Communications on a
National Project
The value of a standard is weighted
by two criterions. First, is the
standard really correct, and by this we mean to question standards like RDF
which has limited usefulness when compared with what the standard openly claims
to be its application domain.
Second, does the standard strongly
inhibit its own modification? The problems
with RDF are simply ignored in the Seybold article, as is customary in the IT
literatures. John Sowa makes a
comment about this at:
Taxonomy Discussion and Natural languages
and Common logic Controlled
English and RDF failure
Let us take an example of
standardization around codes.
IDC code are widely understood to be a much less value that what might be realized if the codes were not misused under a utility function driven by (1) the doctor’s need to maximize cash return from the insurance providers, (2) the uncertainty of medical judgments, (3) the shaping of the ICD code “theory” by the insurance providers, etc.
If one starts out with fundamental
mythology, such as the mythology that computer can think, feel and make
judgments, then hidden utility functions will create a mess. The science has to be correct and
the science has to drive the business models, not the other way around.
Examples of codes that are widely
seen as being dysfunctional abound.
The ICD-9 and ICD –10 codes create terrible problems for real people who
have to get recognition by the “system” that in fact, in a specific situation,
the code assignment was wrong.
Their failure often leads directly to reduced quality of life and even
death.
It could even be that the code
assignment was wrong for more than one reason, including a possibility that no
code within the standard will recognize the specific instance. Moreover, the reason why no code exists
within the standard for this instance is that the insurance companies do not
want to pay for this instance and they have manipulated to code standard to
avoid this outcome. The doctor may
not understand the true nature of this instance, due to systemic bias in medical
education.