title: Bringing responsibility for small area variations in hospitalization rates back to the hospital: the propensity to hospitalize index and a test of the Roemer's Law.
abstract: OBJECTIVE
  To assign responsibility for variations in small area hospitalization rates to specific hospitals and to evaluate the Roemer's Law in a way that does not artificially induce correlation between bed supply and utilization.
  
  
  METHODS
  We used data on hospitalizations and outpatient treatment for 15 medical conditions of nonmanaged care Part B eligible Medicare enrollees of 65 years and older in Massachusetts in 2000.
  
  
  METHODS
  We used a Bayesian model to estimate each hospital's pool of potential patients and the fraction of the pool hospitalized (its propensity to hospitalize, PTH). To evaluate the Roemer's Law, we calculated the correlation between hospitals' PTH and beds per potential patient. Patient severity was measured using All Patient Refined Diagnosis Related Groups.
  
  
  RESULTS
  We show that our approach does not artificially induce a correlation between beds and utilization whereas the traditional approach does. Nevertheless, our approach indicates a strong relationship between PTH and beds (r=0.56). Eighteen (of 66) hospitals had a high PTH that differed significantly from 16 hospitals with a low PTH. Average patient severity in the high PTH hospitals was lower than in the low PTH hospitals. Although the difference was not statistically significant (P=0.12), there was a medium effect size (0.58).
  
  
  CONCLUSIONS
  Variation across hospitals in the PTH index, the strong relationship between beds and the PTH, and the lack of relationship between severity and the PTH suggest the importance of policies that limit bed growth of high PTH hospitals and create incentives for high PTH hospitals to reduce hospitalizations.
authors: Center for Organization, Leadership and Management Research, VA Boston Healthcare System, Boston, MA 02215, USA. mshwartz@bu.edu
authors: Michael
authors: M
authors: Shwartz
authors: Erol A
authors: EA
authors: Peköz
authors: Alan
authors: A
authors: Labonte
authors: Janelle
authors: J
authors: Heineke
authors: Joseph D
authors: JD
authors: Restuccia
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D005260:Female; D006302:Health Services Research; D006739:Hospital Administration / Q000706:statistics & numerical data*; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008404:Massachusetts; D006278:Medicare / Q000706:statistics & numerical data; D012720:Severity of Illness Index; D017062:Small-Area Analysis; D014481:United States