“Smart data, smarter healthcare”

Last week Hugh Leslie & I spent time in Shanghai and Huangzhou at the invitation of Professor Xudong Lu and Professor Huilong Duan. It was a privilege to be invited and particpate in the very first openEHR meetings to be held in China.

It follows on from a surprise discovery of Professor Lu’s work presented at the Medinfo conference in Brazil last year, and Prof Lu attending our openEHR training in Kyoto in Janauary.

Hugh & I presented at two events:

  • a seminar of invited guests and VIPs for the first openEHR meeting to be held in China on April 18 in Shanghai – an introduction to openEHR (Heather) and an overview of openEHR activity in Australia (Hugh); followed by
  • an introduction to openEHR – at the China mHealth & Medical Software conference, Shanghai on April 19

Watch my introduction to openEHR, ‘Smart data, smarter healthcare’ presentation, available via slideshare:

Adverse reaction risk: the provenance

This week I documented the provenance of our Adverse Reaction Risk archetype – it has been a long & memorable journey from the first iteration in 2006 through to its publication in the international openEHR CKM last November 2015.

In the beginning was Sam Heard‘s original archetype – created way back in 2006 when Ocean Informatics had a .biz email address and before any collaboration – just the initial thoughts of one individual.


This was uploaded to the International openEHR CKM in July 2008.


In 2008 this archetype had its first collaborative review. The results of this were collated and as Editor I revised this archetype significantly to include the review feedback PLUS input from a number of publications available from NHS England, FDA and TGA  drug reporting requirements and the ICH-E2B publications. This was uploaded at the end of August 2009.

In late 2010, Australia’s National eHealth Transition Authority (NEHTA) forked the archetype and brought it into the NEHTA CKM environment and ran a series of 5 archetype reviews during the period through to June 2011. The resulting archetype formed the basis for the adverse reaction data elements in the initial PCEHR CDA documents which are currently being transmitted from Australian primary care clinical systems into the PCEHR (now rebadged as ‘My Health Record‘).

NEHTA starts.jpg

In 2012, there was another review round carried out in the international CKM.

The results from that 2012 review, the outcomes from the June 2011 NEHTA archetype and publications from HL7’s FHIR resource and RMIMs were amalgamated by Ian McNicoll in June 2014 to form a new archetype – initial called ‘Adverse Reaction (AllergyIntolerance)‘ and later, the ‘Adverse Reaction (FHIR/openEHR)’ archetype – with the intent of conducting a series of joint FHIR & openEHR community review of the combined model and at the end of the process generating a FHIR resource AND an openEHR archetype with matching, clinically verified content.hl7.jpg

In August 2014 the first joint openEHR/FHIR review was carried out, with myself (@omowizard, AU, openEHR), Ian McNicoll (@ianmcnicoll, UK, openEHR), Graham Grieve (@GrahameGrieve, AU, FHIR) & Russ Leftwich (@DocOnFHIR, USA, HL7 Patient Care/FHIR) as editors. Nasjonal IKT forked the archetype into the Norwegian CKM at the conclusion of that process.


There was a subsequent joint review between openEHR & FHIR that followed, only rather than the few weeks I had anticipated, we had to wait until the FHIR community completed a full FHIR ballot. This blew out the review period to 7 months for our work.


This really highlights the need to separate the ballot/review process for clinical artefacts like FHIR resources and archetypes from balloting process of complete technical standard or specification within a typical standards organisation. If we use this same glacially slow process for the governance of clinical artefacts then it will take decades to achieve high quality shared clinical models.

And one HL7 participant contacted me and said it would be impossible for them to respond to the archetype review in less than 6 months. <facepalm here>. Just for perspective, our typical review round is open for 2 weeks and it takes anywhere from 10 minutes to 30 minutes for most participants to record their contribution.

But we waited… and fed the FHIR ballot comments back into the next archetype iteration. There were not that many! And then we sent it out for the next review – and this time the Norwegian CKM community participated as well. The Norwegian CKM team (led by Silje Ljosland Bakke, @siljelb, & John Tore Valand, @Jtvaland) translated the archetype into Norwegian &  ran a slightly shorter review period, contributing the collective feedback into the international review.

We did this simultaneous review across the FHIR, international and Norwegian communities twice – once in July 2015 and another in November 2015. One of these reviews resulted in the renaming of the archeytpe  concept to ‘Adverse reaction risk’.


At the end of the November 2015 review round, the editors found that there was a consensus reached amongst the participants. In the international CKM we removed the FHIR-specific components and published the content of the ‘Adverse reaction risk’ archetype. The publication status of original archetype was simultaneously changed in the CKM  to rejected – this rejected archetype remains in the international CKM as part of the provenance/audit trail for the published archetype.


The Norwegian CKM has now taken that international archetype and published it within their CKM and under their own governance. The archetypes are semantically aligned.

The FHIR resource has evolved in keeping with the archetype changes. To be completed honest I’m not sure if the final, published openEHR archetype has been reflected back into the latest FHIR resource, but there is no doubt that there certainly the great majority of the two artefacts are aligned due to the joint review process.


The archetype that has finally been published started with the brain dump of a single clinical informatician. At this point in its journey this archetype alone has been shaped by:

  • 13 review rounds
  • 221 review contributions
  • 92 unique individuals
  • 16 countries (top 3 being AU, NO & US)

This has been a very significant block of international work. Getting any kind of consensus on such a clinically significant artefact across different jurisdictions, standards organisations and diverse requirements has not been easy. But we have experienced a great generosity of spirit from all who contributed their time, expertise and enthusiasm to capture an open specification for a single piece of clinical knowledge that can be re-used by others, and potentially improved even more over time.

