Tom, Not sure of the need for <= or >=. It's either beyond the value reading capability of the device or an actual value is record (within some accuracy tolerance).
Heath -----Original Message----- From: [email protected] [mailto:owner-openehr-technical at openehr.org] On Behalf Of Thomas Beale Sent: Saturday, 22 April 2006 5:43 PM To: openehr-technical at openehr.org Subject: Re: Pathology numeric values not supported in DV_Quantity Tim, I agree with the workshop idea, and assume that it could at least be done in Australia as a starting point. Thus, for the short term, I am inclined to add only the very simple "<, >, <=, >=, =" indicator, and possibly consider the "~" one (since these at least allow us to properly represent very low and very high path test values that are sent as "<5" and similar). The complex stuff that Tim has described below needs proper modelling and in the end will lead to new data types (and as Gerard says, it may well lead to something in the archetypes). As with everything, we need to really understand the exact requirements first, and that probably won't happen without a workshop. - thomas Tim Churches wrote: > Thomas Beale wrote: > >> Colin Sutton wrote: >> >>> The 'coding' is surely 'Accuracy' ('Measurement' has 'Accuracy') >>> where this can be None|~|Unknown|Percentage(value)!SD(Distribution >>> type,value) which would cover any measurement (e.g.height,heart >>> rate), not just pathology lab values >>> >>> >> this seems pretty close to a correct model. Slight corrections I >> would suggest are: >> - I am still uncomfortable with '~', since it seems to mean >> "approximate", but "we don't know how approximate"... >> - does "None" mean a) none recorded (i.e. don't know, i.e. same as >> '~') or b) no accuracy, i.e. an exact value (reasonable for some things, e.g. >> the answer to the question "number of previous pregnancies")? >> - in the case of a statistical distribution, one value may not be >> enough to characterise the limits, since the distribution may be >> asymmetric (I don't remember enough beyond normal/T/Chi2 to remember >> if there are distributions that need even more parameters). >> > > In terms of statistical confidence limits/intervals, the parameters are: > the type of limits/interval (frequentist "confidence interval" or > Bayesian "credible interval", the confidence value (typically 95%, but > often not), and the underlying assumed *error* distribution (normal, > Poisson, Student's T, Weibull etc etc). > > However, confidence intervals/limits don't indicate where in a > population distribution a particular value lies - quantiles are more > often used for this - the actual quantile of the value (eg for growth > measurements read against a normogram), or the values of the quartiles > or 5th and 95th percentile, or variations on that. > > >> The question for us in openEHR is how much to implement of such a model: >> we have to be driven by real use cases. >> > > If you really want to nail this problem, a workshop involving a range > of people (from lab scientists to pathologists to clinicians to > epidemiologists and biostatisticians) is required, I think. It could > be in the form of a virtual workshop via email, but you really need to > gather together a diverse group, state the problem/s to be solved (eg > lab values only, or physical measures only, or to include other things > like measures derived from psychological scales or population or study > measures like odds ratios and relative risks or age-standardised > rates?), and get them to generate use cases and explore the issues. > Happy to be involved, but as an epidemiologist, I'd feel more > comfortable if some mathematical statisticians and some lab scientists > were involved too. > > Tim C > > > -- ____________________________________________________________________________ _______ CTO Ocean Informatics (http://www.OceanInformatics.biz) Research Fellow, University College London (http://www.chime.ucl.ac.uk) Chair Architectural Review Board, openEHR (http://www.openEHR.org)
