Data Cleaning
Process of identifying and correcting errors in healthcare datasets
Overview
Data cleaning prepares healthcare data for analysis by fixing errors, removing inconsistencies, and standardizing formats. This critical step ensures that AI models and analytics systems work with reliable, high-quality data.
Common Data Quality Issues
- Missing or null values
- Outliers and anomalies
- Inconsistent formatting
- Duplicate records
- Noise and errors in measurements
- Structural inconsistencies
Error Sources
Problems may arise from:
- Manual Entry
- Typing mistakes
- Format variations
- Incomplete records
- System Issues
- Integration errors
- Format mismatches
- Data corruption
Cleaning Process
Assessment Steps
- Profile the dataset
- Identify error patterns
- Document issues
- Plan corrections
- Validate changes
Correction Methods
Handle issues through:
- Standardization rules
- Format conversion
- Value validation
- Deduplication
- Missing data handling
Healthcare Considerations
Clinical Data
Special attention to:
- Patient Information (PHI)
- Demographic data
- Medical history
- Treatment records
- Clinical Values
- Lab results
- Vital signs
- Medication doses
Quality Requirements
Essential checks for:
- Diagnostic accuracy
- Treatment consistency
- Dosage validation
- Timeline verification
- Code standardization