Data Quality And Validation Services
Ensure accurate, reliable, and trusted data.
Our data quality and validation services ensure clean, consistent, and trustworthy data.
Comprehensive Data Quality Framework
Our proven methodology ensures healthcare data meets the highest standards for accuracy, completeness, and regulatory compliance.
Data Profiling & Discovery
Comprehensive analysis of data sources to identify quality issues, patterns, and improvement opportunities.
Validation Rules Engine
Automated validation with customizable business rules, reference data checks, and real-time monitoring.
Automated Remediation
Intelligent workflows for issue resolution with approval processes and audit trails for compliance.
Why Data Quality Matters in Healthcare
- Patient safety depends on accurate, complete medical records
- Regulatory compliance requires validated data for audits and reporting
- Clinical decision support systems need reliable data for recommendations
- Financial accuracy for billing, claims, and revenue cycle management
Data Domains and Sources
Comprehensive coverage across clinical, operational, financial, device, and laboratory data domains with specialized validation for each source type.
Clinical Data
Patient records, diagnoses, treatments, and clinical observations
Data Sources
Key Challenges
- Data standardization
- Interoperability
- Real-time updates
- Clinical coding
Operational Data
Scheduling, resource management, and workflow information
Data Sources
Key Challenges
- Resource allocation
- Schedule conflicts
- Capacity planning
- Workflow optimization
Financial Data
Billing, claims, revenue cycle, and financial reporting
Data Sources
Key Challenges
- Claims accuracy
- Revenue recognition
- Compliance reporting
- Cost allocation
Device & Laboratory
Medical device data, lab results, and diagnostic information
Data Sources
Key Challenges
- Device integration
- Result validation
- Quality control
- Reference ranges
Data Integration Architecture
Data Ingestion
Real-time and batch data ingestion from multiple healthcare systems with format standardization.
Quality Validation
Multi-layer validation with business rules, reference data checks, and anomaly detection.
Data Distribution
Validated data distribution to downstream systems with audit trails and lineage tracking.
Quality Dimensions Framework
Comprehensive assessment across six critical dimensions: completeness, accuracy, timeliness, consistency, uniqueness, and validity.
Completeness
95%+Ensuring all required data fields are populated
Key Metrics
- Field completion rate
- Record completeness
- Mandatory field validation
Accuracy
99%+Data correctly represents real-world values
Key Metrics
- Data validation rules
- Cross-reference checks
- Source verification
Timeliness
< 15 minData is available when needed and up-to-date
Key Metrics
- Data freshness
- Update frequency
- Latency measurements
Consistency
98%+Data is uniform across systems and time
Key Metrics
- Format standardization
- Value consistency
- Cross-system alignment
Uniqueness
99.5%+No duplicate or redundant data records
Key Metrics
- Duplicate detection
- Record matching
- Deduplication rate
Validity
99%+Data conforms to defined formats and rules
Key Metrics
- Format validation
- Range checks
- Business rule compliance
Quality Measurement Dashboard
Real-time Quality Scores
Quality Trends
Completeness Trend
↗ 2.3% improvement over last month
Accuracy Trend
↗ 1.8% improvement over last month
Timeliness Trend
→ Stable performance maintained
Consistency Trend
↗ 3.1% improvement over last month
Quality Improvement Results
Rule Design and Catalog
Comprehensive validation framework with business rules, reference data validation, and healthcare code sets for accurate data quality assessment.
Business Rules
450+Healthcare-specific validation rules based on clinical and operational requirements
Example Rules
- Patient age must be between 0-150 years
- Discharge date cannot be before admission date
- Medication dosage within therapeutic ranges
- Required fields for billing compliance
Reference Data
200+Validation against standard healthcare code sets and terminologies
Example Rules
- ICD-10 diagnosis code validation
- CPT procedure code verification
- Drug code validation (NDC, RxNorm)
- Provider NPI number verification
Code Sets
150+Standardized medical coding and classification systems
Example Rules
- SNOMED CT clinical terminology
- LOINC laboratory codes
- HL7 FHIR value sets
- Custom organizational codes
Data Relationships
300+Cross-field and cross-table validation rules
Example Rules
- Patient demographics consistency
- Clinical order and result matching
- Insurance eligibility verification
- Provider-patient relationship validation
Rule Management Lifecycle
Define
Identify business requirements and create validation rules
Implement
Configure rules in validation engine with proper parameters
Test
Validate rules against sample data and edge cases
Deploy
Activate rules in production with monitoring
Maintain
Regular review and updates based on feedback
Rule Complexity Levels
Rule Performance Metrics
Execution Speed
Average: 50ms per rule evaluation
Accuracy Rate
99.7% correct validation results
Coverage
95% of data elements validated
Maintenance
Monthly rule updates and reviews
Rule Catalog Benefits
Centralized rule management ensures consistency, reduces duplication, and enables rapid deployment of new validation requirements.
Validation Pipelines
Multi-stage validation process with ingestion checks, transformations, reconciliation, and exception queues for comprehensive data quality assurance.
Ingestion Checks
Real-timeInitial validation at data entry points
Validation Types
- Schema validation
- Format verification
- Mandatory field checks
- Data type validation
Transformations
Near real-timeData cleansing and standardization processes
Validation Types
- Data normalization
- Format standardization
- Code mapping
- Value enrichment
Reconciliation
BatchCross-system data consistency verification
Validation Types
- Source system comparison
- Duplicate detection
- Relationship validation
- Business rule checks
Exception Queues
On-demandFailed validation handling and resolution
Validation Types
- Error categorization
- Priority assignment
- Workflow routing
- Resolution tracking
Pipeline Architecture Flow
Data Sources
Multiple healthcare systems feeding data into validation pipelines.
Validation Engine
Rule-based validation with configurable business logic and checks.
Quality Routing
Intelligent routing of validated data and exception handling.
Data Distribution
Clean, validated data delivered to downstream systems and analytics.
Pipeline Performance Metrics
Exception Management
Critical Errors
Immediate escalation and blocking
Warning Issues
Flagged for review, data flows
Information Alerts
Logged for analysis and trends
Auto-Remediation
Automated fixes for known issues
Pipeline Efficiency Results
Ready to Get Started?
Contact our team to learn how our Data Quality and Validation Services can support your needs and improve your efficiency.
Call us now: +1 (951) 622-8126