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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
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?
•Banks are acing it; why isn’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 with this?
•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
Ear 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 registries:
• 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.
Structure is not the Messiah
•Structured data is not a goal in itself
•Structure where clear value can be identified
•It must be possible to add nuances using free text
•Sometimes free text
is adequate/best
suited for purpose
1
Semantic interoperability
•[…] the ability of computer systems
to exchange data with unambiguous,
shared meaning
•A Holy Grail of health informatics
•Requires (amongst other things)
shared information models
and terminologies
1
NCOIC, "SCOPE", Network Centric Operations Industry Consortium, 2008
«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 governed strictly
* Level 4 semantic interoperability; Walker et al. (2005); http://www.ncbi.nlm.nih.gov/pubmed/15659453
National governance
•Managed by Nasjonal IKT
•Goal: Sharing quality information models
•Online collaboration tools:
–http://arketyper.no
–https://kilden.sykehusene.no/display/KLIM/
•More than 400 clinicians and health informaticians
participating
1
• Specification for structured health records
• openEHR Foundation (openehr.org)
• Free (as in beer AND speech)
• International community
• Two level modelling
• Not an open source application
• Not a downloadable app
Illustration: https://wolandscat.net/2011/05/05/no-single-information-model/
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
Are information models enough?
•Sure, if we’re okay with making 100k models,
one for each diagnosis, lab result, symptom, …
•Sure, if we never want a list of all the patients
who had viral lung diseases
•We need something more: Terminologies
3
Terms/knowledge about
health and healthcare:
Terminologies
Vocabularies,
classifications,
ontologies; ICD-10,
SNOMED CT, ICF
Framework for information
about single individuals:
Information
models
Information structure;
openEHR archetypes, FHIR
resources
Rules to be applied to
recorded information:
Inference models
Rules and knowledge bases
used in decision support
and alert systems.
Some overlap
The things that actually
happen in healthcare:
Process models
Terminologies?
•Controlled vocabularies
•Classifications
•Ontological thesauri
3
Controlled vocabularies
•Flat lists of coded concepts
•Examples: code sets from hl7.org or volven.no
3
Classifications
•Hierarchies
•Examples: ICD-10, ATC
3
Ontological thesauri
•Polyhierarchies with associated attributes
•Synonyms
•Some are combinatorial
•Examples: SNOMED CT,
ICNP
3
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 shine
•Hundreds of thousands of concepts
–Diagnoses, symptoms, lab results,
body structures, organisms, procedures, …
•Inference based on relations between concepts
3
Relation based inference
3
Relation based inference
4
Where terminologies don’t shine
•Context
•Quantitative data types
•Complex concepts
4
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!”
• Diagnosis code had no date to show when it was valid…
4
Quantitative data types
•«Wouldn’t it 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…
4
Complex concepts
•Combinatorial explosion
–"Every kind of rash for every skin area"
–Every combination of oral glucose challenge
⇒ 601 LOINC "glucose" codes:
•Postcoordination may
mitigate, but beware…
4
Grey areas
•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
4
Summary
• Structure is important, but not always appropriate
• Clinicians must drive clinical modelling
• Information models must be shared
• Terminologies are necessary additions to information models
• Grey areas -> pragmatic choice based on requirements
More info:
• Videos of one day seminar in Sweden 2015: http://goo.gl/6Ibbkf

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Standards in health informatics - Problem, clinical models and terminologies

  • 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
  • 2. 2
  • 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? •Banks are acing it; why isn’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 with this? •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 Ear 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 registries: • 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. Structure is not the Messiah •Structured data is not a goal in itself •Structure where clear value can be identified •It must be possible to add nuances using free text •Sometimes free text is adequate/best suited for purpose 1
  • 12. Semantic interoperability •[…] the ability of computer systems to exchange data with unambiguous, shared meaning •A Holy Grail of health informatics •Requires (amongst other things) shared information models and terminologies 1 NCOIC, "SCOPE", Network Centric Operations Industry Consortium, 2008
  • 13. «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
  • 14. 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?
  • 15. Clinicians must participate! •They’re the ones who know the domain •Garbage in ⇒ garbage out •Minimise wrong interpretations
  • 16. Semantic interoperability* requires identical data models Clinical information modelling is difficult and expensive, and should be done once ⇒ Information models should be shared and governed strictly * Level 4 semantic interoperability; Walker et al. (2005); http://www.ncbi.nlm.nih.gov/pubmed/15659453
  • 17.
  • 18. National governance •Managed by Nasjonal IKT •Goal: Sharing quality information models •Online collaboration tools: –http://arketyper.no –https://kilden.sykehusene.no/display/KLIM/ •More than 400 clinicians and health informaticians participating 1
  • 19. • Specification for structured health records • openEHR Foundation (openehr.org) • Free (as in beer AND speech) • International community • Two level modelling • Not an open source application • Not a downloadable app Illustration: https://wolandscat.net/2011/05/05/no-single-information-model/
  • 20. openEHR reference model • EHR structure • Security • Versioning • Participants, dates/times, data types NO CONTENT Credit: Heather Leslie
  • 22. Archetypes • Implementable specification for one clinical concept • Comprehensible for non-techies • Maximum datasets (aspirational) • Reusable THE STANDARDISED CONTENT Credit: Heather Leslie
  • 24. 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
  • 26. Are information models enough? •Sure, if we’re okay with making 100k models, one for each diagnosis, lab result, symptom, … •Sure, if we never want a list of all the patients who had viral lung diseases •We need something more: Terminologies 3
  • 27. Terms/knowledge about health and healthcare: Terminologies Vocabularies, classifications, ontologies; ICD-10, SNOMED CT, ICF Framework for information about single individuals: Information models Information structure; openEHR archetypes, FHIR resources Rules to be applied to recorded information: Inference models Rules and knowledge bases used in decision support and alert systems. Some overlap The things that actually happen in healthcare: Process models
  • 29. Controlled vocabularies •Flat lists of coded concepts •Examples: code sets from hl7.org or volven.no 3
  • 31. Ontological thesauri •Polyhierarchies with associated attributes •Synonyms •Some are combinatorial •Examples: SNOMED CT, ICNP 3
  • 32. 3
  • 33. 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 ???
  • 34. Where terminologies shine •Hundreds of thousands of concepts –Diagnoses, symptoms, lab results, body structures, organisms, procedures, … •Inference based on relations between concepts 3
  • 37. Where terminologies don’t shine •Context •Quantitative data types •Complex concepts 4
  • 38. 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!” • Diagnosis code had no date to show when it was valid… 4
  • 39. Quantitative data types •«Wouldn’t it 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… 4
  • 40. Complex concepts •Combinatorial explosion –"Every kind of rash for every skin area" –Every combination of oral glucose challenge ⇒ 601 LOINC "glucose" codes: •Postcoordination may mitigate, but beware… 4
  • 41. Grey areas •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 4
  • 42. Summary • Structure is important, but not always appropriate • Clinicians must drive clinical modelling • Information models must be shared • Terminologies are necessary additions to information models • Grey areas -> pragmatic choice based on requirements More info: • Videos of one day seminar in Sweden 2015: http://goo.gl/6Ibbkf