Engaging clinicians: building EHR specifications

There is a methodology that is pragmatically evolving from my experience in openEHR clinical modelling work over the past few years. It has developed in a rather ad hoc way, and totally in response to working directly with clinicians. The simplicity and apparent effectiveness – both for me and the clinicians involved - continues to surprise me each time I use it. The clinical content specifications for specialised health records and care plans that we are building are being developed with a sequence of expert input and clinical verification:

1. Identifying the clinical requirements and business rules in conjunction with a selected initial domain expert group;
2. Broader abstract verification of the notion of ‘maximal data set’ for ‘universal use case’ during formal archetype review cycles;
3. Contextual validation during template review by ‘on-the-ground’ clinicians; and finally, although to a lesser degree,
4. Validation during mapping and migration of legacy data.

With each project I am refining this process. Starting off a project with face-to-face meetings has been a ‘no brainer’ – after all, it takes a while for everyone to understand the get the idea of what we are doing. However after initial workshops, pretty much everything else can be done via web conference, online collaboration via CKM and email.

I find the initial workshops are usually greatly satisfying. Within hours we can be creating two outputs – a mind map that reflects the clinicians evolving conversation about their requirements and, in parallel, an equally agile template of clinical content specifications that can be verified by the clinicians in real time.

The mind map is displayed on a shared screen or via a data projector and acts as a living document, evolving as we talk through the clinical requirements, and identify the complexities, dependencies and relationships of all the components. The final mind map may be surprisingly different to how it started, and at the end of the conversation, the clinicians can verify that what they’ve said if accurately reflected in the mind map. It is an open source tool, so we can also share this around after the workshop for further comments.

Most recently I have begun building a template on the fly during the workshop, using any existing archetypes that are available, and identifying gaps or the need for new archetypes on the mind map as we go. In this way we are actually building the content specification in front of the clinicians as well. They get an understanding of how the abstract discussion will actually shape the resulting EHR content and they can verify it as we gradually pull it together. The domain experts are immediately equipped to answer the question: “Does this specification match what you have been telling me you do in practice?”

This methodology seems to bring the clinicians along with us on the clinical modelling journey, and most are able to understand at least some of the implications of some of our requirements discussions and, in particular, the ‘shape’ of the data that we can collect. It is a process seems to suit the thinking process of many clinicians and the overwhelmingly consistent feedback from recent workshops is that they have all actually enjoyed the experience and want to know what are the next steps for them to be involved. So that’s certainly a winner.

And the funders/jurisdictions are anecdotally confirming for me that they are finding that this approach is supporting higher quality specifications in a much shorter time frame.

For example, at a project kickoff workshop for a new project recently, in two days we:

• developed a series of mind maps capturing a consensus view of the clinical requirements and business processes;
• identified all the archetypes required for the entire project, including those that existed and were ‘fit for use’, those that needed some extension to meet requirements and new archetypes that needed to be created;
• identified sources of information or mind mapped the requirements for each new archetype identified; and
• built 3 templates comprising all of the existing archetypes available from a number of sources – the NEHTA CKM http://dcm.nehta.org.au/ckm/, the openEHR international CKM http://www.openehr.org/ckm/ and local drafts that I had on my own computer. For a number of the new archetypes we also collectively identified source information that would inform or be the basis for the archetype development.

All of this described above took 8 medical practitioners clinicians away from their everyday practice for only 1-2 days, each according to their availability. Yet it provided the foundation for development of a new clinical application.

Then I go home. Next steps are to refine the mind map, modify/update/specialise any archetypes for which we have identified new requirements and build the new ones. And in parallel start the collaborative process through a CKM project to ensure that existing and modified archetypes are ‘fit for (our project’s) purpose’, and to upload and initiate reviews on the new draft archetypes.

All work to progress these archetypes to maturity (ie aiming for clinical consensus) and then validate the templates as ready for handover to the implementers can be done online, asynchronously and at a time convenient to the clinicians work/life balance!

I live over 2000 kilometres away from these clinicians. Yet the combination of web conference and CKM enables us to operate as an ongoing collaborative team. It seems to be working well at the moment... No doubt I'll continue to learn how to do it better.

The Archetype Journey...

I'm surprised to realise I've been building archetypes for over 7 years. It honestly doesn't feel that long. It still feels like we are in the relatively early days of understanding how to model clinical archetypes, to validate them and to govern them. I am learning more with each archetype I build. They are definitely getting better and the process more refined. But we aren't there yet. We have a ways to go! Let me try to share some idea of the challenges and complexities I see…

We can build all kinds of archetypes for different purposes.

