Null Flavours and Boolean data in openEHR

The Problem of 'Boolean' or two-valued data 

A general design problem in health information is when to use Boolean data values. There are many situations where a naive analysis might indicate to use a Boolean, i.e. a DV_BOOLEAN in openEHR-speak for a data field such as gender, or as the response to a question like 'have you eaten in the last 12 hours?'. In both cases, the possible values are greater than simply yes/no or some equivalent. Gender could typically have values from a small set of codes like:

male
female
unable to determine
intersex
...

Similarly, yes/no questions in A&E might not be answered due to the patient being unconscious - which is a 'normal' happening in A&E. A 'don't know' answer might be prefectly sensible for many questions asked to patients.

What this means for Archetyping

The implications of the above approach for templates are that archetypes should fully model the range of responses for questions and other data elements that may seem to initially be Boolean in nature. 

The openEHR Approach 

The correct value set should then include yes/no/don't know, and possibly also things like maybe/most likely/unlikely etc.  In general, the value set should include values for any possible patient response - the data then correctly show that the patient was asked, but responded with some kind of 'don't know' or did not respond at all. Situations where the information could technically not be obtained, e.g. physician was talking to patient using an internet chat tool and the communication dropped out, or a response was techically impossible for some other reason, e.g. faulty equipment, should be marked with a null flavour. In general, null flavour is used sparingly in openEHR, and is not used for representing typical (if not necessarily common) clinical events that can be observed perfectly well by the clinician.

The openEHR Null Flavours are currently (with HL7 mappings):

Other approaches

This analysis is the only possble view of affairs, but it does ake care of the need to know what the patient or clinician said, even if it was not definitive. Complicated null flavour approaches tend to mix up such situations with the situation where data were unavailable for a techincal reason.