Null Flavours and Boolean data in openEHR

The Use of Null Flavor in openEHR

In the openEHR Reference Model, the low-level class ELEMENT has attributes 'value' and 'null_flavor'. The latter attribute is taken from HL7 (although used in a different way) and is used to mark a 'lack of data'. Using this attribute in openEHR was inspired by a) the need to do something about marking missing data in health information and b) the use of 'data quality markers' in SCADA control systems which show on the screen when a measured value from the field is out of date or wrong due to technical failure to obtain the current value. In the development of openEHR it was thought that some kind of data quality marker should be available for a similar reason: to indicate technical incapacity to obtain data.

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.

In the HL7v3 modelling approach, Null Flavour is used to indicate 'missing' data, such as to represent situations like 'asked but not answered'.

The openEHR Approach 

In openEHR, we see that when a physician asks a question and it is not answered - e.g. the patient is dazed or becomes unconscious - as being a normal medical situation. There is no technical incapacity of the physician to obtain information - he or she is in effect making a normal observation. So in modelling the information obtained in such situations, we should ensure that value set for questions like 'have you eaten in the last 12 hours' should include yes/no/don't know, and possibly also things like maybe/most likely/unlikely etc. In an A&E (ED) situation, most likely the responses to any question might include no answer, due to unconsciousness.

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):

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.

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.