There are the ones we just want to use for our own project or purpose, to be used in splendid isolation. Yes, anyone can build an archetype for any reason. Easy as. No design constraints, no collaboration, just whatever you want to model and as large or complex as you like.

But if you want to build them so that they will be re-used and shared, then a whole different approach is required. Each archetype needs to fit with the others around it, to complement but not duplicate or overlap; to be of the same granularity; to be consistent with the way similar concepts are modelled; to have the same principles regarding the level of detail modelled; the same approach to defining scope; and of course the same approach to defining a clinical concept versus a data element or group of data elements… The list goes on.

Some archetypes are straightforward to design and build, for example all the very prescriptive and well recognised scales like the Braden Scale or Glasgow Coma Scale. These are the 'no brainers' of clinical modelling.

Some are harder and more abstract, such as those underpinning a clinical decision support system of orders and activities to ensure that care plans are carried out, clinical outcomes achieved and patients don't 'fall through the cracks' from transitions of care.

Then there are the repositories of archetypes that are intended to work as single, cohesive pool of models – each archetype for a single clinical concept that all sits closely aligned to the next one, but minimising any duplication or overlap.

That is a massive coordination task, and one that I underestimated hugely when we embarked on the development of the openEHR Clinical Knowledge Manager, and especially more recently, the really active development and coordination required to manage the model development, collaboration and management process within the Australian CKM – where the national eHealth program and jurisdictions are working within the same domain of models, developing new ones for specific purposes and re-using common, shared models for different use cases and clinical contexts.

The archetype ecosystems are hard, numbers of archetypes that need to work together intimately and precisely to enable the accurate and safe modelling of clinical data. Physical examination is the perfect example that has been weighing on my mind now for some time. I've dabbled with small parts of this over the years, as specific projects needed to model a small part of the physical exam here and there. My initial focus was on modelling generic patterns for inspection, palpation, auscultation and percussion – four well identified pillars of the art of clinical examination. If you take a look at the Inspection archetype clinicians will recognise the kind of pattern that we were taught in First Year of our Medical or Nursing degrees. And I built huge mind maps to try to anticipate how the basic generic pattern could be specialised or adapted for use in all aspects of recording the inspection component of clinical examination.Over time, I have convinced myself that this would not work, and so the ongoing dilemma was how to approach it to create a standardised, yet extraordinarily flexible solution.

Consider the dilemma of modelling physical examination. How can we capture the fractal nature of physical examination? How can we represent the art of every clinician's practice in standardised archetypes? We need models that can be standardised, yet we also need to be able to respond to the massive variability in the requirements and approach of each and every clinician. Each profession will record the same concept in different levels of detail, and often in a slightly different context. Each specialty will record different combinations of details. Specialists need all the detail; generalists only want to record the bare basics, unless they find something significant in which case they want to drill down to the n^th degree. And don't forget the ability to just quickly note 'NAD' as you fly past to the next part of the examination; for rheumatologists to record a homunculus; for the requirement for addition of photos or annotated diagrams! Ha – modelling physical examination IS NOT SIMPLE!

I think I might have finally broken the back of the physical examination modelling dilemma just this week. Seven years after starting this journey, with all this modelling experience behind me! The one sure thing I have learned – a realisation of how much we don't know. Don't let anyone tell you it is easy or we know enough. IMO we aren't ready to publish standards or even specifications about this work, yet. But we are making good, sound, robust progress. We can start to document our experience and sound principles.

This new domain of clinical knowledge management is complex; nobody should be saying we have it sorted...

Archetype quality I

Up until recently clinical content models, such as archetypes, have been regarded as a novelty; watched from the sidelines with interest from many but not regarded as mainstream. However now that they are increasingly being adopted by jurisdictions and used in real systems, modellers need to change their approach to include processes, methodologies and quality criteria that ensure that the models are robust, credible and fit for purpose. There has been some work done identifying quality criteria for clinical models but there is no doubt that establishing quality criteria for clinical content models is still very much in its infancy:

• Kalra D, Tapuria A, Freriks G, Mennerat F, Devlies J (2008). Management and maintenance policies for EHR interoperability resources [36 pages] (Q-REC Project IST 027370 3.3 ). The European Commission: Brussels. (Last accessed May 28, 2011)

• There has been some slowly progressing work in ISO TC 215 - ISO 13972 Health Informatics: Detailed Clinical Models. Recently it has been split into two separate components, not yet publicly available:
• Part 1. Quality processes regarding detailed clinical model development, governance, publishing and maintenance; and
• Part II - Quality attributes of detailed clinical models

Most of the work on quality of clinical models has been based largely on theory, with few groups having practical experience in developing and managing collections of clinical models, other than in local implementations.

