Predictive ModelingPredictive modeling has emerged as an important tool for at-risk providers managing population and patient health. Although the needs for predictive modeling and profiling can vary for different organizations based on their contract models and business goals, providers often need predictive modeling capabilities to: - Understand population risk to inform contract negotiations
- Perform patient risk and likelihood of hospitalization stratification
- Identify high-risk patients for disease management or other intensive health management programs
- Identify patients at higher risk for hospital admissions or emergency room use as candidates for focused intervention and prevention programs
The MedVentive SolutionMedVentive provides patient risk stratification using both concurrent and prospective risk scores. We use models appropriate for each population: commercial, Medicare and Medicaid. Using MedVentive Risk Manager's intuitive point-and-click reporting interface, users easily produce reports for the desired population segment, with the ability to filter by multiple parameters and provide focused reports for predictive modeling. For predictive modeling and risk-adjustment, MedVentive uses DCG models licensed from DxCG (Verisk). We wrap enhanced proprietary functionality around these rules-based (statistical) models to make them more relevant and usable. This makes it easy for provider organizations to use predictive modeling and realize its value by focusing their resources where they are needed most. MedVentive’s new Likelihood of Hospitalization Predictive Modeling Report tool allows clients to stratify their populations with respect to risk for hospitalization. An aggregate Account Planning Report displays statistical and demographic information on members who are likely to be hospitalized. The Member Level Report displays member-level information for those who are most likely to be hospitalized based on their LoH predictive scores, together with the key drivers of the risk for hospitalization for each patient. Learn More
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