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Standards in health informatics
– Problem, clinical models and terminologies
Silje Ljosland Bakke / Information architect, Nasjonal IKT HF / Co-lead, openEHR Foundation Clinical Models Program
E-mail: silje.ljosland.bakke@nasjonalikt.no / Twitter: @siljelb
About me
•Informaticist
•Registered nurse
•Information architect
•Cat herder
2
An ongoing problem…
“In attempting to arrive at the truth, I have applied
everywhere for information but in scarcely an instance have I
been able to obtain hospital records fit for any purpose of
comparison.”
“If they could be obtained, they would enable us to decide
many other questions besides the one alluded to. They would
show subscribers how their money was being spent, what
amount of good was really being done with it or whether the
money was not doing mischief rather than good.”
- Florence Nightingale, 1863
Credit: Heather Leslie
Why is health IT so hard?
•The banks are doing it; why can’t health?
•Complex and dynamic domain
•Lifelong records
•Clinical diversity
•Confidentiality
•Mobile population
Credit: Heather Leslie
Complexity
•Both the number of concepts and the rate of
change is high
•Health is big, and continually growing…
–In breadth
–In depth
–In complexity
•Clinical knowledge is continually changing
Credit: Heather Leslie
How have we been dealing?
•Free text(Specialist and administrative systems have more
structured data, but generic electronic health records
are still mainly free text)
So what do we need structure for?
•Avoid repetition and
shadow records
•Retrieval and overview
•Reuse of record info
•Clinical decision support
•Quality indicators
•Management data
Longitudinal information access
•How long are you planning to live?
•Do you expect your health record to survive
that long?
•Even if it does survive, will
it be readable for future
systems and users?
Credit: Ricardo Correia
Celsius
Aural measurement
IR aural thermometer
Environment: 5° C
Wet clothing Space blanket
temperatureBody
Structuring health is hard
Credit: Bjørn Næss
Structuring identically is even harder
Example: Smoking status in
national registers:
• 9 different variations on
“Smoking status” in 26
different forms
• Additionally: number of cigs
per day, month quit smoking,
number of months since
quitting date, etc.
Brandt, Linn (2016). Report from REGmap February 2016 – Complete mapped register set - Preliminary analysis.
Semantic interoperability
•A (or The?)
Holy Grail of Health Informatics
•[…] the ability of computer systems to
exchange data with unambiguous,
shared meaning
•Requires (among other things)
shared information models
and terminologies
1
«Information model»?
•A definition of the structure and content of the
information that should be collected or shared
– A "minimal dataset"
– A message or interface definition
•Internally all applications have some sort of
information model
•Sharing information requires developing shared
information models
Credit: Ian McNicoll
How have we been doing infomodelling?
•Locked into each product
•In ways that clinicians don’t understand
•Few clinicians participating
•Technicians are left to interpret
•New requirements?
Clinicians must participate!