In 2007, Ocean Informatics participated in a significant pilot project. The recommendations were published in the NHS CFH Pilot Study 2007 Summary Report. My own analysis, conducted in December 2007, revealed that there were 691 archetypes within the NHS repository. Of these, 570 were archetypes for unique clinical concepts, with the remainder reflecting multiple versions of the same concept. In fact, for 90 unique concepts there were 207 archetypes that needed rationalisation – most of these had only two versions however one archetype was represented in five versions! We needed better processes!

Towards the end of 2007 a small team within Ocean commenced building an online tool, the Clinical Knowledge Manager to:

• function as a clinical knowledge repository for openEHR archetypes and templates and, later, terminology subsets;
• manage the life-cycle of registered artefacts, especially the archetype content – from draft, through team review to published, deprecated and rejected. Also terminology binding and language translations;
• governance of the artefacts.

In July 2008 we started uploading archetypes to the openEHR CKM, including many of the best from the NHS pilot project. Over the following months we added archetypes and templates; recruited users; and started archetype reviews. All activity was voluntary – both from reviewers and editors. Progress has thus been slower than we would have liked and somewhat episodic but provided early evidence that a transparent, crowd sourced verification of the archetypes was achievable.

In early 2010, Sweden's Clinical Knowledge Manager had its first archetypes uploaded.

In November 2010, a NEHTA instance of the CKM was launched, supporting Australia's development of Detailed Clinical Models for the national eHealth priorities. This is where most collaborative activity is occurring internationally at present.

In this context, I have pondered the issues around clinical knowledge governance now for a number of years, and gradually our team has developed considerable insight into clinical knowledge governance – the requirements, solutions and thorny issues. To be perfectly honest, the more we delve into knowledge governance, the more complicated we realise it to be – the challenge and the journey continues; a lot is yet to be solved :)

It is relatively easy to identify the high level processes in the development of clinical knowledge artifacts, each of which requires identification of quality criteria and measurable indicators to ensure that the final artifacts are fit for purpose and safe to use in our EHR systems. The process is similar for both archetypes and templates; plus the Requirements gathering and Analysis components are applicable to any single overarching project as well.

For archetypes:

The harder task is that for each of these steps, there are multiple quality criteria that need to be determined, and for each criterion it will be necessary to be able to assess and/or measure them through identifiable quality indicators.

Ideally a quality indicator is a measurement or fact about the clinical model. In some situations it will be necessary to include additional assessments manually performed by qualified experts.

If an indicator can be automatically derived from the Clinical Knowledge Manager (CKM), it ensures that up-to-date assessments of the models are instantly available as the models evolve (such as this Blood Pressure archetype example), and more importantly, without reliance on manual human intervention. However while assessments that do need to be assessed by an expert human – for example, compliance to existing specifications or standards – add valuable depth and richness to the overall quality assessment, they also add a vulnerability due to the need for skilled human resources to not only conduct the assessment, but to apply consistent methodologies during the assessment; these will be much more difficult to sustain.

Assessment of whether the indicators actually satisfy the quality criteria should also ideally be as objective as possible, however our reality is that it will probably more often be subjective and vary depending on the nature of the archetype concept itself. The process cannot be automated, nor can there be a single set of indicators or criteria that will determine the quality of every archetype. We need to ensure appropriate oversight to archetype development, ensuring that a quality process has actually been followed and utilise quality indicators to determine if the quality criteria have been met - on an archetype by archetype basis.

And it continues on...

Clinical Knowledge Governance in a Web2.0 world

Establishing and maintaining the quality of clinical knowledge is clearly the domain of the expert clinicians themselves. This is a broadly accepted principle for management and governance of the traditional clinical knowledge artefacts. However this assumption needs re-evaluation when we need to establish quality, safety and ‘fitness for purpose’ of computable clinical knowledge artefacts that populate Electronic Health Record (EHR) systems. Clinical knowledge has traditionally been created and shared through formal publication and peer-review processes that have been adjudicated by committees of clinical experts. Those expert committees have been appointed through a credentialing process and have had jurisdiction and oversight over the entire publishable content – ‘the buck stops here’. Before the rise of the internet, face-to-face meetings have been where most of the committee work has been done, and the process has most often been slow and expensive but delivered good quality publications. The opportunity cost to each participating clinician has been high with recurring interruptions to their clinical activities. Revision of those publications at a later date repeats this process, taking considerable time, money and resources.

Certainly in recent times, there have been more electronic tools to support these processes – email, teleconferences and videoconferences have improved the logistics of the process, but essentially the process remains unchanged.

Given the increasing traction of electronic health records, there is a parallel movement to develop and share computable clinical content definitions that can be created, published and implemented by: multiple clinical disciplines; generalists and specialists; primary, secondary and tertiary care organisations; population health planning; clinical researchers; and knowledge-enabled systems such as clinical decision support applications. They need to be language independent and translatable, in order to transport health information across national boundaries.

