OWL for Clinical Models

advertisement
KCOM
Kaiser Clinical Ontology Modeling
Peter Hendler and Michael Rossman
With some copyrighted material from
Matthew Horridge
Why Do This?
• Last year we stressed the cost savings
and simplicity added if different
healthcare systems use a similar
(canonical) base model
• This year we will show significant
additional advantages if the model is
created using Web Ontology Language
OWL and Description Logics (DL)
Quick Review Why
Canonical Models
Clinical Models
Why Do We Need Them?
Electronic health care systems have evolved separately over the decades
Most were created in isolation to solve one particular domain problem
(Pharmacy, Lab, Radiology, Clinical Notes, Scheduling, Billing, Admissions
Discharges and Transfers or Clinical Decision Support)
As a result they all have their own models, and they can not share clinical
data without complex expensive interfaces being built
4
Clinical Models
How Do These Systems Interoperate?
All systems have a “data model” whether it is explicitly designed or is just the
result of how the systems store data
You must map the “data model” from one system to the “data model” for the
other system if they are to share data.
This requires too many expensive interfaces that goes up by N squared for
N systems.
Every mapping or interface results in the loss of some meaning
5
Current Information Modeling in KP
Current state of information modeling at KP
All applications are proprietary or legacy “ad hoc” “one-off”
Each system has a unique persistence layer and data model
Each new project generates a new relational database and new analytics
Projects require the creation
of unique interfaces with all
the other programs and systems
Interfacing and integrating
programs and systems is both
expensive and time consuming
6
Canonical Information Modeling implies
A standard representation of clinical data and the implied mapping back
from each application to that (in common) representation
Interoperability is inherently built into
all clinical systems that are based on a
canonical model
At a minimum, if each legacy system
can import and export the canonical
data model, interfacing becomes
much simpler (just N instead of
N(N-1)/2)
7 CANONICAL: conforming to a general rule or acceptable procedure : orthodox [merriam-webster.com]
Why Do This in
OWL?
• How are relevant research and
outcome studies done now?
Some Example
Questions
• Do patients on NSAIDs get more GI
bleeds?
• Do RA patients on biologic DMARDs
get more non pulmonary TB?
• Do RA patients on non biologic
DMARDs do as well as patients on any
DMARDs plus biologic DMARDS?
And how are these
questions answered
today?
By Manual Chart
Review Kat
• This does not change with an Electronic
Health Record of unstructured data.
• Whether paper or electronic, non
structured text and non Ontological
terminologies (like ICD) require
individual reading and evaluation by a
reviewer
By using OWL in
KCOM, these queries
can be automated!
Outline
• Three kinds of modeling kats
• Why use SNOMED / CMT, and OWL?
• What happens when you model the
HL7 RIM backbone in OWL?
• Very Short Intro to OWL and Protege
Outline
• How does KCOM address these
problems?
• The generalizable part of the model
valid for all sub specialty domains
• The specialized parts of the
Rheumatoid Arthritis Assessment Model
(RAAM)
• The “Clinical Stories” used to create
KCOM
• Walk through one semantic query
Three Kinds of
Modelers
This is often the cause of communication problems
between IT people with different training
backgrounds and different ways of looking at
things.
Database Kitteh
Knows about RDBMS
19
Kind of comfortable
Object Oriented Kat
Thinks in Unified Modeling Language (UML). Has lots of friends.
8
20
Ontology Kat
Is lonely, and misunderstood. But very powerful. He made SNOMED
9
21
Why Use SNOMED /
CMT, OWL?
Medical Terminology