•They’re the ones who know the domain
•Garbage in ⇒ garbage out
•Minimise wrong interpretations
Semantic interoperability* requires
identical data models
Clinical information modelling is difficult and
expensive, and should be done once
⇒ Information models should be
shared and strictly governed
* Level 4 semantic interoperability; Walker et al. (2005); http://www.ncbi.nlm.nih.gov/pubmed/15659453
Standards in health informatics - problem, clinical models and terminology
openEHR
• Specification for structured health
records
• openEHR Foundation (openehr.org)
• Free (as in beer AND speech)
• International community
• Two level modelling
– Reference model
– Content models (archetypes)
& data sets (templates)
openEHR reference model
• EHR structure
• Security
• Versioning
• Participants, dates/times,
data types
NO CONTENT
Credit: Heather Leslie
openEHR reference model
Domain
Core
RM
Archetypes
• Implementable specification for one clinical concept
• Comprehensible for non-techies
• Maximum datasets (aspirational)
• Reusable
THE STANDARDISED CONTENT
Credit: Heather Leslie
Archetypes
2
Templates
• Combinations of constrained archetypes
• Data sets for forms, messages, interfaces, etc
• For specific use cases
• NOT user interfaces
THE USECASE SPECIFIC CONTENT
Credit: Heather Leslie
Templates
2
National governance
•January 2014: Archetype governance started
– Goal: quality and interoperability
– Four people
•Online collaboration
– arketyper.no / wiki.arketyper.no
– 350 registrered users
– Vendors and registries participate
0
50
100
150
200
250
300
350
400
jan.14
apr.14
jul.14
okt.14
jan.15
apr.15
jul.15
okt.15
jan.16
apr.16
Number of registrered users
Processes
• Formalised processes for
development, review and
publishing
– Archetype development is
a “do-ocracy”; not centrally
prioritised
– Re-use of international
models
0
5
10
15
20
25
30
35
40
45
jan.14
mar.14
mai.14
jul.14
sep.14
nov.14
jan.15
mar.15
mai.15
jul.15
sep.15
nov.15
jan.16
mar.16
Number of published archetypes
Reviews and approval
• National editorial committee defines review requirements
• Review rounds last one week, several rounds per archetype
• Until clinicians reach consensus… 
• If requirements are met, the archetype is «Published» on
arketyper.no
• Unpublished archetypes are unstable and should not be used
Are information models enough?
•Sure, if we want to make 100k archetypes for
each diagnosis, lab result, symptom, …
•Sure, if we never want to query for all the
patients who had pneumonia caused by viruses
•We need something more: Terminologies
2
Things that are always
true:
Terminologies
Vocabularies,
classifications,
ontologies; ICD-10,
SNOMED CT, ICF
Things that are true for a
specific individual:
Information
models
Information structure;
openEHR archetypes, FHIR
resources
Things that are true given a
specific set of conditions:
Inference models
Rules and knowledge bases
used in decision support
and alert systems.
Rector AL, Rogers J, Taweel A. Models and inference methods for clinical systems: a principled approach. Stud
Health Technol Inform. 2004;107(Pt 1):79-83
Some overlap
Terminologies
•Controlled vocabularies (NCMP, NCSP)
–Flat lists of coded concepts
•Classifications (ICD-10, ATC)
–Hierarchies
•Ontological thesauri (SNOMED CT, ICNP)
–Polyhierarchies with associated attributes
–Some are combinatorial
3
Terminologies vs information models
Information models can be said to
describe the "questions"
Terminologies can give (some of) the
"answers"
Complementary concepts
ICD_10::L40.0::Psoriasis vulgaris
and
SCT_2015::74757004::Skin structure of elbow
SCT_2015::6736007::Moderate
???
Where terminologies rule
•(100s of) Thousands of concepts
–Diagnoses, symptoms, lab results, body
structures, organisms, procedures, …
•Inference based
on relations
between concepts
3
Where terminologies don’t shine
•Context
•Quantitative data types
•Complex concepts
3
Context
•"Let’s just chuck the codes in here so we can bill for
this cancer treatment!"
•15 years later, from the brand new Dr. Google:
–"Ma’am, I’m sorry to tell you you have ovarian
cancer."
–"What!? They were taken out 15 years ago!"
3
More: http://e-patients.net/archives/2009/04/imagine-if-someone-had-
been-managing-your-data-and-then-you-looked.html
Quantitative data types
•"It’d be really nice to just have a code for the
number of the pregnancy the woman is in…"
•"Yeah. 10 ought to be
enough for anybody."