These kind of computable clinical models need the input from many experts, clinicians and others, to ensure that they are not only clinically appropriate but support safe data usage in our EHRs. These models are increasingly being created with ambitious goals – to create once and then re-use many times. In this case, the scope of the models needs to include requirements of the full breadth of clinical professions and specialties. Clinicians remain key to their development and publication, but they also require input from:

• Other domain experts – non-clinicians who will want or need to use these same models for non-clinical purposes such as secondary data use;
• Informaticians – who understand how these models will be the basis for recording health information, exchange between systems, reporting, data aggregation and how knowledge-based activities.
• Terminologists – to ensure that the models will integrate with appropriate terminology value sets;
• Technicians – who will advise on the technical impacts of these models in systems; and
• Translators – who will ensure that the clinical information is faithfully transformed from one language to another.

Examples of these computable clinical content models are many and varied. There are open source and proprietary models of many different flavours and philosophies – archetypes, templates, detailed clinical models etc. In recent years there are increasing attempts to broaden the input to the creation of these models and even to start to standardise them – regionally, nationally and even internationally. In this new paradigm, the traditional approaches to clinical content development, management and governance are no longer sufficient.

When the full breadth, depth, and dynamic nature of clinical knowledge is considered, it is not feasible to be able to appoint an overarching committee or board who would be capable of providing final ‘sign off’ about the clinical ‘correctness’ for any one model. Each clinical knowledge model will require input from varying groups of expert clinicians, terminologists, informaticians and technicians, depending on the clinical knowledge artefact under review. We need to find innovative approaches to online and asynchronous collaboration of a wide range of individuals from diverse backgrounds, expertise and geographical location to ensure these models are suitable for use in clinical systems.

Traditional standards bodies, such as ISO, CEN or HL7 have well defined and fixed processes in place for managing the lifecycle of technical standards through a formal balloting process with registered member bodies. These are definitely not suitable for managing and governing an evolving and dynamic clinical content specification library.

There has been some early work on establishing abstract archetype quality criteria by QREC and more recently, ISO TC 215 Working Group 1 has established a new work item 13972, which is establishing “Quality criteria for detailed clinical models”.  However, neither of these are able to establish the quality of archetype instances for real world use.

I believe that HL7 is working to establish a Template Repository. As I understand it, it will operate as an indexing service to templates that will be stored on distributed servers. Others may be able to provide more details.

Other work is no doubt occurring, of which I am not aware. And of course, each clinical system has to establish the clinical content that it will use in its own proprietary information model. In the US alone, with thousands of clinical software vendors, this means that we have thousands of different computable versions of essentially identical clinical content, but none of it interchangeable without mappings or transformation – what a huge waste of resources! We need to change this blinkered way of thinking.

The openEHR Clinical Knowledge Manager (CKM) is the only online clinical knowledge resource, to my knowledge, which is supporting collaboration by clinicians, other domain experts, informaticians, technicians and translators to achieve consensus about quality and safety in clinical content models – in this instance, openEHR archetypes.  I am directly involved in the development of this tool, and am active as an Editor facilitating the review process of the archetypes – I have described it in previous blog posts.

While CKM is one of the early Web2.0 approaches to collaborating about clinical content models, I am sure there will be more over time. I have spoken to a number of Knowledge Management experts, and to my surprise no-one has yet been able to point me to similar tools, resources establishing quality within a Web2.0 environment. Are we really such pioneers? Surely there are similar approaches in other knowledge domains?

No matter. There is no doubt that we are only in the early stages of a transformation in clinical knowledge governance and we have a lot to learn about how to establish quality criteria in a Web2.0 environment. I’ll post some thoughts in my next post...

openEHR - the World’s Record

I wrote this overview of openEHR way back in 2007, and it was published in PulseIT (Sydney, Australia) [Internet], pp50-55, Issue 6, November 2007. The online publisher has removed access to it, so I've posted it here.

The principles remain unchanged.  I've updated some links, and indicated some minor parts that are a little out-of-date. My previous posts, What on earth is openEHR & Connect with openEHR provide up-to-date stats and links.

In a world where connectivity reigns, our health information is largely still caught up in silos and, in the main, is not shareable by clinicians.  Shared electronic health records (SEHRs) are increasingly needed to provide timely, comprehensive and coordinated healthcare.  Over many years there have been ongoing and thorough attempts to achieve the sharing of health information in order to support the improvement of health outcomes, but this incremental approach, gradually building on previous experience, has not been wholly successful.  Progress has been made, but despite enormous investment and resources, the solution has been more difficult than most ever anticipated.  Healthcare provision does not seem to fit into the same kind of data sharing model as has been successful in other domains such as banking or financial services.

