Data Quality and KPI Integrity
This lesson builds practical understanding of healthcare KPI systems using a high-reliability, quality-management, and performance-improvement lens.
Learning outcomes
- Explain why data quality is foundational to KPI credibility.
- Identify common data quality risks.
- Strengthen trust through governance and validation.
Why trusted data matters
Even beautifully designed dashboards fail if people do not trust the underlying data. KPI integrity depends on completeness, consistency, accuracy, timeliness, and governance.
Common data quality issues
Frequent issues include inconsistent documentation, missing fields, duplicate records, coding variation, delayed feeds, and shifting definitions.
Governance and stewardship
Strong organizations maintain a KPI dictionary, change control process, and regular validation checks.
Practical validation steps
Validation can include source-to-report reconciliation, spot audits, outlier review, and cross-functional review.
Knowledge check
A dashboard shows a sudden fall in infection rates, but a coding workflow change excluded a subset of cases. Answer: Validate the data first.