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The Future of Health Information

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Title: The Future of Health Information


1
The Future of Health Information
  • Barry Smith
  • Ontology Research Group
  • Center of Excellence in Bioinformatics and Life
    Sciences
  • University at Buffalo
  • ontology.buffalo.edu/smith

2
Collaborations
  • National Center for Biomedical Ontology
    (http//NCBO.us)
  • WHO Collaborating Center for Terminology
  • Cleveland Clinic Semantic Database
  • SNOMED CT Disease Ontology
  • German national Electronic Health Record
    initiative Health Version 11

3
Overview of this talk
  • The role of ontology
  • The role of HL7
  • The future of health information

4
Overview of this talk
  • The role of ontology
  • The role of HL7
  • The future of health information

5
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6
we need to know where in the body, where in the
cell
we need to know what kind of disease process
we need semantic annotation of data
we need ontologies
7
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8
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9
Ontologies are
systems of terms for annotating dataThey are
controlled vocabularies designating the types of
entities in realityData designate the instances
of these types
10
  • cellular locations
  • molecular functions
  • biological processes
  • used to annotate the entities represented in the
    major biochemical databases
  • thereby creating integration across these
    databases

The Gene Ontology A set of standardized
textual descriptions of
11
what cellular component?
what molecular function?
what biological process?
12
The process of data annotation
  • yields a slowly growing computer-interpretable
    map of biological reality within which major
    databases are automatically integrated in
    semantically searchable form

13
But now
need to extend the methodology to other domains,
including clinical medicine ? need disease,
symptom (phenotype) ontologies
14
The Problem
need for prospective standards to ensure mutual
consistency and high quality of clinical
counterparts of GO need to ensure consistency of
the new clinical ontologies with the basic
biomedical sciences if we do not start now, the
problem will only get worse
15
The Solution
  • establish common rules governing best practices
    for creating ontologies and for using these in
    annotations
  • apply these rules to create a complete suite of
    orthogonal interoperable biomedical reference
    ontologies

16
First step (2003)
  • a shared portal for (so far) 58 ontologies
  • (low regimentation)
  • http//obo.sourceforge.net ? NCBO BioPortal

17
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18
Second step (2004)reform efforts initiated,
e.g. linking GO to other OBO ontologies to ensure
interoperability
GO

Cell type

Osteoblast differentiation Processes whereby an
osteoprogenitor cell or a cranial neural crest
cell acquires the specialized features of an
osteoblast, a bone-forming cell which secretes
extracellular matrix.
New Definition
19
Third step (2006)
The OBO Foundryhttp//obofoundry.org/
20
  • a family of interoperable gold standard
    biomedical reference ontologies to serve the
    annotation of
  • scientific literature
  • model organism databases
  • clinical data
  • experimental results

The OBO Foundry
21
Compare the UMLS Metathesaurus
  • a system of post hoc mappings between independent
    source vocabularies
  • built by trained experts
  • massively useful for information retrieval and
    information integration
  • creates out of literature a semantically
    searchable space

22
for UMLS
  • local usage respected
  • regimentation frowned upon
  • cross-framework consistency not important
  • no concern to establish consistency with basic
    science
  • different grades of formal rigor, different
    degrees of completeness, different update
    policies
  • no path towards improvement
  • no path towards support for logical reasoning

23
The OBO Foundry is a prospective standard
  • designed to guarantee interoperability of
    ontologies from the very start (contrast to post
    hoc mapping)
  • established March 2006
  • 12 initial candidate OBO ontologies focused
    primarily on basic science domains
  • several being constructed ab initio
  • now 16 ontologies