In order to support interoperability of health information SEHRs need clinical content to be standardised.  This assists the move to standardised communication of complex health information between systems and the opening of these vertical domain and organisational silos, allowing accurate and semantically computable health information flow.  Inevitably the shared clinical content specifications begin to influence the information models within clinical applications.  openEHR is an electronic health record architecture based on many years of research and development, and designed to work in partnership with all vendor systems, organisations and providers to facilitate semantic interoperability of health information.  It is a comprehensive and transformational solution which applies to the capturing and sharing of information from the most complex and dynamic knowledge domain – health.

Why are Health Records so difficult?

The interoperability of health information problem is twofold – firstly, the evolving clinical needs and requirements for a SEHR are difficult to pin down, and secondly the technical issues related to a SEHR solution.

Why are clinical requirements so difficult to capture and transform into an electronic health record (EHR)?  Certainly no-one will argue that our experience of healthcare delivery is changing – hospital in the home projects; supported self-management; patient requests for access to GP and hospital clinical records; personal health records; service coordination; clinical care plans; preventive health priorities...  The list goes on, mirroring the rapidly evolving change in clinical EHR requirements needed to support these new paradigms of healthcare delivery.  These new ways of delivering care require a coordinated approach – healthcare providers need to collaborate efficiently in real time, which is not adequately supported by traditional methods such as meetings and phone calls.  We really need an interoperable and integrated patient record in which all healthcare providers can participate, within a framework providing governance, authorisation and security measures.

Clinical knowledge domain is very complex and dynamic, so any clinical system created using traditional software development methods (embedding the clinical knowledge directly into proprietary application code and databases) has an uphill battle to deal with the changes.  Considering the time it takes for a clinical application to be developed, it may already be out-of-date at its launch!  The inherent difficulties in updating knowledge structures that are hard-coded into the application and database inevitably lead to a gradual deterioration of the integrity of the system.

Clinical decision support is another difficult area – there is a lot of discussion about it, but there is very little real computerised decision support happening in practice.  Certainly, each clinical system has some that is custom built – usually limited to allergy and drug interaction alerts and a reminder system – but there has been very little general progress in this area.  Why is clinical decision support (CDS) still largely found only in the academic domain and not implemented in production clinical systems?  We already have clinical guidelines and other resources which are approved and used in paper formats – why are they not integrated in our clinical desktop applications?  The answer is multi-factorial.  Things that have blocked progress include ownership and sharing of intellectual property, licensing problems etc, but the main issue is to do with practical implementation and cost.  An approved guideline can be transformed into some proprietary computable format, but then it has to be able to integrate into every other clinical system – lots of interfaces have to be built, paid for and maintained.  This is a nightmare.  Another approach is for each system developer to take a guideline and integrate it into their system as best they can.  This introduces variability and the potential for significant quality and liability issues to arise.

Silos of proprietary information cannot talk to each other without mappings and necessary assumptions.  Such transformations inevitably compromise the quality and integrity of the information being shared, thus creating more threats to patient safety.  It is only through use of a common logical clinical content model reflected in our EHRs and CDS systems that the guidelines can be created once according to this agreed model and can be integrated meaningfully and accurately with the clinical information systems to give us quality clinical decision support at the point of care.

From a technical point of view, the current reality around the world is that the majority of vendors are competing to build the greatest and ‘best of breed’ clinical software applications, but all are doing it in their own proprietary way.  The software may have a rich functionality and a great user interface.  It may do a great job in the clinic, hospital or network on which it is installed, but how does it share clinical data?  Some software applications may be able to share with the same system in another geographical location because they share a common data structure, but cannot easily share with the system of another vendor.

Current clinical applications can usually send or receive point-to-point messages using a format which is known and agreed, and based on a standard such as the Health Level 7 (HL7) version 2.  The software may also incorporate a terminology to assist in capturing and storing common clinical concepts.  However, there is a common misconception that if we simply have a messaging standard paired with a terminology, then we have ‘achieved interoperability’.

A New Paradigm for Electronic Health Records

Most of us mistake the software program for the electronic health record; in fact in the USA the word EHR means a clinical application.  The openEHR approach asserts that it is not the application, but rather the data, or health information, which makes up the health record.  And further, that it is a common and agreed structure of that data is what makes the electronic health record of any use for computing in health.

