based effort estimates supporting downstream resource planning
- Compressed planning timeline by approximately 12 to 20 weeks
What this means for you
This pattern applies to any greenfield analytics platform program where planning is stalled by the volume of upstream analysis required. The 4-week engagement produces a funded, execution-ready program. Stakeholders get a plan grounded in source analysis. Engineering teams get target designs and ETL samples they can extend.