A Predefined Rule-Based Multi-Factor Risk Stratification Is Associated With Improved Outcomes at a Rural Primary Care Practice

Abstract

This study built a predefined rule-based risk stratification paradigm using 19 factors in a primary care setting that works with rural communities. The factors include medical and nonmedical variables. The nonmedical variables represent 3 demographic attributes and one other factor represents transportation availability. Medical variables represent major clinical variables such as blood pressure and BMI. Many risk stratification models are found in the literature but few integrate medical and nonmedical variables, and to our knowledge, no such model is designed specifically for rural communities. The data used in this study contain the associated variables of all medical visits in 2021. Data from 2022 were used to evaluate the model. After our risk stratification model and several interventions were adopted in 2022, the percentage of patients with high or medium risk of deteriorating health outcomes dropped from 34.9% to 24.4%, which is a reduction of 30%. The medium-complex patient population size, which had been 29% of all patients, decreased by about 4% to 5.7%. According to the analysis, the total risk score showed a strong correlation with 3 risk factors: dual diagnoses, the number of seen providers, and PHQ9 (0.63, 0.54, and 0.45 correlation coefficients, respectively).