In order for two clinical systems to be able to share health data unambiguously, such that clinicians can read it and the computer can compute with it, more is required.  This semantic, or knowledge-level, interoperability, based on a common and coherent clinical model, is an absolute requirement for truly shareable EHRs and valuable complementary functionality such as clinical decision support and workflow/care planning.  Further, it is only when this clinical content structure is agreed at a local, regional, national or international level, that true semantic interoperability can occur at each of these levels.  The broader the level of clinical content model agreement, the broader the potential for semantic health information exchange. While an increasing number of health informatics experts agree on this view of the problem, incremental work towards EHR standards based on messaging paradigms, such as HL7 v3, have only had limited degrees of success.  At an international level, there is increasing concern that this incremental approach to SEHRs may not be able to solve the issues of semantically interoperable EHRs.  Remember that Einstein argued: “We can't solve problems by using the same kind of thinking we used when we created them.”

We need semantic interoperability for our SEHRs and in order to achieve this we need a transformational change.  We need to use a different paradigm.  The openEHR paradigm has been collaboratively developed, reviewed and refined to realise a collective vision of a high quality, internationally interoperable EHR.

What is semantic interoperability?

Let’s be clear about what semantic interoperability means.  According to Walker et al[1], Level 4 interoperability, or ‘machine interpretable data’, comprises both structured messages and standardised content/coded data.  In practice, it means that data can be transmitted and viewed by clinical systems without need for further interpretation or translation.  This is a basic foundation for a truly shareable EHR and for functionality such as clinical decision support.

For clarity, existing HL7 v2 messages probably fit into Level 3 of Walker’s conceptual framework – ‘Machine organisable data’ which comprises structured messages but unstructured content.  According to Walker, a level 3 message “requires interfaces that can translate incoming data from the sending organization’s vocabulary to the receiving organization’s vocabulary; usually results in imperfect translations because of vocabularies’ incompatible levels of detail.”  We cannot directly compute on a Level 3 message – it requires interpretation or transformation before the computer can use it.  HL7 v3 goes a step further with an underlying semantic model.  The problem has been that using this in systems has proved very difficult.  The HL7 CDA, chosen by Nehta for communication in Australia, bypasses some of the difficulties by at least enabling us to share documents that we can read.  More is needed to achieve semantic interoperability.

What is openEHR?

openEHR[2] is a set of open specifications for an Electronic Health Record (EHR) architecture – but it is not a software application.  Its design purpose is to enable semantic interoperability of health information between, and within, EHR systems – all in a non-proprietary format, avoiding vendor lock-in of data.

All clinical knowledge concepts are captured in a structured way - known as archetypes – outside the software.  The types of archetypes support the recording required for common clinical activities, with some of the key building block archetypes comprising observations, evaluations, instructions and actions.  Data built according to these are stored in an EHR in larger ‘composition’ structures, which have their own archetypes.  Compositions are comparable to a document that results from a clinical event which is performed e.g. a consultation record or a discharge summary.  Archetypes can be simple, such as temperature, blood pressure or diagnosis, or complex, such as capturing the risk to a fetus if the father has a grandmother with Huntingdon’s chorea.  The archetypes contain a maximum data set about each clinical concept, including attendant data required such as: protocol, or method of measurement; related events; and context that is required for the clinical data to be interpreted accurately.  The creation of archetypes and templates is almost purely a task for clinicians – openEHR archetypes put clinicians in the driver’s seat, enabling them to create the breadth, depth and complexity of the health record to suit their needs for direct healthcare provision.

Aggregations of archetypes are combined in openEHR ‘templates’ in order to capture the data-set corresponding to a particular clinical task, such as an ICU discharge summary or antenatal visit record.  When clinicians look at templates, the information contained within them inherently makes sense and doesn’t require significant training for interested clinicians to be able to create templates for their own purposes – be it domain, organisation or purpose specific.  Templates can be used to build generic forms to represent the approximate layout of the EHR in a practical sense, and these can be used by vendors to contribute to their user interface development.

Both archetypes and templates can be linked to terminologies or contextually appropriate terminology subsets that will support appropriate term selection by healthcare providers at the point of data entry.

openEHR is rapidly gaining international momentum, supported by evidence from current high profile implementations of the openEHR specifications such as in the UK NHS Connecting for Health program.

Who is openEHR?

The openEHR Foundation owns the intellectual property of the openEHR architecture specifications.  The Foundation is a not-for-profit company, with the founding partners being University College London (CHIME department) and Australian company, Ocean Informatics. The specifications are the result of over 15 years of research and international implementations, including the Good European Health Record (GEHR), and the collaboration of an international community of people who share a common goal – that of the realisation of clinically comprehensive and interoperable electronic health records to support seamless and high quality patient care.

The registered online community has over 1000 members from 75 countries with many actively participating in debate and contributing to ongoing openEHR development from both technical and clinical perspectives.

