openEHR-technical Digest, Vol 64, Issue 6
thomas.beale at openehr.org
Mon Jun 5 12:54:49 EDT 2017
this has to be essentially correct, I think. If you think about it,
scores (at least well designed ones) are things whose 'questions' have
only known answers (think Apgar, GCS etc), each of which has objective
criteria that can be provided as training to any basically competent
person. When score / scale is captured at clinical point of care, any
trained person should convert the observed reality (baby's heartrate,
accident victim's eye movements etc) into the same value as any other
such person. In theory, a robot could be built to generate such scores,
assuming the appropriate sensors could be created.
With 'true' questionnaires, the questions can be nearly anything. For
example, my local GP clinical has a first time patient questionnaire
containing the question 'have you ever had heart trouble?'. It's pretty
clear that many different answers are possible for the same physical
facts (in my case, occasional arrhythmia with ventricular ectopics whose
onset is caused by stress, caffeine etc; do I answer 'yes'? - maybe,
since I had this diagnosed by the NHS, or maybe 'no', if I think they
are only talking about heart attacks etc).
My understanding of questionnaires functionally is that they act as a
rough (self-)classification / triage instrument to save time and
resources of expensive professionals and/or tests.
There is some structural commonality among questionnaires, which is
clearly different from scores and scales. One of them is the simple need
to represent the text of the question within the model (i.e. archetype
or template), whereas this is not usually necessary in models of scores,
since the coded name of the item (e.g. Apgar 'heart rate') is understood
by every clinician.
Whether there are different types of questionnaires semantically or
otherwise, I don't know.
On 05/06/2017 09:48, William Goossen wrote:
> Hi Heather,
> the key difference is that the assessment scales have a scientific
> validation, leading to clinimetric data, often for populations, but
> e.g. Apgar and Barthell are also reliable for individual follow up
> a simple question, answer, even with some total score, does usually
> not have such evidence base. I agree that in the data / semantic code
> representation in a detailed clinical model it is not different.
Principal, Ars Semantica <http://www.arssemantica.com>
Consultant, ABD Team, Intermountain Healthcare
Management Board, Specifications Program Lead, openEHR Foundation
Chartered IT Professional Fellow, BCS, British Computer Society
Health IT blog <http://wolandscat.net/> | Culture blog
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