Dear Seref, I do not agree with this without having explored all the
possibilities. I think it is important not to jump to conclusions and
keep the discussion open.
I have some ideas how to keep it interoperable. I like to discuss that
with an open mindset.
Talking about interoperability.
By the way, how do you create FHIR messages from OpenEhr-compositions?
Or how do you create Openehr-compositions from FHIR messages?
You have to create a template manually, fitting that item to that
datapoint, isn't it?
Even within two parties using OpenEhr. You are only automagically
interoperable when two parties use exact the same archetypes, else you
need to puzzle the dataitems.
The same things you have to do when you need to handle a generated
archetype. But it will not be that hard. Don't expect much complexity
from these generated archetypes.
I called them before, micro-archetypes, containing only one datapoint,
or a few closely related datapoints.
With machine learning algorithms, it must not be hard to interpret them.
Don't understand me wrong, I like OpenEhr, because of the archetyped
system, and the flexibility it offers. It is not by accident that I
discuss it here and not in a HL7 group, although that would bring more
money.
But if flexibility is slowed down by years of review, discussing and
consensus over the whole world for a set of archetypes, then there is
not much flexibility left.
This can work very good for the archetypes which are in CKM, but all
those new devices, all those new datatypes, all this new protocols,
which cannot wait for these review-procedures, because the market will
be jumped far ahead by then.
Best regards
Bert
On 27-06-18 11:50, Seref Arikan wrote:
Hi Bert,
Let me try to keep it brief: you seem to suggest breaking the openEHR
methodology. If you allow downstream actors (clinical systems, guided
by their users) create archetypes without going through the
methodology, i.e. creating, discussing, reviewing archetypes, you'll
end up with computable health with no interoperability.
This will in turn break machine learning because you cannot learn
anything valuable from datasets which are created based on data, which
are based on models, which are based on clinicians going siri on their
systems.
As a side note, this whole domain will make much faster progress when
someone starts teaching clinicians (when they're at medical school)
that informatics, just like washing hands before an operation, is
partly their responsibility and they cannot get much out of their
systems until they start taking charge of some aspects of it, instead
of waiting for vendors to present them their incorrect/biased view of
clinical care.
Our fundamental problems need humans doing what needs to be done,
we're still nowhere near the capability to get rid of having to do
what openEHR methodology allows us to do, from and AI perspective.
All the best.
Seref (who could not keep it brief...)
On Tue, Jun 26, 2018 at 11:31 PM, Bert Verhees <bert.verh...@rosa.nl
<mailto:bert.verh...@rosa.nl>> wrote:
One short addition, why this discussion, the original point:
What about machine learning?
Machine learning becomes possible when many daily health related
data are available. A machine can, f.e. detect deviations.
Why generated archetypes?
Every day there are new devices, new ideas about health, we cannot
wait for CKM to follow day to day inventions, and some of them
only used by minorities. The EHR must be able to create archetypes
when needed.
Op wo 27 jun. 2018 00:18 schreef Bert Verhees
<bert.verh...@rosa.nl <mailto:bert.verh...@rosa.nl>>:
Thanks for supporting reactions.
It is really typical in western medical science that it is
very problem oriented. All EHRs, even unconventional one, even
the new thinking, it is very problem oriented.
All data are gathered around a problem and in relevance of a
problem. All datastructures are pointing to a problem. Without
problem there is no datarecording.
It is historically grown like that. Medical data collecting is
only done by clinicians, and only when a patient has a
problem, the data around the problem, the diagnosis, and the
treatment, that is important. Data which do not have a known
relevance are not recorded.
And when the patient has a new problem, the only information
available are the problems in history. Information about
lifestyle is unknown. One can ask the patient, but some
patients have a selective memory.
But in sports this is different. Medical datarecording also
happens when there is no problem, but as daily routine. But
now, many people today, also no-sport people, I wrote before
today, measure data many times. Apple patented a blood
pressure device in Applewatch. It is cheap, easy to do.
It will not take long and people have their own EHR at Google,
Amazon, Microsoft, Walmart or Apple, to record their daily
medical data. They maybe will be able to demand that GP's
store their findings in that EHR, so a more holistic view
about the patient will become available, and maybe insurance
companies will reward access to that holistic view.
We must prepare for that, the face of healthcare will change.
Until now it was problem-care, which we called in Orwellian
tradition Newspeak: healthcare. But it will change to really
healthcare. It is something completely different, and it
happens fast.
I learn also from this, while writing I learn. But I have said
it all. Now it would be nice to discuss how to implement
healthcare instead of problemcare.
Bert
Op di 26 jun. 2018 22:18 schreef Karsten Hilbert
<karsten.hilb...@gmx.net <mailto:karsten.hilb...@gmx.net>>:
> But the person should be seen as more then a medical
complaint, but as a
> complex of conditions and lifestyle.
> We need generic archetypes which can store machine
generated datasets to
> store information about the whole person, instead of
only the medical
> condition which is subject of conversation.
>
> I believe I am the only person in this list who thinks
like that. But
> that does not matter.
Actually, any worthwhile GP thinks like that (except we
don't say
things like "datasets" or "generic archetype").
I rather doubt you are alone in this. Even on-list.
Karsten
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