What is openEHR used for?

openEHR is a specification for secure, shareable health information and provides a foundation on which to build interoperable, modular software applications.  These, in turn, can support distributed clinical workflow, such as care plans.

The openEHR specification can be implemented in a number of ways:

• Scalable EHRs – from Personal Health Records to small/medium/large organisations to regional or state clinical record systems and on through to National e-Health programs;
• Message-based, web-service, middleware applications; and
• Integrating existing clinical systems, including virtual federation of data for research or public health purposes.

However, while it has much in the way of additional functionality, it is important not to lose sight of openEHR’s raison d'être – semantic interoperability, or more simply, a truly shareable Electronic Health Record.

How is openEHR different?

There are many aspects of openEHR which clearly differentiate it from other EHR models:

1. Open source initiative The openEHR specifications are freely available under an open licence.
2. Separation of the technical and clinical domains The openEHR design is markedly different to traditional EHR development - it is a 2 level information model.  These 2 levels allow a clear separation of the technical reference (i.e. data) model on which software is based from the clinical knowledge itself.  The appeal of this new design is that the technical components of the EHR can be kept quite separate from the dynamic clinical knowledge model.  In practice, the technicians manage just the technical aspects, while the clinicians are critical in the development of the clinical archetypes and templates that shape the nature of their EHR.
3. Purpose-built EHR The design of the reference and archetype models support many unique openEHR features required for robust clinical record keeping, clinical business process and medico-legal compliance, such as:  distributed versioning and merging of EHR records, including audit trails; a strong history and event model for complex observational information; and archetype-driven semantic querying. openEHR designs supports configurable security, anonymous EHRs, fine-grained standards-based privacy, digital signing, and access auditing.
4. Knowledge-enabled – capturing the dynamic and complex nature of health information Clinicians are able to contribute actively and directly to the development of the clinical knowledge models that underpin their EHRs.  Archetypes can be revised and versioned to reflect the rapid and varied changes in health domain knowledge.
5. Terminology agnostic openEHR connects flexibly to any or all terminologies through either archetypes or templates.  Terminologies such as SNOMED-CT are leveraged when used alongside archetypes, with the archetype clinical model providing context to minimise the need for post-coordination and complexity.  Archetypes and terminologies are not competitive but rather complement each other.
6. Semantic querying Archetypes, in combination with terminologies enable powerful possibilities for semantic querying of repository data – whether for individual usage or for population research.  Archetype-based querying enables true longitudinal processing of health data, regardless of the originating system.
7. Language independent There is no language primacy in archetypes; they can begin in any language and be translated to multiple other languages.  Translation enables the same archetype to be used in different countries, with data created in one language to be interoperable with systems developed and used in other languages.  Archetypes are currently available in English, German, Turkish, Dutch, Swedish, Farsi, Spanish and Portuguese.
8. Sustainable reference model – life-long EHRs The openEHR reference model has been rigorously engineered over the past 15+ years as the foundation for a comprehensive health computing platform.  It consists only of generic data types, structures and a small number of generic patterns, resulting in a small, stable and sustainable information model for IT people to maintain.  This approach allows a clinical data repository to act as a future-proof data store, totally independent of software applications and technology change.  In practice this means that no software application changes, or redeployment, are required when new or revised archetypes are published to reflect changing clinical knowledge.  As a result, life-long, application-independent health records are possible for the first time.
9. Ease of implementation openEHR is comparatively easy to implement:
   • there is little infrastructure required;
   • the software required is small, due to being based on a compact and stable, object-oriented reference model; and
   • the clinical models (Archetypes) can be developed separately from the software application.
10. Ongoing development and enhancement Based on feedback from its international collaborators, openEHR is undergoing continuing development, with ongoing maintenance and releases of specifications and software.
11. Governance of shared content Archetypes are created once, and if broadly agreed upon, they can become the basis of consistent sharing of data content between systems, providers and even other countries.  While archetypes can be designed and used by a given solo practitioner/researcher or locally within an organisation, the power of archetypes becomes more evident the more broadly they are shared.  All systems that use the same archetype – even across country borders and terminologies – will be able to interoperate. The openEHR Foundation is in the process of developing an ontologically based international, open source archetype and template repository[3].  This is being supported by a governance framework which will facilitate archetypes to be submitted for international clinical review, and publication.  It comprises a complete archetype and template lifecycle and version management repository and supporting processes to allow countries, regions, organisations or vendors to freely download and utilise these commonly agreed archetypes in their clinical systems.  Archetype libraries can also be set up at other levels, as needed – domain, organisational, regional, or national.
12. A collaborative model of development rather than a standards-based path The openEHR development to date has been the result of an interested and motivated volunteers from a broad international community of clinicians and software engineers.  Formal and rigorous processes have underpinned its progress and both Architectural and Clinical Review Boards, comprising world experts in their fields, have had oversight of the overarching strategy, process and governance. Standards are not rejected in the openEHR approach; they are just not a part of the development process.  In comparison to a traditional standards-based approach, openEHR development has been relatively rapid and pragmatic, rather than being held back by the slower process of ‘design by committee’.  In fact, the recent European CEN standard for EHR extracts (EN13606) has been based on an earlier version of, and a subset of, openEHR.  In addition, openEHR’s Archetype Definition Language (ADL), has recently been accepted as an International Standards Organisation (ISO) committee draft, for consideration as a standard.

