The Patient Stratification application integrates current cost trends, chronic conditions, as well as social determinant risk models and other sources, to identify the individuals most likely to benefit from proactive care management programs. Users can build and analyze different stratification algorithms based on proven risk models and resource utilization to identify the most important candidates for intervention through complex care management, chronic condition management, readmission prevention or other programs.
- Dynamic stratification algorithm creation and saving for targeting individuals for a variety of care programs
- Analyze cohort or patient/member-specific attributes
- Supports complex attribution to the individual’s PCP via integration with Attribution Modeler or incorporation of client-provided logic.
- Complex patient filtering capabilities to create precise cohort registries
- Automates patient list management with the care management staff for improved intake processing
- Expand ability to identify the most critical candidates for Care Management programs
- Ability to tailor criteria to meet unique program requests
- Reduce resource demands to identify individuals for Care Management programs
- Increased timeliness of identifying individuals for Care Management programs
- Closed-loop capabilities to determine the most impactful algorithms for outcomes improvement
Included stratification variables:
- Utilization (ED visits, admits, ICU stays, costs)
- Risk (Charlson-Deyo, Readmission (LACE), HHS-HCC, Predicted, and Rising Risk)
- Conditions (High, Moderate, Low Acuity)
- Medications (Current number and High Risk Medications as defined by CMS)
- Social determinants
- Acute & Ambulatory EMR – billing, longitudinal active medication list and ADT data
- Inpatient & Outpatient Claims – Commercial, Medicare, Medicaid
- Pharmacy claims
- Catalyst EDW component dependencies: CAP 3.1 or above, Attribution modeler OR client mechanism to attribute patients to physicians, Risk SAM
Health organizations too often lack visibility into complete, longitudinal patient or member data that will help them manage their populations. Those that do attempt to identify high risk individuals spend enormous time and valuable resources, often looking at fragmented source data that doesn’t provide a holistic view of individual risk. Some of the key data elements which are often missing include: identification of individuals with the highest risk for care using multiple models, comparing in-network and out-of-network claims, grouping or analyzing care programs, encounter details, diagnosis codes, providers, age cohorts, and zip codes.
What types of problems does Patient Stratification address?
Health organizations too often lack visibility into complete, longitudinal patient data that will help them manage their populations. Those that do attempt to identify high risk individuals spend enormous time and valuable resources, often looking at fragmented source data that doesn’t provide a holistic view of individual risk. Some of the key but often missing elements include: identification of patients with the highest risk of requiring high cost care using multiple models, comparing in and out-of-network claims, grouping or analyzing by: care programs, encounter details, diagnosis codes, providers, age cohorts, and zip codes.
Care Manager wants to identify:
- The top 5% of individuals who are high risk, high cost, on multiple medications and with high acuity conditions to refer to a complex care management program.
- Individuals for chronic care management programs.
- Individuals to refer to readmission reduction programs.
Success Measures Examples
- Identify the most critical candidates (“high risk, high cost”, etc) for Care Management programs