Famous last words…
3
Complex concepts
•Combinatorial explosion
–"Every kind of rash for every skin area"
–Every combination of oral glucose challenge
⇒ the 601 LOINC "glucose" codes:
•Postcoordination may
mitigate, but beware…
3
Grey area
•Small value sets
•Some contextual information
–Actual diagnosis vs. tentative vs. risk vs. exclusion
vs. family history
•Consistent use is hard, and not always appropriate
–Different use cases will have different requirements
3
Summary
• Clinical information models must be free and shared
• Clinicians must drive clinical modelling
• Terminologies are necessary additions to information models
• Grey areas -> pragmatic choice based on requirements
More info:
• One day seminar/two day course in Oslo May 23/24: http://goo.gl/UW7et5
• Videos of one day seminar in Sweden 2015: http://goo.gl/6Ibbkf

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Standards in health informatics - problem, clinical models and terminology

  • 1. Standards in health informatics – Problem, clinical models and terminologies Silje Ljosland Bakke / Information architect, Nasjonal IKT HF / Co-lead, openEHR Foundation Clinical Models Program E-mail: silje.ljosland.bakke@nasjonalikt.no / Twitter: @siljelb
  • 3. An ongoing problem… “In attempting to arrive at the truth, I have applied everywhere for information but in scarcely an instance have I been able to obtain hospital records fit for any purpose of comparison.” “If they could be obtained, they would enable us to decide many other questions besides the one alluded to. They would show subscribers how their money was being spent, what amount of good was really being done with it or whether the money was not doing mischief rather than good.” - Florence Nightingale, 1863 Credit: Heather Leslie
  • 4. Why is health IT so hard? •The banks are doing it; why can’t health? •Complex and dynamic domain •Lifelong records •Clinical diversity •Confidentiality •Mobile population Credit: Heather Leslie
  • 5. Complexity •Both the number of concepts and the rate of change is high •Health is big, and continually growing… –In breadth –In depth –In complexity •Clinical knowledge is continually changing Credit: Heather Leslie
  • 6. How have we been dealing? •Free text(Specialist and administrative systems have more structured data, but generic electronic health records are still mainly free text)
  • 7. So what do we need structure for? •Avoid repetition and shadow records •Retrieval and overview •Reuse of record info •Clinical decision support •Quality indicators •Management data
  • 8. Longitudinal information access •How long are you planning to live? •Do you expect your health record to survive that long? •Even if it does survive, will it be readable for future systems and users? Credit: Ricardo Correia
  • 9. Celsius Aural measurement IR aural thermometer Environment: 5° C Wet clothing Space blanket temperatureBody Structuring health is hard Credit: Bjørn Næss
  • 10. Structuring identically is even harder Example: Smoking status in national registers: • 9 different variations on “Smoking status” in 26 different forms • Additionally: number of cigs per day, month quit smoking, number of months since quitting date, etc. Brandt, Linn (2016). Report from REGmap February 2016 – Complete mapped register set - Preliminary analysis.
  • 11. Semantic interoperability •A (or The?) Holy Grail of Health Informatics •[…] the ability of computer systems to exchange data with unambiguous, shared meaning •Requires (among other things) shared information models and terminologies 1
  • 12. «Information model»? •A definition of the structure and content of the information that should be collected or shared – A "minimal dataset" – A message or interface definition •Internally all applications have some sort of information model •Sharing information requires developing shared information models Credit: Ian McNicoll
  • 13. How have we been doing infomodelling? •Locked into each product •In ways that clinicians don’t understand •Few clinicians participating •Technicians are left to interpret •New requirements?
