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.

Oil & water: research & standards

The world of clinical modelling is exciting, relatively new and most definitely evolving. I have been modelling archetypes for over 8 years, yet each archetype presents a new challenge and often the need to apply my previous experience and clinical knowledge in order to tease out the best way to represent the clinical data. I am still learning from each archetype. And we are still definitely in the very early phases of understanding the requirements for appropriate governance and quality assurance. If I had been able to discern and document the ‘recipe’, then I would be the author of a best-selling ‘archetype cookbook’ by now. Unfortunately it is just not that easy. This is not a mature area of knowledge.

I think clinical knowledge modellers are predominantly still researchers.

In around 2009 a new work item around Detailed Clinical Models was proposed within ISO. I was nominated as an expert. I tried to contribute. Originally it was targeting publication as an International Standard but this was reduced to an International Specification in mid-development, following ballot feedback from national member bodies. This work has had a somewhat tortuous gestation, but only last week the DCM specification has finally been approved for publication – likely to be available in early 2014. Unfortunately I don’t think that it represents a common, much less consensus, view that represents the broad clinical modelling environment. I am neither pleased nor proud of the result.

From my point of view, development of an International Specification (much less the original International Standard) has been a very large step too far, way too fast. It will not be reviewed or revised for a number of years and so, on publication next year, the content will be locked down for a relatively long period of time, whilst the knowledge domain continues to grown and evolve.

Don’t misunderstand me – I’m not knocking the standards development process. Where there are well established processes and a chance of consensus amongst parties being achieved we have a great starting point for a standard, and the potential for ongoing engagement and refinement into the future. But…

A standards organisation is NOT the place to conduct research. It is like oil and water – they should be clearly separated. A standards development organisation is a place to consolidate and formalise well established knowledge and/or processes.

Personally, I think it would have been much more valuable first step to investigate and publish a simple ISO Technical Report on the current clinical modelling environment. Who is modelling? What is their approach? What can we learn from each approach that can be shared with others?

Way back in 2011 I started to pull together a list of those we knew to be working in this area, then shared it via Google Docs. I see that others have continued to contribute to this public document. I’m not proposing it as a comparable output, but I would love to see this further developed so the clinical modelling community might enhance and facilitate collaboration and discussion, publish research findings, and propose (and test) approaches for best practice.

The time for formal specifications and standards in the clinical knowledge domain will come.  But that time will be when the modelling community have established a mature domain, and have enough experience to determine what ‘best practice’ means in our clinical knowledge environment.

Watch out for the publication of prEN/ISO/DTS 13972-2, Health informatics – Detailed clinical models, characteristics and processes. It will be interesting to observe how it is taken up and used by the modelling community. Perhaps I will be proven wrong.

With thanks to Thomas Beale (@wolands_cat) for the original insight into why I found the 13972 process so frustrating – that we are indeed still conducting research!

Quality indicators & the wisdom of crowds

The Collaboration & Verification step during the archetype development process in particular poses a particular challenge to determine appropriate quality criteria as it is performed online using the Clinical Knowledge Manager and, in the main, on a volunteer basis. This is effectively a form of crowd-sourcing in a Web2.0 environment known as ‘crowd wisdom‘, and from my research and enquiries there does not appear to be other examples of quality assessments based on crowd-sourced information. I would be very pleased if anyone could identify similar efforts in this or other domains that I have missed.

While benefits from ‘the wisdom of crowds’ has been identified since 1907, beginning with Francis Galton‘s account of fair-goers guessing an ox’s weight, there are clearly also some valid concerns that need to be addressed. How do we determine that ‘the crowd’ is appropriate for the collective task and the outcome is a safe and credible?

No doubt there will be concern expressed by some that we have no specific knowledge of each individual participating in a voluntary and collaborative process – that they have not been appointed as an expert for the task. It is true that they may have less credentials than might be chosen for appointment to a formal committee, but they may contribute critical grassroots knowledge that may not otherwise be available or, conversely, be more qualified than those experts we could gather together in one physical location at one specific time. It is very possible that the caliber of the CKM review group may actually be greater than those appointed by peers. The effect of individuals who have an ‘outlier’, or extreme, opinion will usually be balanced by the effect of the ‘crowd’.

Consider also that it is just not possible to appoint a single group of experts, qualified and accredited in the traditional sense, who are equipped to arbitrate on the fitness of every single clinical model – model quality will be compromised because of the sheer breadth and depth of clinical knowledge that we need to capture in archetypes. For a task this large and diverse, one committee to verify every archetype will be woefully inadequate; specific committees determined according to a profession or a domain expertise will not fulfill the task either as many archetypes cross professional and domain boundaries. An ideal scenario is to draw together committees of experts that are appropriate for each individual archetype – comprising those who have expressed an explicit interest in nurturing this archetype through to publication and implementation (‘Adopters’ in CKM); representation from a broad range of relevant professional backgrounds, from a broad range of domain expertise and from a range of geographical locations; and still others who can be identified as having specific expertise in the area – only then can we hope to ensure that all aspects of the archetype are adequately addressed.

