Dear colleagues,
Apologies for cross posting - just wanted to ensure that we share these
insights broadly...
We recently passed the eleven-year anniversary for the first upload to the
international CKM - the body temperature archetype. As Europe readies itself
for summer holidays and the clinical review season slows down, it is a good
time to review the progress of the openEHR clinical modelling program.
Roughly 6 weeks ago I created and downloaded a number of reports from CKM. I've
spent some time analysing the data and thought I'd share what I learned with
you.
This exploration was triggered by a tweet from Ewan Davis last December asking:
"How many person hours do you think has gone in to creating the openEHR
archetypes available via CKM - I think it must be in excess of 100,000 hours
(40 person years)"
It took a while to gather the data and propose reasonable assumptions so that
we could make time and effort estimates, but here goes...
CKM stats
(As of July 5 2019):
1. Community
* Registered users - 2239
* Countries represented - 95
2. Archetype library
* Total archetypes - 785
* Active archetypes
i.
Published - 93
ii. Published
as v1, needing reassessment - 6
iii. In review
- 31, with at least 7 about to be published
iv. Draft - 351
v. Initial
(in incubators) - 110
* Proposed archetypes - 10
Behind the scenes
(from CKM reports, May 2019)
1. Number of archetypes which have completed or are undergoing a review
process - 130
2. Number of review rounds completed - 295
3. Number of archetype reviews completed by all reviewers - 2995
4. Number of unique reviewers - 272
5. Reviews completed per review round - 10.15
6. Average number of reviews per archetype - 23.04
7. Average number of reviews per reviewer - 11.01
8. On average, approximately 100 unique reviewers log into to CKM 900 times
per month during the past 12 months.
Time estimates
This is where things become interesting...
Task
Hours
Design time
10,437
Reviewer time (assumes 30 minutes per review)
1,498
Editorial time
2,522
Clinical Knowledge Administrator (CKA)
1,057
Translation
775
Total
16,289
This equates to roughly 8.5 person years.
Obviously, I have made some assumptions about the average time for many
activities and if we factor in incidental conversations or pondering modelling
conundrums or cross pollination between CKMs we could reasonably increase the
estimate to 10 person years. However, try as I could, there was no way I could
justify bumping them up in order to achieve estimates of 20, much less 40,
person years. These numbers reflect the work for archetypes that are owned and
managed in the international CKM. This includes an estimation of work done by
the reviewers and editors from the Apperta and Norwegian CKMs if their
archetypes are now residing in the international CKM, or multiple CKMs. It does
not reflect the work done on reviews from the now retired Australian CKM,
although estimates of design time have been part of the assumptions.
I interpret Ewan's estimate to reflect his impression that the effort to
achieve what we have done so far was huge. I too believed that the effort was
epic, but in my head it was still only in the ballpark of about half of his
initial estimate. That the actual effort appears to be only 8-10 person years
totally surprised me. Initially my figures were considerably lower; I did go
back to the figures and tried to massage them upward because this is obviously
a rather inexact science, more like an educated guestimate, but this is as far
as I feel comfortable going.
In addition, Thomas Beale estimates that on average there are 14 clinically
significant data elements per archetype, according to the ADL Workbench. These
are the relevant data points that we design, review etc. So 785 active
archetypes x 14 data points/archetype suggests that we have a library of
approximately 10,990 data points, none of which are duplicates or overlapping
in the governed archetypes. And if we agree with my estimate of a total of
16289 hours, the amount of time per data element is 16289/10990 - only 1.48
hours each, covering design, review, maintenance, governance.
What conclusions can we draw?
* Firstly, modelling 'openEHR style' seems to be quite efficient,
surprising even those of us who are involved daily and secondly, this unique
collaborative and crowdsourced approach to standardisation of clinical data is
working well. On top of that, if you remember that more than 95% of the
editorial work and reviewer's time has been volunteer, then it this truly has
been an extraordinary community endeavour.
* Secondly, the ratio of reviewer time to design time is noteworthy - 1498
hours of review, compared to 10437 hours of design. In effect, we have
successfully minimised reviewer effort by making each 30-minute review count as
efficiently as possible, and that has been achieved by attention to detail and
spending time investigating and developing strong design patterns before we
send them out for review. Over the years we have made some bad design choices
and had to rethink our approach. Gradually we have been developing some good
patterns and, before you ask where we have documented them, I will point you to
the published archetypes - each of them functions as a potential pattern for
the next archetype we intend to develop - we reference and reuse the patterns
as much as possible. In this way our library is growing, and our modelling is
improving. As an example, a current area of serious rework is the Physical
examination archetypes which are being 'renovated' at present. It does make me
think that for every hour spent in design it is a good investment of time and
effort - that may not seem apparent in the early days, but I think that we are
finding that it is paying off for the archetypes that we are designing years
later, based on the (good and bad) learnings from those earliest archetype
designs.
* Thirdly, we have some insights into the modelling community, and for the
first time we have some idea about the level of activity by those with various
roles and activities. We also have an estimate of the size of the data library
at data element level, so that we are able to compare to other similar
modelling efforts elsewhere in the world.
I would particularly like to thank my co-lead, Silje Ljosland Bakke, and Ian
McNicoll for their dedicated efforts, and of course to all of the other
Editors, Reviewers and Translators who have so generously volunteered their
time and expertise to create a strong free and public foundation for digital
health data standards.
We should all be very proud of this work. This will be our legacy that will
live on after well after we've all long retired.
Kind regards
Heather Leslie
Dr Heather Leslie
MB BS, FRACGP, FACHI, GAICD, FIAHSI
M +61 418 966 670
Skype: heatherleslie
Twitter: @atomicainfo, @clinicalmodels & @omowizard
www.atomicainformatics.com
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