BOOKS OF RtfiDIfGS - PAHO/WHO

CAME MIX DEFINITION BY DRG
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'I~e use of LOS as a imeasure of case complexity
has been studied by other researchers.
Luke in his work on case mix measurement"l
established the high degree ok
correlation between LOS and total charges
rendered the patient. Lave and Leinhardt
found significant correlation between LOS
and measures of case mnix complexity.'?
The process sI lorming the DRCGs was
beguin bIy partitioning the data )base into
mutntally exclusive and exhaustive primary
diagnostic areas, called Major Diaglnostic
Categories. Each nmijor c;ategory was then
examined separately and filrther subdivided
into groups based on values of
variables suggested by the statistical algorithmn.
Physician review of these recommended
subdivisions often led to modification.
Thus, at each stage of the process
the suhgroups were hased both on statistical
criteria as well as physician judgment.
The precise variables that were included
in class definitions varied across the major
categories. For example, age was determined
to be important in explaining utilization
for hernia patients, but not an important
factor for gastric ulcer patients. From
each Major Diagnostic Category, a number
of final patient classes was formed. These
final patient classes are the DRGs. A more
extensive discussion of the data base, the
statistical algorithm, and the general
strategy used in constructing the DRGs is
presented in the following subsect:ons.
2.1 Data Base
The data base used to construct the
scheme contained approximately 500,000
hospital records from 118 institutions in
New Jersey, 150,000 records from 1 Connecticut
hospital and 52,000 records of federally
funded patients from 50 instittutions
in a PSRO region. These records contained
demnoglrphic inlfornation al>out each patient
(e.g. sex, age) as well as clinical and
diagnostic information related to his hospital
stay (e.g. pro)lemns/(liagnoses, surgical
procedures, special services used).
MEDICAL CARE
Diagnostic information in the data base
was coded with both classification systems,
ICDA8 and HICDA2. Since there is not a
tlFect inatch between the 2 schelimes, data
from all hospitals could not be combined in
a unified data base. Thus, it was decided to
construct the classification scheme using
the miore prevalent ICDA8 codes as the
standard. The ICDA8 version was then
translated to HICDA2. This translation
was evaluated with hospital dato. and
itecessary modlitficatioiis were miade to insure
the consistency of the classification
across the 2 coding schemes. Both ICDA8
and HICDA2 record surgical procedures
tsiíng 3-digit codes. These procedure
codes cover not only operations performed
but also some therapies and minor diagnostic
procediures. For ICDA8, the ranges of
codes that were considered to reflect actual
operations were 010-999 and A10-A59.
Likewise, for HICDA2, the actual code
range for operations is consjdered to he
010-920 and 933-936. In constructing the
patient classification scheine, only codes
within these ranges were considered as
surgical procedures.
2.2 Statistical Methodology
The particular statistical methodology
employed is a variation of the Automated
Interaction Detector (AID) method of
Sonquist and Morgan, which has previously
heen applied in the analysis of conmplex
saniple survey data at the UniversitY
of Michigan Survey Research Center.2?
The objective of this approach is to
examine the interrelationships of the variables
in the data base and to determine, in
particular, which ones are related to sonme
specified measure of interest, referred to as
the dependernt variable. This is accomplished
by recursively subdividing the
observations, through binary splits. inito
subgroups based on values of variables that
maximize variance reduction or minimize
the predictive error of tlie depeindent vairiable.
Subgroups aire designated termiiiiiinal
'4