From concept to clinic
Good …. but not enough
• Standardisation of terms with SNOMED and ICD-10
coding provides shareable data but doesn’t retain
the clinical insights and knowledge required for:
– Stratified medicine
– Specialised commissioning
– Genomic medicine
Stratified medicine
The treatment for this
instance of
rheumatoid arthritis
occurring in a patient
with these
characteristics
The treatment for
rheumatoid arthritis
Will require very large sets of
well organised, routinely
collected clinical data
Information loss
• Reducing a diagnosis to a single piece of
data, however coded, removes almost all
clinical insights
A diagnosis cluster with SNOMED CT coding
Seropositive
rheumatoid arthritis
SCTID:239791005
Autoimmune disorder
SCTID:8582009
Rheumatoid arthritis
SCTID:69896004
Arthritis
SCTID:3723001
Seronegative
rheumatoid arthritis
SCTID:239792003
Adding clinical insight
Lung cancer
Smoking related disease
High cost drug disease
Disease associated with
increased cardiovascular
mortality
Rheumatoid arthritis
Adding clinical insight
Lung cancer
Smoking related disease
High cost drug disease
Disease associated with
increased cardiovascular
mortality
Rheumatoid arthritis
Concept map of arthritis
Adding clinical insight
Lung cancer
Smoking related disease
High cost drug disease
Disease associated with
increased cardiovascular
mortality
Rheumatoid arthritis
Capturing phenotype
• Phenotype definition:
– The set of observable characteristics of an individual or a
disease
• For an individual:
– Body shape
– Skin colour
– (Lifestyle choices)
• For a disease:
– Which manifestations of the disease are present or absent?
– How severe or active are those manifestations?
Capturing phenotype – how not to do it
• Any attempt to represent subgroups of complex diseases with
single codes will fail.
• Example: A disease has eight individual, mutually independent
manifestations each of which can be present or absent.
• It follows, therefore that there are 28 (= 256) different phenotypic
subsets for that disease and that the required coding set will be
too large to manage.
• Disease and phenotype must be separated
An ORCHID Core Data Set
Diagnosis + core + longitudinal data
Seropositive
rheumatoid arthritis
Drug monitoring:
FBC
LFTs
U&Es
Disease monitoring:
DAS 28
HAQ
CRP
ORCHID Class Hierarchies
•
ORCHID class hierarchies are Directed Acyclic Graphs
•
Used for diagnosis, medications, laboratory tests, etc
•
Three-level, with Core Data Sets to represent phenotypes
•
Nodes can also carry clinical coding (SNOMED, ICD-10, etc)
Data Dictionary
•
The Data Dictionary combines ISO-13696 and HL7 CDA
structures
•
Elements
•
Clusters of Elements
•
Entries
•
Arranged (reused) in
–
Sections
–
Compositions (HL7 CDA Document)
• Messages
• Views
• Forms
Assessments – Graphical Entry
Graphical Entry of Fracture Record
In conclusion
• Electronic health records will provide unprecedented opportunities
to exploit routinely collected clinical data for many purposes
• We can only take advantage of the opportunities presented by
EHRs if we develop standardised data sets mapped to
appropriate codings
• SNOMED CT is conceptually rich but needs content improvement
and ongoing ownership and stewardship of specialty reference
sets
• Optimum use of clinical data requires complex information models
linked to simple user interfaces
• Clinicians working with specialist societies and their Royal
Colleges must play a central role in these developments