The Healthcare Analytic Adoption Model outlines 8 levels of analytic maturity for healthcare organizations. Level 5 maturity involves using data-driven improvement to optimize clinical processes and outcomes. Reaching Level 5 requires a robust data governance function to achieve conditions like standardized controlled vocabularies, consistent internal reporting, and an integrated enterprise data warehouse. The model then describes increasingly advanced analytics like predictive risk modeling and precision medicine at Levels 6 through 8.

1. Healthcare Analytic Adoption Model
In the Healthcare Analytic Adoption Model, a robust data governance
function is required in order to achieve the conditions of Level 5 maturity.
Level 8
Level 7
Level 6
Level 5
Level 4
Level 3
Level 2
Level 1
Level 0
Precision Medicine, Big Data
& Prescriptive Analytics
Clinical Risk Intervention
& Predictive Analytics
Population Health Management
& Active Analytics
Data-driven Improvement of
Clinical Process & Outcome
Automated External Reporting
Automated Internal Reporting
Standardized Controlled Vocabulary
& Patient Registries
Enterprise Data Warehouse
Fragmented Point Solutions
• Tailoring patient care based on population outcomes and
genomics data. Treatment and engagement include IoT.
• Organizational processes for intervention are supported
with predictive risk models. Fee-for-quality includes fixed
per capita payment.
• Tailoring patient care based on population metrics. Fee-for-
quality includes bundled per case payment.
• Reducing variability in care processes. Focusing on
internal optimization and waste reduction.
• Efficient, consistent production of reports & adaptability
to changing requirements.
• Efficient, consistent production of reports & dashboards
widely available in the organization.
• Relating and organizing the core data content.
• Collecting and integrating the core data content.
• Inefficient, inconsistent versions of the truth.
Cumbersome internal and external reporting.
HCAD 6635 Health Information Analytics Copyright © 2016 Frank F. Wang 1