•SNOMED
•Ontology Description Logic
•Concerned with clinical meaning, not billing
•Fine grained enough to be clinically
meaningful
•Can be used for Outcomes measurements
•Can be used by machines to make inferences
Inferences possible with SNOMED
•Strep throat is caused by streptococcus
•Pneumococcal pneumonia is caused by
pneumococcus
•Streptococcus and pneumococcus are both sub
types of gram positive cocci
•Therefore both pneumococcal pneumonia and
strep throat are gram positive cocci infections.
First Example Question
Do patients on NSAIDs get more GI bleeds?
Without SNOMED or Ontology, clinical experts
have to know the names and codes of all
medications that are “a kind of” NSAID.
They have to know all the names of the
hundreds of ICD9 codes that are “a kind of” GI
bleed.
This requires Chart Review Kat and is error
25
Second Example Question
•
•
•
•
•
Do RA patients on biologic DMARDs get
more non pulmonary TB?
How many ICD9/10 codes are “a kind of” RA
How many ICD9/10 codes are “a kind of”
DMARD?
How many ICD9/10 codes are “a kind of”
non pulmonary TB?
Very difficult to do manually. Automatically
done by SNOMED semantic search!
26
What happens when you model the HL7
RIM backbone in OWL?
4/8/2013
Kaiser Permanente © 2013
28
A Very Short Intro To
OWL and Protege
It’s all about triples
Protege
• Three main views
• Taxonomy: Only “Is A”
• OWL-Viz: Only “Is A”
• Definition: Where the triplets are
defined
Taxonomy
View
OWL Viz View
Definition View
OWL is all about Triplets
Domain and Range
Subclasses
Define CheeseyPizza
Define MargheritaPizza
Define SohoPizza
A Stated Taxonomy View
A Stated OWL-Viz View
An Inferred OWL-Viz View
Stated and Inferred Taxonomies
How It Looks To
The Reasoner
IsA
How It Looks
To the Reasoner
IsA
They could be
Myocardial Infarction and Acute Myocardial
Infarction
The right side is the child of (subsumed by) the left
side
Or they could be Pneumonitis and Infectious
Pneumonitis
To the Reasoner it doesn’t matter, as long as it can
keep track of all the symbols.
It is manipulating symbols but the result makes
perfect sense and results in clinically useful
46
inferences
What Does RAAM
Model?
4/8/2013
Kaiser Permanente © 2013
48
What Goes In and Out of The Brain
Not trying to model the rules, or what happens in
the brain of the expert who makes the decisions
Only modeling the data that a human expert
clinician specialist brain needs to make it’s own
assessment
Once the brain has made the assessment then we
model the decision
This is “Decision Support” in a new way, no rules or
suggested solutions, just support the decision
maker with data
49
The Reasoner Completely Understands the Entire Model
Semantics
Detects Inconsistencies
Makes Logical Inferences
Classifies Clinical Data Automatically
4/8/2013
Kaiser Permanente © 2013
50
The Reasoner Knows All About The Whole Model
4/8/2013
Kaiser Permanente © 2013
51
The generalizable part of the model valid
for all sub specialty domains
Some example views into the model
4/8/2013
Kaiser Permanente © 2013
53
4/8/2013
Kaiser Permanente © 2013
54
4/8/2013
Kaiser Permanente © 2013
55
4/8/2013
Kaiser Permanente © 2013
56
4/8/2013
Kaiser Permanente © 2013
57
The Medical
Specialty Domain
Specific Part
4/8/2013
Kaiser Permanente © 2013
59
4/8/2013
Kaiser Permanente © 2013
60
4/8/2013
Kaiser Permanente © 2013
61
4/8/2013
Kaiser Permanente © 2013
62
4/8/2013
Kaiser Permanente © 2013
63
4/8/2013
Kaiser Permanente © 2013
64
How KCOM is bound
to SNOMED
Individual Terms Bound to SNOMED-CT
4/8/2013
Kaiser Permanente © 2013
66
The Clinical Stories
Used to Design
KCOM
Based on clinical
cases
• When KCOM was first designed, we
took six examples of clinical notes from
Rheumatoid Arthritis Assessments
• They covered various clinical scenarios
• We will select six specific clinical
statements from case number one and
explore them in depth
• We will look at them in English, UML
