Making Terminologies useful and usable: Clinical Terminologies in the 21st Century: What are they for? What might they look like?

1
Making Terminologies useful and
usableClinical Terminologies in the 21st
Century What are they for? What might they
look like?

• Alan RectorBio and Health Informatics
  Forum/Medical Informatics GroupDepartment of
  Computer ScienceUniversity of Manchester
• rector_at_cs.man.ac.ukwww.cs.man.ac.uk/mig
  img.man.ac.ukwww.clinical-escience.orgmygrid.man
  .ac.uk

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An Old Problem

• On those remote pages it is written that animals
  are divided into
• a. those that belong to the Emperor
• b. embalmed ones
• c. those that are trained
• d. suckling pigs
• e. mermaids
• f. fabulous ones
• g. stray dogs
• h. those that are included in this classification
• i. those that tremble as if they were mad
• j. innumerable ones
• k. those drawn with a very fine camel's hair
  brush
• l. others
• m. those that have just broken a flower vase
• n. those that resemble flies from a distance"

From The Celestial Emporium of Benevolent
Knowledge, Borges
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But why in healthcare?

• Whats it for? Whats the purpose?
• Terminologies are of little use in themselves
• How will it make care better? new things
  possible?
• How will it make information systems better?
• Painful experience of 20 years of over-selling
  and under performance
• Do we need it Clinically? Technically?
• If we need it
• what is it? Is it one thing or many?
• How will we know if we have it?
• How will we know if it is fit for purpose

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Why Now?

• Whats different now?
• Web, E-Science, Grids
• Web speed
• New technologies OWL, new DLs, hybrid frame-DL
  environments www.semanticweb.org
• Post genomic medicine personalised medicine
• Joining up Healthcare Medical and Bioscience
  research CLEF
• Systemisation of healthcare
• Clinical error reduction, clinical governance,
  evidence based medicine,
• Does anybody else have similar problems?
• Ontologies are flavour of the month in
  E-Science Web
• Bioinformatics is building them very rapidly
• What can we learn from them?

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A Convergence of Need

• Post genomic research

• Safe, high quality, evidence based health care

Knowledge is Fractal
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The requirements Tools chain

• Clinical users with needs to improve care /
  clinical knowledge
• Applications for clinical users that meet those
  needs
• Developers needs for terminology to build those
  applications
• Terminologies which fit the applications
  builders needs to meet the clinical users needs

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Who is it for?(Useful usable to whom?)

• Clinical users
• Carers - prospective
• Reviewers retrospective
• Researchers, managers, assessors,
• The community how it shares its knowledge
• Knowledge creators / distributors
• Application developers
• Easier to re-use what exists than to build new
• Re-use or bust
• Terminology authors
• Quick responsive evolution

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Useful and Usable

• Useful for what?
• Supports needed applications
• Purpose
• Does it well
• Quality
• Usable by whom?
• Intuitive / understandable
• Handy
• What you need is to hand
• Timely

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Preview of Arguments

• The priorities are clinical needs supported by
  applications supported by terminology
• Clinical quality is critical
• Useful and usable to clinical users, developers,
  reviewers, authors
• In an open evolving world, open managed evolution
  is the only plausible way forward
• Current technology gives us the opportunity to
  cope
• Tools and environments are as important as content

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Where we come from
Best Practice
Best Practice
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Terminology is Now Middlewarehuman-machine /
machine - machine

• Explicit
• Machines can only manipulate what is represented
  explicitly
• More re-use ? more manipulation ? more
  explicitness
• Understandable
• People can only build, maintain and use it if
  they can understand it
• Adequate
• Expressive enough to do the job but still
  computationally tractable
• Reliable
• People can use it consistently
• Scalable and maintainable

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Where we think we are going

• Pre-1980 paper
• Application specific retrospective human oriented
  systems
• ICD, early SNOMED, CPT, OPCS,
• Mid 1980s 1990s electronic paper
• Retrospective reporting Prospective collection
• ICPC Read I,
  II
  
• Mid 1990s mid 2000sCentralised computer based
• Retrospective reporting Prospective collection
• OpenGALEN, Read III, SNOMED-RT
• PENPAD
• Mid 2000s ? Web based open managed evolution
• ???? but see the Semantic Web, Gene Ontology,
  etc.