Where is openEHR being used? [as at October, 2007]

openEHR is being used in both active research and commercial activities.  [4] Research on openEHR is being conducted in Sweden, Australia, United Kingdom, USA, Sri Lanka and Spain.  Commercial development is occurring in Australia, United Kingdom’s NHS Connecting for Health, Netherlands, Belgium, Sweden, Turkey and the USA.

The United Kingdom’s National Health Service (NHS) Connecting for Health program has just commenced a formal clinical modelling program using openEHR archetypes and templates to provide a common and agreed clinical content on which to base its clinical applications.  In a pilot early in 2007, content developed for NHS Maternity and Emergency domains were provided to vendors for implementation in new clinical application development.[5] These archetypes are available in the public domain, and have undergone broad internal review by expert clinicians prior to being approved for NHS usage.  The Emergency templates developed reflect the top 10 presentations to an Emergency Department – including chest pain, shortness of breath and collapse.  The Maternity templates followed the clinical journey of a pregnant woman – from a pre-pregnancy consultation and antenatal visits, through to capturing the labour and delivery record, including Partogram data.  Each template is made up of a variable number of archetypes – ranging from a few simple templates containing only 2 or 3 archetypes through to complex templates containing up to 80 discrete clinical concepts.

Current openEHR status and supporting tools [as at October, 2007]

1. openEHR Specification The latest openEHR Specification (Release 1.0.1) was published on 15 April 2007.
2. Clinical Content Development of archetypes and templates has gained significant momentum in the past 12 months:
   • Archetypes As a result of initial Australian modelling work and, more recently, within the UK NHS, there are now approximately 250 archetypes.
   • Templates Over forty distinct templates representing clinical needs in Emergency and Maternity were developed over a short time period in March/April 2007.
3. Commercial Tools Currently there are a number of commercial tools supporting the open source openEHR implementation.  The major tools to date are developed in .Net and Java.  Australia’s Ocean Informatics[6] has developed a suite of products supporting openEHR implementation in .Net, while Sweden’s Linkoping University have developed an Archetype Editor in Java and Cambio Healthcare Systems[7] have an early Java kernel.

The Ocean .Net products include:

• Clinical Knowledge Suite:
  • Archetype Editor (open source) and Template Designer, including semi-automated screen form construction based on templates;
  • Terminology Toolset – including a caching terminology server and subsetting tool, incorporating SNOMED-CT in the first instance; and
  • Archetype-powered Query Designer

• OceanEHR platform – including a universal data repository, demographic service binding, generic EHR Viewer; and data integration & transformation services.
• Archetype/Template Library Service as an online template and archetype repository

Get involved with openEHR

Membership of the openEHR community is free and open to everyone via the openEHR website[8].  It assumes a commitment to a common vision for high quality, interoperable EHRs, and a willingness to share ideas and experience.  All levels of interest and contribution are welcome – from novice to expert.  The lists range from discussion on technical, clinical and implementer mailing lists through active development of archetypes, templates and tools, to review and critique of clinical content models and technical specifications. Whether you are a clinician, patient advocate, vendor or provider of health care you have a role in the development of what is becoming known as 'the world's record'.

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[1] Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, Middleton B. The Value of Health Care Information Exchange and Interoperability. Health Affairs 2005 Jan 19 - http://content.healthaffairs.org/cgi/content/abstract/hlthaff.w5.10v1

[2] For more details, see www.openEHR.org

[3] For more details, see www.archetypes.com.au [UPDATE 2010: Clinical Knowledge Manager - www.openEHR.org/knowledge]

[4] For more details, see www.openehr.org/projects/t_projects.htm [UPDATE 2010: http://www.openehr.org/projects/eiffel.html]

[5] For more details of the NHS clinical modeling work, see www.ehr.chime.ucl.ac.uk/display/nhsmodels/Home

[6] For more details, see www.oceaninformatics.com

[7] For more details, see www.cambio.se/zino.aspx?lan=en-us

[8] www.openehr.org