Unambiguous data: Positive presence; positive absence

The bulk of openEHR archetype modelling is focused on how to record the positive presence of data – for example the diagnosis of diabetes or asthma, or orders for long-term beta blocker medication.

When it is not possible to determine data values – the nature of clinical medicine being not so exact a science – ‘null flavours‘ are used to mark an ‘incapacity to obtain data’, especially for a mandatory data point. The current openEHR null flavours are:

  • no information – “No information provided; nothing can be inferred as to the reason why, including whether there might be a possible applicable value or not”;
  • unknown – “A possible value exists but is not provided”;
  • masked – “The value has not been provided due to privacy settings”; and
  • not applicable – “No valid value exists for this data item”.

If there is no data recorded at all, nor any null flavour, no conclusion can safely be drawn from the lack of data; there is a void in our knowledge.

And then there is the need to record things that are noticeably not present. Some approaches subscribe the notion of ‘negation’ – resulting in a somewhat awkward and potentially ambiguous statement that might go something along the lines of ‘diagnosis of diabetes, not’, potentially sitting closely alongside another selectable term of ‘diagnosis of diabetes’. You can see that it is easy to get it wrong.

The openEHR preferred approach is to record the positive absence of data – an explicit and unambiguous statement of ‘no history of diabetes’ in an archetype with the explicit intent to record the absence of a diagnosis.

Specific absence of data needs to be recorded positively, where this is sensible:

  • To encourage unambiguous clinical records;
  • Assisting clinical decision support; and sometimes (unfortunately)
  • For medico-legal purposes.

Examples of global positive exclusion statements include:

  • No known adverse reactions
  • No surgical history
  • No significant family history
  • Not currently taking any medicines

Specific positive exclusion statements include:

  • No known allergy to penicillin (or <insert drug/plant/food/venom or other substance here>);
  • No family history of bowel cancer (or <insert diagnosis here>);
  • Not currently taking immunosuppressants (or); or
  • No pacemaker in situ.

Keep in mind that exclusion statements are only meaningful and relevant at the decision-making moment at which they are recorded. They have a fleeting temporal validity, and then the question has to be asked again – “Are you allergic to anything?”or “Have you ever had any serious illness or operations?”.

Some examples:

  • Recording an absence of an adverse reaction event to penicillin becomes immediately obsolete when the patient suffers an anaphylactic reaction to the penicillin subsequently administered; or
  • The past history might reveal no previous diagnosis of asthma, yet asthma might be the cause for today’s wheeze; or
  • In a Discharge Summary – ‘Not currently taking any medications’ is a vital piece of information for the patient’s usual GP, yet that drug-free state might only be current until the first follow-up visit.

So exclusions cannot be relied upon for any future decision-making. They are just ‘in the moment’ statements that need to be re-asked and regularly updated in records (both paper and electronic) as part of routine clinical practise.

So while they may be valuable, does that mean we desire to model every possible negative statement positively? This is certainly not desirable, nor is it helpful. A pragmatic approach is to research those situations where explicitly modelling exclusion statements provide some obvious value; preferably those that will have consistent re-use or may be utilised in knowledge-based activities such as Clinical Decision Support (CDS) systems.

Improved outcomes may result if CDS systems prompt for the recording a positive statement of:

  • ‘no known adverse reactions’ prior to prescribing medicines; or
  • ‘no history of asthma’ prior to prescribing beta blockers.

Currently we have archetypes for recording the presence of an Adverse Reaction, a Problem/Diagnosis, Family History and Medicines. In addition we have a generic parent archetype for Exclusion (ie the positive absence) plus specialisations for Adverse Reaction, Medication, Family History and Problem/Diagnosis exclusions so far.  The generic Exclusion archetype can be used as a standardised way to record the more uncommon statements – ‘Not (currently) pregnant’ might be a candidate here. But if there are regular use cases identified, these too may warrant their own explicit archetypes to ensure that positive absence is recorded consistently and safely.

One thought on “Unambiguous data: Positive presence; positive absence

  1. Pingback: ICMCC News Page » Unambiguous data: Positive presence; positive absence

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