ACG-PMs™: Advantages in High-Risk Case Identification
The ACG® Predictive Models are cutting edge approachs to better target your case management and disease management activities. The ACG PMs remain grounded in the disease burden perspective unique to the ACG® System, which focuses on commonly occurring patterns of morbidity and an assessment of all types of medical need.
This comprehensive approach has repeatedly been proven to have many advantages over comparable case-mix adjustment approaches, especially those that use complex data-mining or artificial intelligence algorithms where, conceivably, no two patients are categorized in the same manner.
Identifies Unique Individuals for Care Management
Unlike many traditional methods for case identification (such as hospital concurrent review and ED utilization reports) the ACG Predictive Models identify many persons in need of care-management intervention before they become high utilizers. ACG PMs will identify up to 25% more individuals for care management than other methods based on prior utilization. By emphasizing developing patterns of morbidity, the ACG PMs helps to identify individuals with a high disease burden—those who are often seeing multiple providers and taking multiple prescriptions. These individuals are often excellent candidates for care management as they may benefit from improved coordination of care. Greater insight about the convergence of risk, medical utilization and prescribing patterns can be captured by combining risk defined by diagnoses with risk defined by retail pharmacy claims.
Improves Care Management Processes
Significant administrative efficiency is introduced to the care management process in the areas of patient risk assessment and in patient targeting. Assessments of patient risk can be performed rapidly and on the whole population using readily available claims information. When retail pharmacy claims are available, high risk patients can be identified with as little as 3 months of data. Additionally, the clinical markers produced by the system eliminate the need to review claims manually, helping the care manager understand a patient’s disease and morbidity profile; and, even further winnowing the number of records that warrant detailed chart review. More individuals are identified because pharmacy data and medical claims data will often identify different lists of members with the same clinical diagnosis. The ACG System uses both data sources and facilitates automated selection of members based upon program-specific criteria. This allows care managers to spend more time interacting with patients and less time on analysis.