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How we will know when we get thereCriteria for
success

• Re-use
• A recognised growing library of common decsision
  support modules
• Stop starting from scratch!
• Integration
• 2 independently developed DSSs integrated
  with2 independently developed EPRS
  withoutexponentially increasing effort.

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Criteria for success

• Authoring
• No individual invests in their own terminology
• enterprise-wide terminology servers
• Indexing
• Simplification of systems
• a sharp drop in special cases and exceptions
• a sharp increase in authors productivity

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Criteria for success

• User interfaces
• Real systems in real use with real patients by
  real clinicians
• transparent systems

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Stones in the Road

• Why are we not there yet?
• Some background definitions
• Some hypotheses

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Clinical quality logical quality

• Clinical quality do users put in the right
  things?
• Repeatability of information captue (inter rater
  reliability)
• For decision support in prospective use
• For retrieval in retrospective use
• Salience
• Relevance to clinical decisions for prospective
  use
• Significance to questions for retrospective use
• A better measue than coverage
• Logical quality do systems give the right
  responses?
• Correct organisation (classification)
• Correct inferences given correct input

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Hypothesis 1

• Most computer oriented terminology development
  ignores clinical quality
• The EHR as black hole
• Bigger is not necessarily better
• although clinical quality was the primary
  concern of traditional paper/human oriented
  terminologies(and there are honourable
  exceptions e,g, ICPC).
• Evidence High variability in recorded use
  Systematic failure to use
  data from GP systems in clinical studies
  (despite PRIMIS) Our own
  colleagues experience in repeated studies
  Current planned cost of cohort post
  genomic studies

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Three models

• Meaning - ontologies
• Can I depend on the answers?
• Dyspnoea is a respiratory problem
• Clinical significance decision support
• What should I think of / how does it affect
  decisions
• Dyspnoea can be a symptom of congestive heart
  failure
• Model of use EHR/human factors
• Is what I want to hand is it handy?
• Dyspnoea should be a question on a cardiac
  history

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Hypothesis 2

• Early terminologies emphasised models of use and
  significance and failed for lack of model of
  meaning
• Heart diseases are in 13 Chapters of ICD9
• Recent terminologies emphasise model of meaning
  and fail for lack of models of use and
  significance
• Evidence
• User dissatisfaction, non-use, and poor quality
  data
• The few systems based on models of use have been
  surprisingly popular with doctors, e.g. MedCin,
  ORCA
• But hard to use for retrieval
• We have fewer formal models of use than of
  meaning
• We have almost no models of significance

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Grounding cost vs Clean-up cost(with thanks to
Enrico Coiera)

• Grounding cost
• The cost of establishing a given quality of
  communication
• How much French do you need to order a meal?
• Clean up cost
• The cost of fixing miscommunication
• How many surprises will you accept? of what kind?

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Special purpose vs Re-usable Multipurpose

• Special purpose terminologies
• Almost all retrospective
• Reporting for remuneration ICD9-CM, CPT
• Reporting for epidemiology - ICD10, OPCS
• Multipurpose re-usable terminologies
• Aspire to be the glue for Patient centred
  systems Personalised Medicine
• Decision support
• Electronic Health Records
• Research
• Integration with Bioscience
• But too often multipurpose means no purpose
  multiapplication means
  no application

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Need Multipurpose mean no purpose?

• Multiple purposes held by multiple groups
• Multiple sources of expertise authority
• One size does not fit all
• Multiple collaborations
• Multiple legacies
• Multiple purposes use multiple applications
• Applications are the point of interaction
• Applications make needs concrete testable

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Multipurpose means interacting with othersIts a
big open world out there

• Bioscience
• Gene Ontology, National Cancer Institute Center
  for Bioinformatics (NCICB), The Digital
  Anatomist/ Mouse Anatomy/Mammalian Anatomy,
  BioJava,PRINTS, EMBL, Microarrays, Protemoics,
  Metabalomics, Systems Biology
• Medicine meets bioscience
• Cancer therapeutics, New imaging,
• E-Health sharing and pooling data Collections
  based research
• BioBank, NTRAC, NCRI, NCTR, CLEF,
• Health Intelligence
• MRC policy on data sharing

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Hypothesis 3

• Grounding costs can be delimited for special
  purpose terminologies
• Grounding costs are indefinite for re-usable
  terminologies ( is historically high)
• Without purposes testable through applications
  there
• Danger of the escalating deadly embrace
• Must have terminology to build applications
  but Must have applications before terminology
• Evolutionary approach the only exit

