Data Quality Failures
Definition
NNDSS data quality issues include incomplete fields (e.g., testing reason in 31% cases, Indigenous status differences 52% vs 90%). Improvements noted with electronic reporting, but rework persists for analysis and national reporting.[1][3]
Key Findings
- Financial Impact: Rework 10-20 hours/week per jurisdiction; 1-3% error rate in key demographics.
- Frequency: Ongoing in fortnightly/annual reports.
- Root Cause: Heterogeneous state reporting systems, patient non-disclosure, manual entry.
Why This Matters
The Pitch: Public Health in Australia 🇦🇺 incurs AUD 50,000+ yearly rework from incomplete NNDSS data. Automation ensures 95%+ completeness.
Affected Stakeholders
Data analysts, Policy makers, WHO reporters
Deep Analysis (Premium)
Financial Impact
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Current Workarounds
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Methodology & Sources
Data collected via OSINT from regulatory filings, industry audits, and verified case studies.
Related Business Risks
Notifiable Disease Reporting Penalties
Surveillance Data Delays
CGRPs Non-Compliance Penalties
Grant Administration Overhead
Delayed Grant Acquittals
Grant Administration Compliance Penalties
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