title: Chaos to complexity: leveling the playing field for measuring value in primary care.
abstract: OBJECTIVE
  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.
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: William P
authors: WP
authors: Moran
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Jingwen
authors: J
authors: Zhang
authors: Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
authors: Mulugeta
authors: M
authors: Gebregziabher
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Elisha L
authors: EL
authors: Brownfield
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Kimberly S
authors: KS
authors: Davis
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Andrew D
authors: AD
authors: Schreiner
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Brent M
authors: BM
authors: Egan
authors: Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA.
authors: Raymond S
authors: RS
authors: Greenberg
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: T Rogers
authors: TR
authors: Kyle
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Justin E
authors: JE
authors: Marsden
authors: Department of Clinical Pharmacy and Outcome Sciences, South Carolina College of Pharmacy, USC Campus, Columbia, SC, USA.
authors: Sarah J
authors: SJ
authors: Ball
authors: Division of General Internal Medicine and Geriatrics, Medical University of South Carolina, Charleston, SC, USA.
authors: Patrick D
authors: PD
authors: Mauldin
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D015897:Comorbidity; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D005260:Female; D006761:Hospitals / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D017533:Marital Status; D008875:Middle Aged; D011203:Poverty / Q000706:statistics & numerical data; D011320:Primary Health Care / Q000458:organization & administration*; D044469:Racial Groups / Q000706:statistics & numerical data; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D017678:Sex Distribution
keywords: disease clustering; patient-centred medical home; practice-level resource allocation; risk stratification; social determinants of health