March 1, 2019

Risk Stratification Methods

The foundational step to targeting high-risk patients is to identify them. Risk stratification is a tool for identifying entities' patient population and predicting patients at highest risk, likely to be at high risk, and prioritizing the management of thier care in order to prevent unhealthier outcomes. Having a platform to stratify patients according to risk is key to the success of any population health management initiative. During Greater Columbia ACH's June Learning Collaborative meeting, we will be discussing more risk stratification tools to address Milestone 2A.2 in the Practice Transformation Contract. 

Several different methods are available for stratifying a population by risk: 
  • Hierarchical Condition Categories (HCCs): Part of the Medicare Advantage Program for CMS, HCC contains 70 condition categories selected from ICD codes and includes expected health expenditures.
  • Adjusted Clinical Groups (ACG): Developed at Johns Hopkins University, ACG uses both inpatient and outpatient diagnoses to classify each patient into one of 93 ACG categories. It is commonly used to predict hospital utilization.
  • Elder Risk Assessment (ERA): For adults over 60, ERA uses age, gender, marital status, number of hospital days over the prior two years, and selected comorbid medical illness to assign an index score to each patient.
  • Chronic Comorbidity Count (CCC): Based on the publicly available information from Agency for Healthcare Research and Quality (AHRQ)’s Clinical Classification Software, CCC is the total sum of selected comorbid conditions grouped into six categories.
  • Minnesota Tiering (MN): Based on Major Extended Diagnostic Groups (MEDCs), MN Tiering groups patients into one of five tiers: Tier 0 (Low: 0 Conditions), Tier 1 (Basic: 1 to 3), Tier 2 (Intermediate: 4 to 6), Tier 3 (Extended: 7 to 9), and Tier 4 (Complex: 10+ Conditions).
  • Charlson Comorbidity Measure: The Charlson model predicts the risk of one-year mortality for patients with a range of comorbid illnesses. Based on administrative data, the model uses the presence/absence of 17 comorbidity definitions and assigns patients a score from one to 20, with 20 being the more complex patients with multiple comorbid conditions. It is effective for predicting future poor outcomes. This method is explained in further detail below

One thing all of these models have in common is that they are based, in some degree, on comorbidity. Understanding comorbid conditions is a critical aspect of population health management because comorbidities are known to significantly increase risk and cost. In fact, a study from the Agency for Healthcare Research and Quality reports that care for patients with comorbid chronic conditions costs up to seven times as much as care for those with only one chronic condition.