24
Ontology Scope URL Custodians
Cell Ontology (CL) cell types from prokaryotes to mammals obo.sourceforge.net/cgi- bin/detail.cgi?cell Jonathan Bard, Michael Ashburner, Oliver Hofman
Chemical Entities of Bio- logical Interest (ChEBI) molecular entities ebi.ac.uk/chebi Paula Dematos, Rafael Alcantara
Common Anatomy Refer- ence Ontology (CARO) anatomical structures in human and model organisms (under development) Melissa Haendel, Terry Hayamizu, Cornelius Rosse, David Sutherland,
Foundational Model of Anatomy (FMA) structure of the human body fma.biostr.washington. edu JLV Mejino Jr., Cornelius Rosse
Functional Genomics Investigation Ontology (FuGO) design, protocol, data instrumentation, and analysis fugo.sf.net FuGO Working Group
Gene Ontology (GO) cellular components, molecular functions, biological processes www.geneontology.org Gene Ontology Consortium
Phenotypic Quality Ontology (PaTO) qualities of anatomical structures obo.sourceforge.net/cgi -bin/ detail.cgi? attribute_and_value Michael Ashburner, Suzanna Lewis, Georgios Gkoutos
Protein Ontology (PrO) protein types and modifications (under development) Protein Ontology Consortium
Relation Ontology (RO) relations obo.sf.net/relationship Barry Smith, Chris Mungall
RNA Ontology (RnaO) three-dimensional RNA structures (under development) RNA Ontology Consortium
Sequence Ontology (SO) properties and features of nucleic sequences song.sf.net Karen Eilbeck
25
RELATION TO TIME GRANULARITY CONTINUANT CONTINUANT CONTINUANT CONTINUANT OCCURRENT
RELATION TO TIME GRANULARITY INDEPENDENT INDEPENDENT DEPENDENT DEPENDENT
ORGAN AND ORGANISM Organism (NCBI Taxonomy) Anatomical Entity (FMA, CARO) Organ Function (FMP, CPRO) Phenotypic Quality(PaTO) Biological Process (GO)
CELL AND CELLULAR COMPONENT Cell (CL) Cellular Component (FMA, GO) Cellular Function (GO) Phenotypic Quality(PaTO) Biological Process (GO)
MOLECULE Molecule (ChEBI, SO, RnaO, PrO) Molecule (ChEBI, SO, RnaO, PrO) Molecular Function (GO) Molecular Function (GO) Molecular Process (GO)
Building out from the original GO
26
  • OBO low-regimentation ontology portal
  • OBO Foundry high-regimentation collaborative
    initiative to create a gold standard suite of
    interoperable ontologies

The vision
27
  • Common Anatomy Reference Ontology
  • Disease Ontology (DO) SNOMED CT
  • Biomedical Image Ontology (BIO)
  • Environment Ontology (EnvO)
  • Biobank Ontology (BrO)
  • Clinical Trial Ontology (CTO) with WHO Global
    Trial Bank, Immune Tolerance Network, ACGT
    Advancing Genomics Clinical Trials in Cancer EU
    IP

Ontologies under construction
28
Clinical Trial Ontology
  • part of a larger project called the Ontology for
    Biomedical Investigations (OBI)

29
OBI
  • controlled vocabulary for biomedical
    investigations including
  • protocols
  • instrumentation
  • material
  • data
  • types of analysis and statistical tools applied
    to the data

http//obofoundry.org/
30
Clinical Trial Ontology
  • To serve merger of data schemas
  • To serve flexibility of collaborative clinical
    trial research
  • To serve design and management of clinical trials
  • To serve data access and reuse send me all
    trials which ...

31
Ontology vs. Database Schema
  • Separate development of data schemas and
    information models (HL7) and terminologies such
    as SNOMED CT
  • the two do not work together

32
Ontology vs. Database Schema
  • diabetes gt disease
  • diabetes gt string
  • temperature gt quality
  • temperature gt integer

33
CTO
34
CTO Continuant
35
CTO Occurrent
36
Clinical Trial Ontology Working Group
  • http//www.bioontology.org/wiki/
  • Workshop on May 16-17, 2007

37
  • The role of ontology
  • The role of HL7
  • The future of health information

38
HL7 V3
  • the data standard for
  • biomedical informatics
  • http//aurora.regenstrief.org/schadow/
    HL7TheDataStandardForBiomedicalInformatics.ppt

39
HL7 V2
  • a workable messaging standard
  • faced the problem of local dialects
  • seeks to solve this problem by having all HL7
    artifacts conform to a single Reference
    Information Model (the RIM)

