BOOKS OF RtfiDIfGS - PAHO/WHO

ei, paid to these standards in conducting
Delphi.' 3
n '/he iterative cípnvcrgc,:: : ,i cxput upiimon
may reflect increasing c :iiiotmity insicad
of increasing accuracy.
O "Expert" forecasts may bc more susceptible
to errors arising from "scnools of thought,"
conflicts, or from unanticipated developmcnis
in ficlds outside the expertise in the
group. The mcthod's usual total reliance
on expert opinion, unleavened by other
methodological checks, requires spccial
caution."
O The strength and accuracy of piedictions
are heavily dependent upon the initial conditions
specified and upon thc qualily and
appropriateness of the information supplied
to the participants during the iterative process.
Poor information tends to produce
polr results, in Delphi as elsewhere.
Trend Extrapolation
- 3;- --
A wide range of methods fall under this heading.
Each involves a review of the historical
data (primarily quantitative) pertaining to
sone problen: or issue. Using these data, trends
are "jdermined," assumptions made, intervening
vuariables are identified where pouaible, and
the interaction of some combination of these
facton is projected into the future as a forecasi.
Regresion or time series analysis are
among the mit frequently cmployed statislical
Uend exrapdlation techniques.' m
Two major variations of this method are
common. The first projecus data that cxpress
directly the phenomena being forecasted. Most
bed-necd formulas used in health service forecasting
are a variation on this theme. Por example,
the well-known Hill-Burton formula
projects bed requirements on the basis of trends
in population, use-rates, and average daily
censaus.'
The socond common varicty is based upon
data that are thought or known to be correatled
with thc phenomenon being projected.
This method ofien is employed when little or
no dta exist for thc phenomenon one wishes to
forecast. Use of demographic data as a surrogate
for ith factors in column one of Table
1 is a comnmon example. Or-citing the basic
Forecasting Hiealh Care Services
hypothetical ample used throughout this
paper-an inao,, th study of EMS facilities that
already have adopted computer diagnostic consulttion
services might show that their usage
is related in a complex way to some ten diffcrent
variables. These might include such factors
as physician work load, degree of medical
specialization, access to and use of other consultative
services, cost of the computer service,
and others. Previously completed studies make
it possible to predict the growth rate through
1985 for these ten variables. Using these
growth rates and the historical correlation between
these factors and the use of computers
by EMS personnel, it might be possiblc to predict
that 28 percent of local EMS pcrsonnel
will employ computer diagnostic consultation
in 1985.
Trend extrapolation methods have a number
of strengths to commend them to areawidc
planners. Here are some of the more important
ones:
Streng/lhs Claired:
O Future developments often may be straightforward,
predictable continuations of the
present and immediate past, at least within
"acceptable" error limits (e.g., population
growth and mix, incidence of disease, level
and distributions of income). Where this
is true, trend extrapolations can be very
effective at minimal cost, particularly over
the short run.
O These methods encourage the search for
trends and continuities and for factors that
cause, augment, or inhibit such trends.
O Since these metheds deal with "objective"
data and with matlhematical techniques, they
tend to reduce (though, of course, not eliminate)
the effects of analyst prejudice or
bias.
U The average behavior of large numbers of
people is much more stable and "predictable"
than that of individuals or small
groups. The capacity of quantitative (particularly
statistical) methods for dealing with
a large volume of data is very helpful on issues
involving the "trend" behavior of large
populations.
O Most statistical methods are based upon
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