The challenge
Healthcare data is valuable. Confidence in it cannot be assumed.
Healthcare organizations depend on increasingly diverse data sources, including claims, enrollment, provider, clinical, pharmacy, lab, and financial data. Each source can contain missing fields, inconsistent formats, nonstandard coding, unexpected values, or relationships that only become problematic when data is combined for analysis.
The solution
Make data confidence part of the analytics lifecycle.
The MedInsight Data Confidence Model evaluates healthcare data throughout acquisition, loading, processing, enrichment, and delivery. Embedded within the MedInsight Health Cloud, it combines automated checks, comprehensive audits, reconciliation, peer review, and expert oversight to create a dependable foundation for analytics and decision-making.
How does the MedInsight Data Confidence Model improve healthcare analytics?
The MedInsight Data Confidence Model uses a comprehensive audit process to identify gaps, evaluate relationships between files, and strengthen the reliability of downstream analytics.
The result: More reliable reporting, stronger analytics, less avoidable rework, and greater confidence in the decisions that follow.
Improving data confidence at every level
Validate files, fields, and schemas
Evaluate incoming files for expected structure, valid values, population rates, completeness, and client-specific requirements before they flow into analytics.
Assess relationships across data sources
Confirm that claims, enrollment, providers, members, and other sources connect as expected and identify items that do not align across files.
Detect meaningful changes over time
Compare data submissions and key metrics longitudinally to identify changes that may not be visible within a single file or field.
Validate analytic methodologies and outputs
Check whether analytic engines and enriched measures execute as expected, including risk scores, episodes, evidence-based measures, groupers, and benchmarks.
Reconcile financial and operational results
Where appropriate, compare consolidated results with general ledger totals, payer performance reports, settlement reports, source data, and refresh outputs.
Add expert review and transparent communication
Combine automated controls with peer review, dedicated support, and communication among data suppliers, decision-support teams, and analytic users.
What makes the MedInsight Data Confidence Model different?
Data checked in context
Evaluate whether individual fields, related files, longitudinal patterns, and analytic outputs make sense together.
Quality monitored continuously
Assess data during intake, loading, processing, enrichment, refreshes, and reporting.
Confidence for every stakeholder
Give technical, clinical, financial, and business teams a shared foundation for interpreting results.
Relevant resources
Claims Data Aggregation, Integration, and Management
Claims data aggregation, integration, and management is the process of collecting, standardizing, and organizing claims data from multiple sources into a reliable foundation for analysis. With the right data quality processes in place, that foundation improves reporting, identifies care gaps, reduces risk and cost, and supports value-based care. Talk to a MedInsight data expert See … Claims Data Aggregation, Integration, and Management
Enhancing data integrity with MedInsight’s Data Confidence Model
In this video, MedInsight Vice President of Client Success Derrick Johnson describes how MedInsight’s Data Confidence Model works to ensure high-quality, credible data – with rigorous intake processes, careful aggregation of disparate sources, a thorough system of audits and referential integrity checks to ensure accuracy and identify gaps, and ongoing client support.
