Chaos to complexity: leveling the playing field for measuring value in primary care

Rationale, aims and objectives Develop a risk‐stratification model that clusters primary care patients with similar co‐morbidities and social determinants and ranks ‘within‐practice’ clusters of complex patients based on likelihood of hospital and emergency department (ED) utilization. Methods A retrospective cohort analysis was performed on 10 408 adults who received their primary care at the Medical University of South Carolina University Internal Medicine clinic. A two‐part generalized linear regression model was used to fit a predictive model for ED and hospital utilization. Agglomerative hierarchical clustering was used to identify patient subgroups with similar co‐morbidities. Results Factors associated with increased risk of utilization included specific disease clusters {e.g. renal disease cluster [rate ratio, RR = 5.47; 95% confidence interval (CI; 4.54, 6.59) P