Population Health Model
The tables in the Population Health Model represent complex transformations of the HIEBus and Unified models into enriched, higher-order concepts that are useful for population health-related analyses. These transformations include concepts such as condition profiles, medication therapies, certified measures, and risk factors. Several complex algorithm-based computation subsystems are implemented within the high performance data pipeline to perform these transformations, including:
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Condition Profiles and Risk Score computes a real-time implementation of the CMS HCC RAPS Score and HCUP CCSR. Condition profiles based on these algorithms can be used to create disease registries, sub-populations with multiple chronic conditions, utilization breakdowns by chronic conditions, and to identify persons with chronic conditions that have not been documented recently.
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Galileo Encounter Reconciliation computes primarycaresetting columns for claim-sourced and EHR-sourced data, annotates tables in the Unified Model, and then creates a comprehensive, reconciled set of derived encounters to represent hospitalizations and ED visits. Derived encounters readily enable accurate utilization trends and utilization-based risk profiles using all available data sources.
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Galileo Risk Stratification Framework (GRSF) computes historical risk factors in a vectorized model that can be used to relate current risk profiles to prospective utilization that is targeted by care management programs. GRSF performs whole-population risk stratification and assigns risk levels based on real-time risk factors detected from claim-sourced and EHR-sourced data for a population.
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Galileo Measures Engine computes certified measures from claim-sourced and EHR-sourced data in order to report on up-to-date population measure performance and to identify real-time care gap opportunities.
