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

Abstract

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 < 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%).

Conclusions: Risk 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.

Keywords: disease clustering; patient-centred medical home; practice-level resource allocation; risk stratification; social determinants of health.