RE: [xml-dev] The year is 2027, and we need to examine archived XML docu

[Len Bullard <len.bullard@uai.com>]

Boy howdy on that one.  HL7 and the six or seven other code domains used for
health systems will make a programmer choke.  The medical community relies
on a lot of obscure (nay, plain just weird) terminology to stay wealthy and
prominent.

Fortunately, those are upstream from the alert systems.  Alerts are NOT
incidents and incidents are NOT cases:  CAP gets wound around the axle on
that until one realizes that 'reportable' incidents that create alerts are a
much smaller subset than the laboratory reports.  My sympathy to anyone
writing the case investigation system that consumes the lab reports.
However, if you aren't trying to document the precise syndromes, diseases,
etc., and do the statistical data reporting, variations on CAP work fine for
the incident report itself as long as Department of Health is not relying on
it to transport the case conditions in detail.  

IOW, keep the incident/alerting messages quite separate from the reporting
systems and analyze carefully which information is near-real time and which
is pre-post-event analytics.  Figuring out that what an 'event' is and
sorting out the overloads because CAP used the terms wantonly consumes
design time.

len

From: Michael Kay [mailto:mike@saxonica.com]  

Ann Wrightson gave a talk on HL7 at Extreme a few weeks ago, and I was
frankly appalled. The level of abstraction adopted in the encoding of this
data is so high that the chance of anyone understanding it who hasn't been
immersed in the subject for years is to my mind near zero. The concept of
data being human-readable and tags being self-defining has gone completely
out of the window. I suspect the "documented schema" in this case is more
like 65,000 pages than 6,500 - though I'm guessing. Yet this data (medical
records) is exactly what researchers will want to be studying in 50 or 100
years time.

Even when the data is being used today, I would think the risk of
miscommunications is very high when the data is so complex and
un-self-explanatory.
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