BayesIQ Data Reliability Audit
Audit Period: December 2025
Good — some issues need attention
BayesIQ audits a healthcare organization's patient data infrastructure across readmission tracking, patient identity matching, and outcome reporting. The audit identifies 3 material discrepancies — including 50 near-duplicate patient records inflating aggregations and null patient IDs breaking downstream filters — directly affecting board-reported readmission metrics. After targeted remediation, the reliability score improves from 31 to 81 and the readmission rate discrepancy narrows from 5.7% to within acceptable tolerance.
Key metrics: reported vs audited
| Metric | Period | Reported | Audited | Delta |
|---|---|---|---|---|
| readmission_rate | 2025-12 | 13.0% | 12.3% | overstated 5.7% |
Top findings
- High50 rows affected
Near-duplicate rows detected
50 duplicate patient records inflating readmission counts, distorting quality metrics reported to CMS
- High25 rows affected
Null values in required column: patient_id
25 records missing patient_id — these patients are invisible to outcome tracking and compliance reporting
Recommended actions
- 1
Investigate duplicate records. Add dedup logic keyed on non-key fields.
Owner: Data Engineering · Effort: Medium
- 2
Fix null values in required column 'patient_id' at the source.
Owner: Data Engineering · Effort: Medium
- 3
Investigate root cause of readmission_rate discrepancy for 2025-12. Check for duplicate events, missing data, or filter logic differences.
Owner: Data Engineering · Effort: Small
Prepared by BayesIQ · 2025-12