I just can't resist. Rather than moving in a philosophical direction, my humble suggestion is to build and use model(s) that serve the needs of effective/adequate communication. There is a trade-off between descriptive power and number of constraints placed on the model. Required/standard attributes act as constraints on models and systems that implement these models. In asking the question in the subject line (during a psychiatric mental status exam), the intention is not to elicit an exhaustive list of universally agreed upon attributes - but rather to examine the first few similarities that come to mind. Similarly, why do we care about modeling 2/3 tons of Apple vs. 3 cart-loads of Oranges (continuous/discrete and across variables) via a set of common attributes (e.g. Data_value, Quantity)? Why should these particular attributes be modeled differently from other attributes such as Color, Size, pH, Taste, Shape, Price, etc? More below. On Mon, 24 Sep 2001, Bart Koppers wrote: ... > for example: a code (could be an integer, but a string would do fine too) > or a link to a certain dosage of a drug, in which that dosage contains > information like "twice a day", might not be a numeric value or whatsoever. > It is however, quantifiable. ... Bart makes a very important point. Once you introduce a "subtype=Quantity", you are burdened with drawing numberous lines between what are considered "Quantity" and those that are not-Quantity. This necessary extra-work must be justifiable in terms of more effective/efficient communication (for example, certain operations/calculations are meaningful if the attribute is a subtype of "Quantity".) Back to the original question: > > Another modelling question, as part of our development of the openEHR > > "convergence model" for EHRs: > > > > In GEHR there is a DATA_VALUE subtype called QUANTITY, which models > > physical quantities. See bottom for abstract semantics. > > > > HL7 has a type called QTY also, with similar semantics. > > > > The question is: do we really need two types, to model discrete and > > continuous quantities? For example, DISCRETE_QUANTITY and > > CONTINUOUS_QUANTITY. If you followed my reasoning thus far, you will not be surprised by my interest in asking - So What? What can I do with discrete quantity that I cannot do with continuous quantity? Why should I even bother knowing the difference between the two? Where is that list of terrible things that will happen if I use "Continuous" for everything? Even more importantly, what does Quantity mean (in the functional sense) within the GEHR model? What can one do with attributes of the Quantity type that cannot be done with Color attributes, for example? > > Currently, the type of value in QUANTITY is REAL, > > which theoretically accommodates INTEGER, i.e. > > the possible values of discrete measurements, but it hides the true > > nature of discrete v continuous thing being measured; in particular, we > > have to add semantics to the class to allow it to be specified as > > discrete or continuous. I think I understand what you are saying. But, some examples will help. Thanks, Andrew --- Andrew P. Ho, M.D. OIO: Open Infrastructure for Outcomes www.TxOutcome.Org (Hosting OIO Library #1) Department of Psychiatry, Harbor-UCLA Medical Center University of California, Los Angeles