HL7 V3
40
After 10 years?And many attempts?And gigantic
investments of energy and funding?
is there a single, successful RIM-implementation?
41
There are clear examples of failure of
billion-dollar implementations resting on the RIM
and of programmers involved in such failures who
are tearing out their hair, and blaming HL7
42
Is it justified, in these circumstances, to
promote HL7 V3 as an ISO Standard in the domain
of patient care?
43
One indispensable foundation for a successful
standard
  • a correct and uniform interpretation of its
    basic terms
  • Act
  • Participation
  • Entity
  • Role
  • ActRelationship
  • RoleLink

44
Demonstrably, the HL7 community does not
understand its own basic terms
  • Sometimes Act means information about an act
  • Sometimes Act means real-world action
  • Sometimes Act means a mixture of the above
  • Sometimes in the very same sentence

45
Consequences of unclarity here
  • Different user groups have interpreted the same
    classes in different ways
  • Different message specifications used different
    interpretations
  • This recreates interoperability problems
  • Can we be sure that these problems will not lead
    to incidents relevant to patient safety?

46
Even with clarity and clear documentation the
RIM would still be in bad shape
http//hl7-watch.blogspot.com/
47
Where are diseases
  • Acts ?
  • Things, Persons, Organizations ?
  • Participations ?
  • Roles ?
  • ActRelationships ?
  • RoleLinks ?

48
The HL7 Clinical Genomic Standard
  • defines an allele as the observation of an allele
  • defines a phenotype as the observation of an
    observation

49
The 35 bn. NHS Program Connecting for Health
  • has applied the RIM rigorously, using all the
    normative elements, and it discovered that it
    needed to create dialects of its own to make the
    V3-based system work for its purposes (it still
    does not work)

50
The RIM has no coherent answer
  • Basic categories cannot be agreed upon even for
    common phenomena like snakebites.
  • HL7 V3 dialects are formed and the RIM does not
    do its job.

51
The moral of this story
  • Dont claim to be
  • the data standard for biomedical
    informatics
  • until you have a system that works
  • http//aurora.regenstrief.org/schadow/
    HL7TheDataStandardForBiomedicalInformatics.ppt

52
  • The role of ontology
  • The role of HL7
  • The future of health information

53
A New Paradigm for Health Information
  • How achieve semantic interoperability amongst
    healthcare applications?
  • Through referent tracking

www.org.buffalo.edu/RTU
54
The myth of unambiguous understanding through
biomedical terminologies
How many numerically different disorders are
listed here ?

How many different types of disorders are listed
here ?

How many disorders have patients 5572, 2309 and
298 each had thus far in their lifetime ?

cause, not disorder
55
Does seeing the labels help ?
56
We have unique IDs
  • for patients
  • for healthcare deliverers
  • for images
  • for invoices

57
Lets introduce unique IDs
  • for everything that is mentioned in the record
  • lesions
  • fractures
  • presentings
  • surgical procedures
  • http//sourceforge.net/projects/rtsystem
  • IUI instance unique identifier

58
Better public health statistics
59
Better reasoning over health information
  • John Does John Smiths
  • liver liver
  • tumor tumor
  • was treated was treated
  • with with
  • RPCIs RPCIs
  • irradiation device irradiation device

John Does liver tumor
was treated with RPCIs
irradiation device
60
Application principles
Ontology
continuant
disorder
person
CAG repeat
EHR
Juvenile HD
IUI-1 affects IUI-2 IUI-3 affects
IUI-2 IUI-1 causes IUI-3
Referent Tracking Database
61
Goal A New Form of Evidence Based Medicine
  • Now
  • Decisions based on the outcomes of (reproducible)
    results of well-designed studies
  • Guidelines and protocols
  • Evidence is hard to get, takes time to
    accumulate.
  • Future
  • Each discovered fact or expressed belief should
    instantly become available as contributing to the
    total body evidence, wherever its description is
    generated.
  • Data eternally reusable independent of the
    purpose for which they have been generated.
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