> I agree there is a need to be able to create archetypes much more
quickly based on device specifications. We need to work on that.
If you are looking for device specifications, I guess you are aware of
the medical device information model and the nomenclature of ISO EN IEEE
11073?
http://11073.org/, especially
https://standards.ieee.org/develop/wg/PHD.html
11073:10101 is the nomenclature standard.
They have "Device Specialisations" for blood pressure, blood sugar,
medication dispensers, ...
This is heavily used by many, including Bluetooth, NFC, ....
This is also used in FHIR:
http://build.fhir.org/devicemetric.html
This is Maturity Level 1, so not fully stable ("Trial Use Use
Context: Not Intended for Production use")
Work seems to be ongoing, I see very recent changes there.
Hope this helps,
greetings,
Stefan
Am 28.06.2018 um 17:19 schrieb Bert Verhees:
On 28-06-18 10:33, Thomas Beale wrote:
On 27/06/2018 13:00, Bert Verhees wrote:
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?
that is correct, because FHIR imposes its own model. This is the
basic reason why one should not really use message standards to
interoperate over systems whose data are already transparently
structured. However, some organisations want to pay for these
pointless conversions, so people do them.
Stability and Mapping:
I think FHIR is good, because it is a stable model, and mapping
to/from FHIR can be used for long time, and FHIR is also much used, so
mappings can be used in more occasions. There are also disadvantages,
like the HTTP-REST protocol which it incorporated. Google is now
planning a GRPC protocol for FHIR, and that is promising, because
every datatype can have its own GRPC field predefined, and the
performance can really improve very much, maybe even 100 times as
fast. As a rule of thumb one could say: Never use REST/JSON/HTTP1.1
for stable models, it is throwing away a lot of performance.
Transparancy:
Data must not only be transparent in a way that people can understand
them, but they must also be transparent in a way that the
software-internals of the sender and receiver can handle them. For
that purpose they need to be mapped from and to these internal
processes. If a GP receives a FHIR message and maps it to his own
EHR-tables, then the data from that message become available in the
normal working screens of the doctor. That is transparency that is needed.
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.
they only need to use the same data points of those archetypes, or
else any specialised derivative. This isn't hard to achieve; pretty
clearly all systems using openEHR today use the same vital signs
archetypes or derivatives to record vital signs. There is no point
doing otherwise.
I don't know if that is true, but if you say so, I accept that
statement, also because it is restricted to vital signs.
https://en.wikipedia.org/wiki/Vital_signs
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've missed some of the earlier discussion, but unless you are
dealing with genuinely novel measurements or orders, you won't have
any 'generated archetypes' for most Observations or Instructions or
Actions. You might have some for novel questionnaires or other kinds
of assessment tools (new kind of score etc). But for the vast
majority of cases, I would think the real need is for runtime
/matching/ of data points from /existing/ archetypes to create
on-the-fly templates, something we've known about for 15 years.
I agree, there are not an endless number of data-points-types. They
could also be predefined. We would need sport-coaches, athletes and so
on to help us with that.
I called them before, micro-archetypes, containing only one
datapoint, or a few closely related datapoints.
Let's assume some of these are created, for the reasons mentioned
above; pretty soon you are going to want to curate them properly and
add them to the library. Over time, the number of 'generated
archetypes' will fall to nearly zero, and it will be the matching
process that is the main challenge when encountering data not planned
for.
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.
it is slowed down, that's true, and it could be faster. But I don't
see how that reduces flexibility.
The inflexibility is in giving the proceedings out of hands, losing
control, having to deal with changes which are not asked for or
wanted. The data-points would need to be as simple as possible, mostly
in ELEMENT-structure instead of CLUSTER, or only very simple CLUSTERS
when 1 data-point is not sufficient. No deep structures, I would advise.
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.
I agree there is a need to be able to create archetypes much more
quickly based on device specifications. We need to work on that.
Yes, I agree
Bert
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