Idea
A scalable data pipeline that standardizes multi-institutional critical care EHR data for researchers and healthcare AI developers.
Research Paper
Core Innovation
This paper introduces CRISP, a pipeline that uniquely integrates data quality management, vocabulary mapping to SNOMED-CT, deduplication, and unit standardization in a modular, parallelizable framework. Unlike prior tools, CRISP handles billions of records efficiently across institutions, enabling rapid creation of machine learning-ready datasets. It also provides baseline clinical prediction models to support downstream AI research.
Market Size (TAM)
$2–10B TAM, $1–2B SAM; assumption: growing demand for interoperable EHR data platforms and AI-ready clinical datasets in healthcare and research sectors.
Potential Customers & Pain Points
- Hospitals needing unified EHR data processing
- AI researchers requiring clean harmonized datasets
- Health equity analysts lacking standardized multi-source data
Business Model
Subscription-based SaaS platform with tiered pricing for data volume and feature access; enterprise licensing for large institutions; consulting for custom integrations.
Competitive Landscape
- OHDSI
- TriNetX
- Cerner HealtheIntent
Implementation Challenges
- Integration with diverse institutional data systems
- Ensuring data privacy and compliance
- Adoption by healthcare organizations with legacy systems
Validation Strategy
- Pilot deployment with partner hospitals to process real-world EHR data
- Benchmark data quality and processing speed against existing pipelines
- Demonstrate improved AI model performance using CRISP-processed datasets
Research Paper Overview
The CRITICAL Records Integrated Standardization Pipeline (CRISP): End-to-End Processing of Large-scale Multi-institutional OMOP CDM Data
Summary
CRISP is a data processing pipeline designed to transform large-scale, multi-institutional critical care EHR data into machine learning-ready datasets. It addresses challenges of heterogeneous data harmonization by providing transparent data quality management, unified vocabulary mapping to SNOMED-CT, deduplication, unit standardization, and modular parallel processing. CRISP enables rapid processing of billions of records and offers baseline clinical prediction models to accelerate AI research and health equity studies.