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Central Control vs Open managed evolution

• Académie française vs Oxford English Dictionary
• Scholasticism vs Empiricism
• The arrogance of the a priorPeople dont know
  what they do
• Look to see what is actually used
• Language technology shows time and again that our
  predictions are faulty
• Command economy vs Social Market
• Participation is the issue rather than money
• Somebody will still have to pay
• But at least they might pay for something useful

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Central management

• Owned by one Authority
• Coupling tight / autonomy low/ participation low
• Grounding costs high / Clean up costs low?
• must have everything before you can do anything
• Change slow lockstep
• A product

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Open managed evolution

• Owned by the community multiple authorities
• Coupling loose/ autonomy high / participation
  high
• To be useful usable involve users using systems
• Grounding costs low / Clean up costs high?
• Just in time Just enough
• Agree where it counts
• Change quick and local - threaded with
  annealing
• A process

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Hypotheses 4

• Single purpose clinical terminologies can be best
  managed centrally
• By definition are developed in conjunction with
  an application
• Re-usable terminologies can only succeed by open
  managed evolution
• Many purposes require many contributors
• Evidence Speed of uptake of HL7/LOINC
• W3C the evolution of
  the Web
• Re-usable terminologies can only be developed in
  open collaboration with applications
• Otherwise multipurpose become no purpose

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Hypothesis 5

• Modern technology provides the means to support
  open managed evolution without compromising
  clinical quality or technical stability
• Trade lower grounding cost for greater clean up
  cost
• Focus on minimal stable core. Defer commitments.
• Evidence OpenGALEN, Gene Ontology
• Utilise Web/Grid technologies for rapid
  dissemination and coordination
• Evidence Current developments at Mayo clinic
  using LDAP
• Distribute terminology like domain names

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The technologies

• Applications centric development Decoupled
  development
• Special purpose languages / Intermediate
  Representations
• Deferred commitment
• Clinical before technical
• Logic based ontologies
• Models of clinical significance
• Models of clinical use
• Models of EHRs
• Web services Grid technology
• Authentication/authorisation/accounting
• Distributed directories LDAP
• Service discovery

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Decoupled development using Conceptual Lego

• If we manage the connectors and the pieces the
  users can build most things for themselves
• Without compromising quality

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Applications centric Development
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Loosely Coupled Development
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The templates are more important than the
underlying formalism

• "Open fixation of a fracture of the neck of the
  left femur"
• MAIN fixing
• ACTS_ON fracture
• HAS_LOCATION neck of long bone
• IS_PART_OF femur
• HAS_LATERALITY left
• HAS_APPROACH open

Intermediate Representations are critical
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complex underpinnings can will change
(SurgicalProcess which isMainlyCharacterisedBy
(performance which isEnactmentOf
(SurgicalFixing which

• hasSpecificSubprocess (SurgicalAccessing
• hasSurgicalOpenClosedness
  (SurgicalOpenClosedness which
• hasAbsoluteState surgicallyOpen))

actsSpecificallyOn (PathologicalBodyStruc
ture which lt involves Bone
hasUniqueAssociatedProcess
FracturingProcess hasSpecificLocat
ion (Collum which
isSpecificSolidDivisionOf
(Femur which
hasLeftRightSelector
leftSelection))gt))))
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Decoupling Flexibility

• Use formality to permit flexibility
• Change need not mean instability
• Formality means effects can be predited
• Most users only need change in tightly controlled
  areas
• Lesson from the Semantic WebForking a natural
  part of development
• Harmless if strictly local
• Manageable if controlled from standard Lego
  templates
• Clean up cost
• 10-20 central effort is a reasonable target
• Necessary to cope with change and ignorance
• Evolution by annealing

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Scalable models of use
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Scalable models of Use PENPAD
FRACTURE SURGERY
250,000 forms from 10,000 FactsFractal
tailoring
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Scalable models of useFractal tailoring forms
for clinical trials
Hypertension
Hypertension
Idiopathic Hypertension
Idiopathic Hypertension
In our companys studies
In our companys studies
In Phase 2 studies
In Phase 2 studies
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It can work