This is what the published Adverse reaction risk archetype looks like today:


The detail and thought behind each data element and example is significant. Yet we know it is not perfect, nor ‘finished’.

No doubt we will identify new requirements or need to modify it. This journey will then start its next phase…

If you have identified additional requirements… If you disagree with the model…  then register and contribute to the community effort now by registering on the international CKM and make a change request or start a discussion thread.



The art of Clinical Lists

Keeping a clinical list up-to-date in a local EHR current is not a trivial task. Keeping it up-to-date and accurate in a shared environment – well… Read on. It is not easy.

The classic lists are:

  • Allergies/Adverse Reactions;
  • Problem/Diagnosis;
  • Medications;
  • Procedures; and
  • Family History.

At very least, in a shared environment we need common data models and curation, usually by a trusted physician in close consultation with the patient – technical and human factors. For example, the only person who *really* knows what medicines the patient is taking, is the patient themselves. Without the patient front and centre, involved and contributing, a true ‘current’ medicine list is impossible.

And so the data that underpins an accurate list is critical. I’ve already posted once about this – Unambiguous Data: Positive Presence, Positive Absence. The openEHR preferred approach is to record the positive presence of information AND the positive absence of information separately, using specific archetypes for each purpose.

If we use Adverse Reactions as a working example (which we will assume includes allergies, hypersensitivities and intolerances as well), we need to be able to be certain about each of the following types of data:

  • Following enquiry or investigation, we need the ability to record explicit and general statements about the the absence of any adverse reactions, allergies, hypersensitivities or intolerances in the patient’s history to date, using the EVALUATION.exclusion-adverse archetype. For example, “No known adverse reactions” which is only accurate and up-to-date at the exact time of recording. A split second after recording, it may be out-of-date as an adverse reaction to an administered medicine might occur immediately, thus rendering the statement obsolete and triggering recording of the patient’s first ever reaction to a medication. Generic exclusion statements should be reviewed regularly and this can be prompted by the ‘Last updated’ data element in each archetype. Some would argue that generic exclusions should only be recorded in event-based data (for example, as part of a consultation note) as it is only accurate at the time of recording, but it depends on how the persistent lists are managed in the clinical system.
  • Following enquiry or investigation, again, the ability to record explicit and specific statements about absence, using the same EVALUATION.exclusion-adverse archetype. For example, “No known adverse reaction to Penicillin”. As above, regarding accurate only at the time of recording. Again, specific exclusion statements should be reviewed regularly and this can be prompted by the ‘Last updated’ data element in each archetype. Some would argue that this should only be recorded in event-based data as it is only accurate at the time of recording, but it depends on how the persistent lists are managed in the clinical system. As above, some would argue that specific exclusions should also only be recorded in event-based data.
  • Following the occurrence of an adverse reaction from any physiological mechaism, the ability to record specific inclusion statements about the explicit knowledge that a reaction to a known medicine or class of medicines has occurred – no matter if minutes ago, years or decades. For example, a rash in response to administration of Penicillin, using the EVALUATION.adverse_reaction archetype – typically recording the name  or class of the medicine, substance or agent plus supporting details about the reaction manifestation.

Thus, three types of data:

  1. General exclusions
    • No past problems or diagnoses
    • No relevant surgical history
    • No significant family history
  2. Specific exclusions:
    • No history of diabetes
    • No previous appendicectomy
    • No family history of diabetes
  3. Inclusions
    • Diagnosis of diabetes mellitus type II
    • Appendicectomy, 1998
    • Family history of heart disease, epilepsy

Each of these kinds of information clearly needs to be recorded and persisted in an electronic health record as per each of the examples above. A clinical list may be updated automatically by the computer system where it is clinically absolutely safe to do so, but must be curated and updated by a clinician expert where in situations where clinical knowledge or discernment is required.

It could be argued that simple business rules in a standalone clinical system could ensure that in a new record for a new patient, and where no entries are stored regarding the presence or absence of any adverse reactions, that the system:

  • can reasonably infer no information is available;
  • should display some kind of clear message to the clinician that there is “no information available”, neither present or absent, about the patient’s adverse reaction status; and
  • prompt the clinician to record appropriate explicit statements of presence or absence during the next clinical consultation.

‘No information available’ is quite a different statement to previously noted assertion that there are “No known adverse reactions” which follows careful inquiry and evaluation by a clinician.

It is probably good practice that a new patient record in a clinical system should start with a default statement such as “No information about adverse reactions is available”, rather than just inferring that no information is available based on no recognised data entries about explicit presence or absence. This is therefore the fourth type of list that is relevant to persistent clinical lists

There is a ‘grey zone’ when information needs to be exchanged between systems or aggregated into a central system. To receive in a message an explicit statement that there is ‘no information about adverse reactions available’ enables the receiving system to understand the adverse reaction data situation unambiguously and reflect that in the receiving record appropriately. If no explicit information is sent in the message, the receiving system does not actually know if that reflects a true absence of data; a positive exclusion of data; or that there is an error in the message. The apparent absence of data for the clinician, no matter if not available or missed in error, will not change their clinical practice – they will still need to ask about adverse reactions prior to prescribing. If the patient can provide the information, then inappropriate medicine administration is averted, but if not, then there is a potential clinical safety risk to the patient.

Other arguments for explicitly stating that there is no known information occur in situations where the patient is not able to answer, for example if unconscious. Clinical management will still proceed, based on whatever information can be gleaned and with the clinicians needing to be prepared for anything! A slightly more complicated scenario ,which is medicolegally significant, occurs when an uncooperative patient, will benefit from the recording/exchange of ‘no information available’ as data plus the corresponding ‘reason for no information’. Similarly, other knowledge related activities, such as clinical decision support, will also benefit from explicit and consistent use of statements of inclusions/presence, exclusions and absence/no information.