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.
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.
Authors: Karen Abernathy; Jingwen Zhang; Patrick Mauldin; William Moran; Mac Abernathy; Elisha Brownfield; Kimberly Davis Journal: J Prim Care Community Health Date: 2016-06-24
Authors: Brent M Egan; Susan E Sutherland; Peter L Tilkemeier; Robert A Davis; Valinda Rutledge; Angelo Sinopoli Journal: PLoS One Date: 2019-06-19 Impact factor: 3.240
Authors: Shelley-Ann M Girwar; Robert Jabroer; Marta Fiocco; Stephen P Sutch; Mattijs E Numans; Marc A Bruijnzeels Journal: Health Sci Rep Date: 2021-07-23