• The Lessons of GALEN
• Loosely coupled development based on formal
  ontologies works
• Coherence without uniformity
• 90 of work done locally
• Ontologies can be modular rather than monolithic
• Plug and play terminology development
• The Lessons of PENPAD
• Models of use based on formal ontologies scale
• 250,000 forms from 10,000 facts
• The Lessons of the Semantic Web
• It works for knowledge management
• Growing user community outside of medicine
• No longer rocket science

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So what areLogic based ontologies
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Logic-based Ontologies Conceptual Lego
SNPolymorphism of CFTRGene causing Defect in
MembraneTransport of ChlorideIon causing Increase
in Viscosity of Mucus in CysticFibrosis
Hand which isanatomicallynormal
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Logic based ontologies

• A formalisation of semantic nets, frame systems,
  and object hierarchies via KL-ONE and KRL
• is-kind-of implies (logical
  subsumption)
• Dog is a kind of wolf meansAll dogs are
  wolves
• Modern examples DAMLOIL /OWL?)
• Older variants LOOM, CLASSIC, BACK, GRAIL,
  K-REP,

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Logic Based Ontologies The basics
Validating (constraining cross products)
Primitives
Descriptions
Definitions
Reasoning
Thing
red partOf Heart
red partOf Heart
(feature pathological)
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Building with Conceptual Lego
Species
Genes
Function
Disease
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Avoiding combinatorial explosions

• The Exploding Bicycle From phrase book to
  dictionary grammar
• 1980 - ICD-9 (E826) 8
• 1990 - READ-2 (T30..) 81
• 1995 - READ-3 87
• 1996 - ICD-10 (V10-19 Australian) 587
• V31.22 Occupant of three-wheeled motor vehicle
  injured in collision with pedal cycle, person on
  outside of vehicle, nontraffic accident, while
  working for income
• and meanwhile elsewhere in ICD-10
• W65.40 Drowning and submersion while in bath-tub,
  street and highway, while engaged in sports
  activity
• X35.44 Victim of volcanic eruption, street and
  highway, while resting, sleeping, eating or
  engaging in other vital activities

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The Cost Normalising (untangling) Ontologies
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The Cost Normalising (untangling)
OntologiesMaking each meaning explicit and
separate
PhysSubstance Protein ProteinHormone
Insulin Enzyme Steroid
SteroidHormone Hormone ProteinHormone
Insulin SteroidHormone
Catalyst Enzyme
PhysSubstance Protein ProteinHormone
Insulin Enzyme Steroid
SteroidHormone Hormone
ProteinHormone Insulin
SteroidHormone Catalyst Enzyme
build it all by combining simple trees
Hormone Substance playsRole-HormoneRole Pro
teinHormone Protein playsRole-HormoneRoleS
teroidHormone Steroid playsRole-HormoneRole
Catalyst Substance playsRole
CatalystRole Insulin ? playsRole HormoneRole
Enzyme ?? Protein playsRole-CatalystRole
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But none of it works without toolsNone of it
works without communication cooperation
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Communicating software environments
Environments rather than servers

• Clinical users - care and review
• Environments for entering retrieving information
• Methodologies for measuring and monitoring
  quality of information
• Human factors, language technology, fractal
  tailoring to needs
• Application developers
• Configuration tools much more than terminology
  servers
• The key to success
• Ontology authors
• Tools for distributed loosely coupled authoring
• Ontology managers (the gurus)
• Tools for reconciliation, change management,
  meta-authoring of templates

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Summary of Arguments

• The priorities are clinical needs supported by
  applications supported by terminology
• Unless they serve clinical needs, applications
  are useless
• Unless they serve applications, terminologies are
  useless
• Unless used reliably, terminologies are
  meaningless
• Meaning is a social construct
• Clinical quality should be our watchword
• Useful and usable to clinical users, developers,
  reviewers, authors
• Requires models of use clinical significance
• Requires tools and environments
• In an open evolving world, open managed evolution
  is the only plausible way forward
• Participation and control are the issues not
  money
• Current technology gives us the opportunity to
  cope
• If we let development follow need
• If we use them to the full
• 19th century methods wont cope with 21st
  century problems

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Making Terminologies useful and
usableClinical Terminologies in the 21st
Century What are they for? What might they
look like?

• Alan RectorBio and Health Informatics
  Forum/Medical Informatics GroupDepartment of
  Computer ScienceUniversity of Manchester
• rector_at_cs.man.ac.ukwww.cs.man.ac.uk/mig
  img.man.ac.ukwww.clinical-escience.orgmygrid.man
  .ac.uk