and finally in KCOM OWL
4/8/2013
Kaiser Permanente © 2013
69
4/8/2013
Kaiser Permanente © 2013
70
4/8/2013
Kaiser Permanente © 2013
71
4/8/2013
Kaiser Permanente © 2013
72
4/8/2013
Kaiser Permanente © 2013
73
4/8/2013
Kaiser Permanente © 2013
74
4/8/2013
Kaiser Permanente © 2013
75
4/8/2013
Kaiser Permanente © 2013
76
4/8/2013
Kaiser Permanente © 2013
77
4/8/2013
Kaiser Permanente © 2013
78
4/8/2013
Kaiser Permanente © 2013
79
4/8/2013
Kaiser Permanente © 2013
80
4/8/2013
Kaiser Permanente © 2013
81
4/8/2013
Kaiser Permanente © 2013
82
4/8/2013
Kaiser Permanente © 2013
83
4/8/2013
Kaiser Permanente © 2013
84
4/8/2013
Kaiser Permanente © 2013
85
4/8/2013
Kaiser Permanente © 2013
86
4/8/2013
Kaiser Permanente © 2013
87
And One Semantic
Query Example
2
Compare the number of gastrointestinal
bleeds in RA patients in the following groups.
Those who have and have not taken NSAIDs
Break this up into steps.
First find Patients with RA
PersonAsPatient and subjectOf Observation
hasAssociatedFinding Rheumatoid Arthritis
(we will be using this basic query in all the other
examples)
Now using this limited cohort of RA patients
continue to query as follows to find the sub
groups.
Now we find two sub groups, those who are not on NSAIDs
and those that are.
(we can do this for current use or past use which ever we
choose)
PersonAsPatient and subjectOf Observation has
MedicationAdministration has AssociatedMedication some
NSAID.
(note the subsumption here is very useful. Otherwise you
have to accumulate all the meds that are NSAIDs with the
help of a clinical/pharmacy expert. In the KCOM case this
clinical knowledge is part of the model itself)
PersonAsPatient and subjectOf Observation has
MedicationAdministration has AssociatedMedication
ONLY NOT NSAID (we will not explain the difference
between “some” and “only” but this query gets those NOT
on any kind of NSAID.
Now we have these two groups and we need to find in
each one who has had GI bleed. The ICD9 or ICD10 has
many different diagnosis that are all some kind of GI bleed.
Not being able to use SNOMED subsumption is a fatal
drawback. Because we are using SNOMED and because
we are using OWL in our base clinical model we can
simplify this complex query into.
PersonAsPatient and SubjectOf Observation
hasAssociatedFinding some <<bleeding and has
finding site gastro-intestinal structure>>
(the latter part is a post coordinated SNOMED
expression I used just for demonstration We could
also use instead the pre-defined SNOMED term
74474003:GastroIntestinal Hemorrhage (disorder)
It is important to point out. There are too many ICD9
and ICD10 codes that are all a kind of
“Gastrointestinal Hemorrhage” and unless you
happen to know all of them, you will miss some
patients.
This SNOMED Description Logic Subsumption query
will catch all of them even if you don’t know what they
are called. Even a clinical expert could not be
expected to recall every possible kind of ICD9 or 10
term that is some kind of gastrointestinal bleed.
Conclusions
• Last year we stressed the advantages
(in time money and simplicity) of using
standard (canonical) models to
integrate clinical systems
Conclusions
• This year we show that by using models
based on Description Logic (OWL) and
SNOMED we are able to use Semantic
Searching to automate important and
complex queries that would otherwise
need manual chart reviewers and take
much more time and expense.
Does Ontology Kat
Work Well With
OWL?
Abbreviations
•
•
•
•
•
•
•
•
RAAM: Rheumatoid Arthritis Assessment Model
RDBMS: Relational DataBase Management System
OO: Object Oriented
KCOM: Kaiser Clinical Ontology/OWL Model
DMARD: Disease Modifying AntiRheumatic Drug
NSAID: Non Steroidal AntiInflammatory Drug
OWL: Web Ontology Language
DL: Description Logics
Download
Related flashcards

Rheumatology

58 cards

Rheumatology

57 cards

Create Flashcards