BOOKS OF RtfiDIfGS - PAHO/WHO

" E MIX DEFINITION BY DRG
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Y )
is the mean value of the dependent variable
in the kth group. The total within
group sunm of squares (TWGSSQ) for the G
groups is the sum of the total squared deviations
of each grotip's observations froin
the respective groupl meian and is given by
TWGSSQ (G) =
G
Y,,= i R
i¡ Rt
M1,
' (Y" -- Yk,)
j=!
For a given independent variable, the
CLASSIFY algorithm partitions observations
into the particular set of groups that
results in the uminimization ofTWCSSQ for
a specific dependent variable. Since
TWGSSQ is proportional to the variance
left unexplained by the independent variable,
minimization of TWGSSQ results in
the miniiiiization of the unexplained variance
of the data.
2.3 DRG Formation from Major
Diagnostic Categories
To facilitate the analysis over the wide
range of disease conditions in the acutecare
setting all diagnoses were initially
divided int.i 83 mutually exclusive and
exhaustive Major Diagnostic Categories.
Their formation was also motivated to insure
diagnostic hornogeneity. Thus, the
final clusters do not contain patients that
transcended these categories. For example,
from the point of view of output utilization,
it may be appropriate to form a patient
class with hemorrhoíds, hypertrophy of
tonsils, and normal delivery. The ou" ut
utilization of these patients is very similar,
often ret,s.`ring a relatively minor surgic.l
procedure with a very short preoperative
stay and a total hospitalization period of 2
1.
iE Rk
(2.5)
MiEDICAL CAIIE
or 3 days. However, the physicians who
would treat these patients as well as the
treatment processes of the problems they!
are presenting are quite diflerent. Therefore,
it was felt that including such pati.iats
ill the samine class would not define a nedlically>
meaningthl category.
The specification of the Major Diagnos.
tic Categories wvas peirforíied by al coiniittee
of clinicians, tollowing 3 general
principles:
1. Major Diagnostic Categories miust
have consisteiicy ii ternus oftleira iilt(llie.
phvsiop.atho>logic classificaitiotn. or in tlht.
maiutier in which tlihey are cliniícally ma.iaged.
2. iMaijor Diagnostic Categories ¡must
have a siulicient iinuiber of patients.
3. Major Diagnostic Categories mniust
cover the complete range of codes withouit
overlap.
A list ofthese categoríes as de'ined by their
ICDA8 and HICDA2 codes appears in
Table 1. There is also an indication of the
corresponding Professiohal Activity Study
(PAS) diagnosis groups that correspondl to
each category. Note that the categories are
very broad, such as Diseases of the Eye.
Diseases of the Cardiovascular System aud
Infectious Diseases..
A consistent process was followed in partitioning
each Major Diagnostic CategorY
into DRGs. First of all, each category was
refined by eliminating certain unwanted
observations. Cases with dead patients or
bad records, and those that were particiularly
deviant, were excitided ironi tiirIIILr'
analysis. Cases with dead patients were
removed trom consideration since thleir
lengths of stay were probably atypical of
the disease or problemni under consi(lerai
tion. Records with olbviois coding errors or
missing data were also eliminated because
their information could be misleading. Ob-