Pointers to the current maturity of any model and its’ readiness for publication through the Collaboration and Verification process might include:

Collaboration & Verification Process Criteria Indicators
PROCESS An appropriate collaborative, peer review process has been followed
  • Number of review rounds completed <CKM derived>
  • Number of completed individual reviews <CKM derived>
An appropriate reviewer community has contributed to the reviews
  • Total number of unique reviewers <CKM derived>
  • Professions (more than one may be selected):
    • Number of Clinicians <CKM derived>
      • Medical <CKM derived>
      • Nursing <CKM derived>
      • etc
    • Number of Health informaticians/ technical experts
    • Number of Terminologists <CKM derived>
    • Number of Administrators    <CKM derived>
    • Number of Consumers <CKM derived>
  • Domain expertise breakdown (more than one may be selected):
    • Cardiology <CKM derived>
    • General Practice <CKM derived>
    • Allied health <CKM derived>
    • Etc
  • Geographical location of reviewers <CKM derived>
EVIDENCE Appropriate references have been supplied, where appropriate
  • <Insert all references> <CKM derived>
FIT FOR PURPOSE The model fulfils identified requirements (eg business, technical and stakeholder reqts)
  • Program X:
    <Manually assessed and inserted>
  • Standard Y:
    <Manually assessed and inserted>
  • Specification Z:
    <Manually assessed and inserted>
The state of Reviewer consensus is appropriate:
  • Latest Review Round recommendations <CKM derived>:
    • Accept – ready for publication, or minor changes have been identified that don’t impact on the meaning of the model when corrected (eg typo’s)
    • Minor changes – issues identified that are content-related eg modification of metadata, element naming or descriptions, but are not structural
    • Major changes – issues identified that are structural and require significant re-organisation or re-working of the model
    • Reject – the model is fundamentally flawed
    • Abstain
Clinical safety assessment has been completed/up-to-date
  • <Manually assessed and inserted>
Clinical safety assessment status is appropriate
  • <Manually assessed and inserted>

Acknowledgements: Ian McNicoll, Sebastian Garde, Hugh Leslie, Mary Kelaher, Stephen Chu.

From the indicators above it is possible to draw some simple conclusions regarding the reviewer community, in terms of the size of the reviewer community and the balance in terms of domain expertise, professional involvement and geographical distribution. For example, if there are small numbers for a common archetype concept, it may be necessary to suspend the process until these factors can be rectified; on the other hand, for an obscure or specialised archetype concept small numbers may be acceptable. If there are few clinicians reviewing an archetype for a clinical concept, this should also raise alarm bells. In addition, the level of community has consensus will also indicate some sense of progress – a majority of ‘Minor change’ recommendations after 4 review rounds can indicate good progress; all ‘Accept’ recommendations after ‘x‘ review rounds indicates consensus and agreement if the constituted community is well balanced; ‘Major change’ or ‘Reject’ recommendations after a number of review rounds can indicate an archetype in trouble and requiring investigation.

In order to support the crowd-wisdom approach, we need to provide adequate opportunities for those observing from ‘outside’ to have insight into the collaboration and verification process, plus make sure that the decision-making process is transparent, via the Clinical Knowledge Manager tool.  A single Governance Committee could be appointed to have oversight of the CKM processes using the quality criteria and indicators as markers and to enable formal sign off of the models prior to publication. This group will ensure that:

  • a quality processes has been followed in the development and evolution of the archetype; and
  • that the quality indicators as measured or assessed confirm that the processes have been appropriate for that archetype.

Harnessing the ‘wisdom of the crowd’ has the potential to be more powerful than traditional quality processes for a task of this nature, to determine the quality of the clinical content in our electronic health records – we need to learn how best to tap into that wisdom.

It will not be perfect – and the fail-safe is that if something has been missed or done inappropriately, then the CKM community of independent individuals have mechanisms to point out the flaws, errors or concerns to the wider CKM community. The Editors and the Governance committee, always accountable to the community, will be required to respond to and resolve the issues raised to the satisfaction of the CKM community.

Appropriate is a word that I’ve used quite a bit in this post. I’m fully aware that it is in defining ‘appropriate’ in each use-case that we meet our next challenge…

This post reflects my current thoughts; no doubt these will be refined further as we gain experience. Will keep you posted 🙂