Measuring differences and similarities in hospital caseloads: a conceptual and empirical analysis.
OBJECTIVE
This article conceptually and empirically evaluates alternative index measures that have been used to distinguish among hospital caseloads. It introduces two new measures.


METHODS
The study relies on 1987 Medpar data, which provide a 100 percent sample of Medicare Part A claims for the calendar year.


METHODS
Descriptive statistics indicate the sensitivity of alternative caseload measures to hospital bed size, region, and urban/rural location. Multiple regression analysis then examines the ability of the caseload measures to distinguish among hospitals based on hospital- and area-specific characteristics.


METHODS
A provider level file containing the number of cases treated by each provider in each DRG was constructed from the Medpar data and merged with data from the American Hospital Association and the Area Resource File.


RESULTS
Different indexes purporting to measure hospital specialization are often evaluating very different aspects of the hospitals' caseloads. Prior work has indicated a specialization among hospitals during the period from 1980 to 1985. Replication of this work using other indexes could verify the increase in specialization and might provide a clearer picture of market or hospital characteristics associated with changing caseloads.
Dayhoff
D A
DA
Center for Health Economics Research, Waltham, MA 02154.
Cromwell
J
J
D002391:Catchment Area, Health / Q000706:statistics & numerical data*; D003953:Diagnosis-Related Groups / Q000706:statistics & numerical data*; D006302:Health Services Research / Q000379:methods; D006754:Hospital Restructuring / Q000706:statistics & numerical data*; D006780:Hospitals, Rural / Q000706:statistics & numerical data; D006786:Hospitals, Urban / Q000706:statistics & numerical data; D016344:Medicare Part A / Q000706:statistics & numerical data; D008511:Medicine / Q000706:statistics & numerical data; D015999:Multivariate Analysis; D011357:Product Line Management / Q000706:statistics & numerical data; D011787:Quality of Health Care; D012044:Regression Analysis; D013038:Specialization; D014481:United States