Risk Stratification Methods, Use Cases, and Lessons Learned

To deliver on the promise of high-quality, value-based care for our patients in the context of workforce shortages and an aging population, risk stratification is essential. It involves systematically categorizing patients by risk level based on their health status and other factors.^1–3 This allows for more targeted interventions, equitable panel sizes, efficient resource allocation, and a proactive, population-based approach to care.^4,5

The Military Health System (MHS) has been exploring risk stratification for many years as a way to encourage more appropriate care delivery and reduce “low-value care.”^6,7 Based on the MHS’ recent implementation of risk stratification in primary care, this article describes risk scoring options, use cases, and lessons learned.

Risk stratification is based on a qualitative or quantitative “risk score.” Multiple methodologies exist for calculating this score.

FPM previously published a methodology based on gender and age.⁸ Perhaps the best known and most widely used methodology is the Johns Hopkins Adjusted Clinical Group (ACG) System, which offers an array of outputs (including numeric scores, resource utilization bands, and patient need groups) and inputs (including utilization patterns, social determinants of health, and medications). It has been used successfully in a variety of settings.⁹ Other methodologies use a qualitative score, sometimes called a “worry score,” which clinical staff assign based on their knowledge of the patient. This approach has proven beneficial in the prediction of decompensation in the inpatient setting.¹⁰ Some health systems have developed their own methodology, such as the University of California-San Francisco’s risk score for the purpose of panel weighting.¹¹

The MHS has used several risk-scoring methodologies over the years, including the ACG System, the Illness Burden Index (IBI, an offshoot of the ACG), and a proprietary system called the Health Risk Score (HRS). The HRS uses regression analysis on cost and claims data to identify weights for diagnoses. Weights are cumulative in patients with multiple diagnoses and represent the proportionate resources needed to care for them. For example, a healthy patient with no chronic conditions might have a risk score of 0 or 0.5, while a patient with several chronic conditions might have a risk score of 6 or more.