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

Rationale, aims and objectivesDevelop 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. MethodsA retrospective cohort analysis was performed on 10408 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. ResultsFactors 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<0.0001]}, low clinic visit adherence [RR=0.33; 95% CI (0.28, 0.39) P<0.0001] and census measure of high poverty rate [RR=1.20; 95% CI (1.11, 1.28) P<0.0001]. In the cluster model, a stable group of four clusters remained regardless of the number of additional clusters forced into the model. Although the largest number of high-utilization patients (top 20%) was in the multiple chronic condition cluster (1110 out of 4728), the largest proportion of high-utilization patients was in the renal disease cluster (67%). ConclusionsRisk stratification enhanced with disease clustering organizes a primary care population into groups of similarly complex patients so that care coordination efforts can be focused and value of care can be maximized.