Enact Business Solutions2025Private
Cardiac EDA Processing
Cardiac ETL workflow for Oracle exports, labs, and curated patient-level datasets.
- Role
- Data Engineer
- Work track
- Enact Business Solutions
- Category
- Data Engineering
Context and problem
Cardiac data needed consistent identifiers, timelines, and lab structures before it could be trusted for analysis.
Execution and contribution
Processed Oracle exports and multi-sheet clinical workbooks with Python, Pandas, and OpenPyXL validation routines.
Outcomes and proof
Recorded results and supplied evidence from the work.
- 01
- Oracle exports
- 02
- Multi-sheet lab parsing
- 03
- Repeatable QC
- Standardized cardiac patient records.
- Prepared repeatable data refresh outputs.
- Added QC checks for missingness, duplicates, and date consistency.
- Private clinical data integration with repeatable quality enforcement and structured output delivery.
Available evidence
- ETL transformation workflow
- Clinical data quality control checkpoints
Gallery and evidence
Healthcare Data Flow and supplied project material.
Technical record
Stack
- Python
- Pandas
- OpenPyXL
- Oracle exports
Focus
- Cardiac
- Healthcare ETL
- Clinical Data
Confidentiality
Private healthcare workflow. Sensitive records are excluded.Related work
More records from the same work track or technical category.
