title: Risk-stratification methods for identifying patients for care coordination.
abstract: BACKGROUND
  Care coordination is a key component of the patient-centered medical home. However, the mechanism for identifying primary care patients who may benefit the most from this model of care is unclear.
  
  
  OBJECTIVE
  To evaluate the performance of several risk-adjustment/stratification instruments in predicting healthcare utilization.
  
  
  METHODS
  Retrospective cohort analysis.
  
  
  METHODS
  All adults empaneled in 2009 and 2010 (n = 83,187) in a primary care practice were studied. We evaluated 6 models: Adjusted Clinical Groups (ACGs), Hierarchical Condition Categories (HCCs), Elder Risk Assessment, Chronic Comorbidity Count, Charlson Comorbidity Index, and Minnesota Health Care Home Tiering. A seventh model combining Minnesota Tiering with ERA score was also assessed. Logistic regression models using demographic characteristics and diagnoses from 2009 were used to predict healthcare utilization and costs for 2010 with binary outcomes (emergency department [ED] visits, hospitalizations, 30-day readmissions, and highcost users in the top 10%), using the C statistic and goodness of fit among the top decile.
  
  
  RESULTS
  The ACG model outperformed the others in predicting hospitalizations with a C statistic range of 0.67 (CMS-HCC) to 0.73. In predicting ED visits, the C statistic ranged from 0.58 (CMSHCC) to 0.67 (ACG). When predicting the top 10% highest cost users, the performance of the ACG model was good (area under the curve = 0.81) and superior to the others.
  
  
  CONCLUSIONS
  Although ACG models generally performed better in predicting utilization, use of any of these models will help practices implement care coordination more efficiently.
authors.affiliation: Health Care Policy and Research, Mayo Clinic, 200 First St Southwest, Rochester, MN 55905. E-mail: haas.lindsey@mayo.edu.
authors.forename: Lindsey R
authors.initials: LR
authors.lastname: Haas
authors.forename: Paul Y
authors.initials: PY
authors.lastname: Takahashi
authors.forename: Nilay D
authors.initials: ND
authors.lastname: Shah
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Stroebel
authors.forename: Matthew E
authors.initials: ME
authors.lastname: Bernard
authors.forename: Dawn M
authors.initials: DM
authors.lastname: Finnie
authors.forename: James M
authors.initials: JM
authors.lastname: Naessens
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D017598:Efficiency, Organizational; D005260:Female; D006801:Humans; D008297:Male; D008485:Medical Audit; D008875:Middle Aged; D008910:Minnesota; D010346:Patient Care Management / Q000458:organization & administration*; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D055815:Young Adult