  • 14. Clinicians must participate! •They’re the ones who know the domain •Garbage in ⇒ garbage out •Minimise wrong interpretations
  • 15. Semantic interoperability* requires identical data models Clinical information modelling is difficult and expensive, and should be done once ⇒ Information models should be shared and strictly governed * Level 4 semantic interoperability; Walker et al. (2005); http://www.ncbi.nlm.nih.gov/pubmed/15659453
  • 17. openEHR • Specification for structured health records • openEHR Foundation (openehr.org) • Free (as in beer AND speech) • International community • Two level modelling – Reference model – Content models (archetypes) & data sets (templates)
  • 18. openEHR reference model • EHR structure • Security • Versioning • Participants, dates/times, data types NO CONTENT Credit: Heather Leslie
  • 20. Archetypes • Implementable specification for one clinical concept • Comprehensible for non-techies • Maximum datasets (aspirational) • Reusable THE STANDARDISED CONTENT Credit: Heather Leslie
  • 22. Templates • Combinations of constrained archetypes • Data sets for forms, messages, interfaces, etc • For specific use cases • NOT user interfaces THE USECASE SPECIFIC CONTENT Credit: Heather Leslie
  • 24. National governance •January 2014: Archetype governance started – Goal: quality and interoperability – Four people •Online collaboration – arketyper.no / wiki.arketyper.no – 350 registrered users – Vendors and registries participate 0 50 100 150 200 250 300 350 400 jan.14 apr.14 jul.14 okt.14 jan.15 apr.15 jul.15 okt.15 jan.16 apr.16 Number of registrered users
  • 25. Processes • Formalised processes for development, review and publishing – Archetype development is a “do-ocracy”; not centrally prioritised – Re-use of international models 0 5 10 15 20 25 30 35 40 45 jan.14 mar.14 mai.14 jul.14 sep.14 nov.14 jan.15 mar.15 mai.15 jul.15 sep.15 nov.15 jan.16 mar.16 Number of published archetypes
  • 26. Reviews and approval • National editorial committee defines review requirements • Review rounds last one week, several rounds per archetype • Until clinicians reach consensus…  • If requirements are met, the archetype is «Published» on arketyper.no • Unpublished archetypes are unstable and should not be used
  • 27. Are information models enough? •Sure, if we want to make 100k archetypes for each diagnosis, lab result, symptom, … •Sure, if we never want to query for all the patients who had pneumonia caused by viruses •We need something more: Terminologies 2
  • 28. Things that are always true: Terminologies Vocabularies, classifications, ontologies; ICD-10, SNOMED CT, ICF Things that are true for a specific individual: Information models Information structure; openEHR archetypes, FHIR resources Things that are true given a specific set of conditions: Inference models Rules and knowledge bases used in decision support and alert systems. Rector AL, Rogers J, Taweel A. Models and inference methods for clinical systems: a principled approach. Stud Health Technol Inform. 2004;107(Pt 1):79-83 Some overlap
  • 29. Terminologies •Controlled vocabularies (NCMP, NCSP) –Flat lists of coded concepts •Classifications (ICD-10, ATC) –Hierarchies •Ontological thesauri (SNOMED CT, ICNP) –Polyhierarchies with associated attributes –Some are combinatorial 3
  • 30. Terminologies vs information models Information models can be said to describe the "questions" Terminologies can give (some of) the "answers" Complementary concepts ICD_10::L40.0::Psoriasis vulgaris and SCT_2015::74757004::Skin structure of elbow SCT_2015::6736007::Moderate ???
  • 31. Where terminologies rule •(100s of) Thousands of concepts –Diagnoses, symptoms, lab results, body structures, organisms, procedures, … •Inference based on relations between concepts 3
  • 32. Where terminologies don’t shine •Context •Quantitative data types •Complex concepts 3
  • 33. Context •"Let’s just chuck the codes in here so we can bill for this cancer treatment!" •15 years later, from the brand new Dr. Google: –"Ma’am, I’m sorry to tell you you have ovarian cancer." –"What!? They were taken out 15 years ago!" 3 More: http://e-patients.net/archives/2009/04/imagine-if-someone-had- been-managing-your-data-and-then-you-looked.html
  • 34. Quantitative data types •"It’d be really nice to just have a code for the number of the pregnancy the woman is in…" •"Yeah. 10 ought to be enough for anybody." Famous last words… 3
  • 35. Complex concepts •Combinatorial explosion –"Every kind of rash for every skin area" –Every combination of oral glucose challenge ⇒ the 601 LOINC "glucose" codes: •Postcoordination may mitigate, but beware… 3
  • 36. Grey area •Small value sets •Some contextual information –Actual diagnosis vs. tentative vs. risk vs. exclusion vs. family history •Consistent use is hard, and not always appropriate –Different use cases will have different requirements 3
  • 37. Summary • Clinical information models must be free and shared • Clinicians must drive clinical modelling • Terminologies are necessary additions to information models • Grey areas -> pragmatic choice based on requirements More info: • One day seminar/two day course in Oslo May 23/24: http://goo.gl/UW7et5 • Videos of one day seminar in Sweden 2015: http://goo.gl/6Ibbkf