Data Extraction & Warehousing - Thomas Jefferson University Hospital

TJU with logo

Background: Thomas Jefferson University Hospital

Jefferson University Hospitals and Thomas Jefferson University (TJU) are partners in providing excellent clinical and compassionate care for its patients in the Philadelphia region, educating the health professionals of tomorrow in a variety of disciplines, and discovering new knowledge.

With 30,000+ people reimagining health care, education, and discovery, Jefferson continues to top the list of hospitals in Pennsylvania (3rd), and the Philadelphia metro area (2nd), in the U.S. News & World Report’s annual listing of the best hospitals and specialties.

Challenges

  • TJU Hospitals have multiple locations with different EHRs in use in different locations
  • Each location has its own database with no single consolidated place to get all data
  • One of the biggest challenges was the difficulty in handling patient merging across the different systems used in the different locations

Solution

314e Corporation undertook the project to extract patient data from the various EHR software and merge them in a single data warehouse. A P360 data warehouse was designed to host the data from multiple systems into the Netezza Server. The design was based on a hybrid approach of Dimensional modelling/workflow design with dimension and fact tables. Some of the identity mappings such as EMPI and provider mapping were resolved using JEMPI and identity. Custom algorithms were used to deidentify the PHI data based on the user roles.

The ETL framework for loading the data was designed and several dozen views based on workflows such as admission, lab results, procedure orders, clinical findings, encounter information, billing data, problem list, medical history, and social history, etc. were created for user consumption.

Business Outcomes

The solution provided by the 314e team allowed a unified organization-wide view of patient data – clinical and financial, in one place. This allowed for better patient care and resource savings for the client.

Key Technologies involved

microsoft sql server - key technology data extraction
netezza - key technology data extraction

314e’s Cloud Adoption Services

Infrastructure Migration

Converting existing VM or physical machine into cloud computing instance and containerize workloads

Application Migration
Application Migration

Desktop apps virtualizing via app streaming; Web app migration using APIs and service mesh technologies

Data Migration
Data Migration

Migrating existing data marts and application-specific relational database silos into the cloud

Platforms Supported
Platforms Supported

314e has extensive expertise for migration to any of the big three – AWS, GCP, and Azure platforms

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