Healthcare Data Governance
Healthcare data governance is the formal framework of policies, roles, processes, and standards that ensure health data is accurate, consistent, secure, accessible to authorized users, and used in compliance with regulatory requirements including HIPAA.
What is Healthcare Data Governance?
Data governance in healthcare encompasses five core components: data stewardship (defining who owns each data element and is accountable for its accuracy), data quality standards (how accuracy is measured and maintained, including error rate thresholds and validation rules), data access policies (role-based access control defining who can view, edit, or export what data under what conditions), metadata management (data dictionaries, data lineage tracking from source system to analytics output), and master data management (patient identity matching, provider directory maintenance, facility codes). Healthcare-specific challenges are significant. The US lacks a national patient identifier, meaning record linkage depends on probabilistic matching algorithms — patient matching error rates of 5–10% are common in multi-system environments, creating incorrect record merges or duplicate records that corrupt analytics denominators. Code system standardization across EHR platforms (SNOMED CT, ICD-10, local codes) remains inconsistent. HIPAA's minimum necessary standard governs data access, and FHIR API governance requires consent model definitions for external queries. Data governance failures are the root cause of most healthcare analytics inaccuracy — wrong patient populations, mismatched records, and unmapped code systems render dashboards misleading regardless of the analytics tool applied.
Why It Matters for Healthcare Analytics
Without sound data governance, quality measures have wrong denominators, population health panels contain duplicate or misassigned patients, and cost analytics compare incompatible service categories. No analytics platform can compensate for upstream governance failures — clean data is the prerequisite for meaningful insights.
How Vizier Supports Data Governance
Upload your data from multiple source systems, then ask "Are there duplicate patient records or inconsistent provider assignments in our dataset?" — Vizier surfaces data quality anomalies, flags mismatched identifiers, and documents data lineage so you know exactly what is driving each metric in your analytics.