#Uncomplexication of Clinical Infostructure

Don’t tie yourself up in knots about the title. There was a story behind it, and it was more interesting than ‘an overview of clinical modelling’. No matter, the content is what matters!

The presentation was delivered today to the Arctic Conference on Dual-Model based Clinical Decision Support & Knowledge Management. The only downside was that the conference was in Tromsø, Norway and I was safely at home in Melbourne.

My task: to present an overview of modelling, current status and lessons learned.

The presentation turned into a bit of a rambling story of my engagement with clinical modelling and experiences with archetypes over the last decade. I was lucky enough to be there at the beginning of the first serious archetype-building activity in the UK in 2007, and to participate in the subsequent evolution of the openEHR modeling community and methodology – now over 1100 people from 80+ countries!. So this presentation is a bit of a ramble down memory lane as well as providing a bit of a summary of how we got here, where we are and what can we see coming?

In particular, I can now safely say that while at times I was ready to jump ship and use the life buoy pictured in the presentation, that is no longer the case.

The Archetype ‘Elevator Pitch’

elevatorI’ve been asked for the classic ‘elevator pitch’: How does a non-openEHR expert, non-geek explain the notion of developing a library of archetypes to their colleague or boss?

Here goes for the 2 minute version:


WHAT: The development of a clinician-led library of clinical content specifications can be a foundation piece of national health IT infrastructure, intended to support interoperability of all granular health information, rather than a limited selection of messages or documents as we have now.

WHY: To ensure that the patterns for health data that is being collected and exchanged is consistent and broadly re-used across the jurisdictional eHealth environment, no matter whether it be organisational, regional, national or international. This will provide a firm basis for establishing technical and semantic interoperability. For example, the recording core of clinical concepts such as diagnosis or medication orders will be consistently captured, stored and exchanged across organisations, vendor systems and for a full variety of clinical purposes ranging from home data collection, through primary and secondary care, research and population health.


    • The clinical content itself should be defined, reviewed and declared fit for purpose by clinicians and domain experts, not just technicians.
    • The development and governance process should be managed by expert health informaticians.
    • The end users will be the vendors and organisations.
    • The overall project governance should be led by clinical professional groups +/- a potential consortia with appropriate political/industry groups etc.

HOW: Utilise the current archetype methodology and Clinical Knowledge Manager tool.

RESULT: The outcome of such a program of clinical content standardisation would provide a long term and sustainable approach to developing, maintaining and governing jurisdictional health data specifications. It could form the backbone for a national health data strategy and is a key way to ensure that clinicians contribute their expertise to jurisdictional eHealth programs.


Then perhaps consider extending it to a 5 minute version with some practical context:

In the short term, the existing archetypes in any or all of the national or international CKMs would be leveraged as a starting point. Many of these archetypes are quite mature as the result of implementations. For example, as the basis for the CDA specifications used by the Australian PCEHR and recent NT Health development. They cover core clinical concepts that are used in most settings and across a range of clinical activities.

Each clinically agreed archetype could be utilised as the jurisdictionally agreed pattern/specification for a given clinical concept – a public ‘line in the sand’ if you like. In the early days vendors could use these archetypes as a pattern to transform their existing databases to in order to exchange data between systems. Over time, vendors may choose to gradually re-engineer their existing/legacy databases to directly conform to the models, thus supporting ease of direct interoperability and reduce the need for vendors to manage clinical content independently.

For new vendors or projects, this library of clinical data patterns could provide the basis for ready ‘approved’ data collections (ie approved by the clinicians participating in the development  and review process), rather than reinventing the wheel with their own proprietary data elements and adding further to the creation of data silos.

Funding sources would need to be identified, probably most often from a public source, keeping in mind that the outcomes can then be made publicly and freely available under a Creative Common licence. The outcome would be a national library/repository of free clinical content specifications, based on an open standard, and able to be used as the basis for all national eHealth work – design an archetype once, reuse in any/all clinical contexts, and the data that is created using these archetype patterns will be sharable and interoperable.

Any suggestions for refinements?

Know your data?

Recently I was reminded of some work we did a number of years ago. It involved a large research database, painstakingly collected over 20 years.

The data was defined across a number of specialisations within a single clinical domain and represented in 83 data dictionaries stored in an Excel spreadsheet.

Data was collected based on a series of questionnaires, and we were told that successive data custodians had, true to human nature, made slight tweaks and updates to the questionnaires on multiple occasions. The data collected was actually evolving!

The only way to view the data was to open each of the 83 spreadsheets, painstakingly, one by one.

We were engaged to create archetypes to represent both the legacy data and the data that the research organisation wished to standardise to take forward.

So the activity of converting these data dictionaries – firstly to archetypes for each clinical concept, and then representing each data dictionary as a template – resulting in considerable insight into the quality and scope of the data that hadn’t been available previously.

For example, the mind map below is an aggregation of the various ways that questions were asked about the topic of smoking.

SmokingInterestingly, what it showed was that no one individual in the organisation had full oversight of the detailed data in all of the data dictionaries.The development of the archetypes effectively provided a cross section of the data focusing on commonality at a clinical concept level and revealed insights into the whole data collection that was a major surprise to the research organisation. It triggered an internal review and major revision of their data.

Some of the issues apparent in this mind map are:

  • A number of questions have been asked in slightly different ways, but with slight semantic variation, thus creating the old ‘apples’ vs ‘pears’ problem when all we wanted was a basket of apples;
  • Often the data is abstracted and recorded in categories, rather than recording the actual, valuable raw data which could be used for multiple purposes, not just he purpose of the rigid categories;
  • Some questions have ‘munged’ two questions together with a single True/False answer, resulting in somewhat ambiguous data; and
  • Some questions are based on fixed intervals of time.

No doubt you will see other issues or have more variations of your own you could share in your systems.

And we have repeatedly seen a number of our clients undergo this same process, where archetypes help to reveal issues with enormously valuable data that had previously been obscured by spreadsheets and the like. The creation of archetypes and re-use of archetypes as consistent patterns for clinical content has an enormous positive impact on the quality of data that is subsequently collected.

And while harmonisation and pattern re-use within one organisation or project can be hard enough, standardisation between organisations or regions or national programs or even internationally has further challenges. It may take a while to achieve broader harmonisation but the benefits of interoperability will be palpable when we get there.

In the meantime the archetypes are a great way to trigger the necessary conversations between the clinicians, domain experts, organisations, vendors and other interested parties – getting a handle on our data is a human issue that needs dialogue and collaboration to solve.

Archetypes are a great way to get a handle on our data.