In the fast-paced world of healthcare, technology is changing at such a rapid pace that it is often difficult to keep up with its ever-changing applications. One way in which technology has had a tremendous impact is in regard to patient populations. As data analytics continue to evolve, we also continue to gain better insight into trends within specific patient populations which enables us to better predict and manage the resources needed in order to provide the best quality care for these populations. Additionally, these trends can be used by healthcare organizations in order to improve the overall health within specific patient populations as organizations continue to pursue the quadruple aim as a means of improving their success factors within the context of value-based care.
One key tool in predicting how best to care for specific patient populations is risk stratification. And while risk stratification is nothing new, some of the indexes and tools available to do so might be. Below is a comprehensive list of current measures and indexes that can be used in order to further bolster your organizations quadruple aim of bolstering population health:
- Hierarchical Condition Categories (HCCs): Uses ICD-10 codes to categorize conditions in order to predict healthcare expenditures.
- Adjusted Clinical Groups (ACG):is used to predict hospital utilization based on patients in-and-outpatient visit history.
- Elder Risk Assessment (ERA): is an index that is used for patients over 60 that uses a series of variables to assign patients a risk score.
- Chronic Comorbidity Count (CCC): groups patients into six distinct categories based on comorbidity.
- Minnesota Tiering (MN): assigns patients “complexity tiers” based upon how many chronic condition categories affect them.
- Charlson Comorbidity Measure: provides a means of categorizing comorbidity in studies.
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