Detailed Clinical Models
WC3, March 29, 2007
Acknowledgements
Joseph (Joey) Coyle
Thomas (Tom) Oniki
Craig Parker
Yan Heras
Roberto Rocha
Harold Solbrig
and many others …
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Agenda
What are the capabilities we want in the new system?
Why are detailed clinical models essential?
What are the implications for software development if
we do detailed clinical models?
The clinical element model in a nutshell
Relationship of CE’s to other models
Detailed clinical models and the PRD process
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The essentials of the proposition
The motivation for creating detailed clinical models is
to support the capabilities we want the system to
have
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Longitudinal conception to grave EHR
Real-time patient specific decision support
Sharing of data within and outside of the enterprise
Clinical and administrative research and analysis
Sharing of decision support logic and protocols
Standard, open, modular, application development
environment
The only feasible way to meet these goals is to
support detailed clinical models for clinical data
that reference standard coded terminology
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A Detailed Clinical Model
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Longitudinal conception to grave
EHR
• Comprehensive of all categories of clinical data
– History, physical, pharmacy, laboratory, …
• All types of data
– Text, numeric, coded, images, sounds, …
• Retained for 100+ years
• The legal record for all or part of the patient’s data
• The data will outlive any particular application,
service, programming language, database, or
message format
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Real time, patient specific, decision
support
Alerts
Advising
• Potassium and digoxin
• Coagulation clinic
• Antibiotic assistant
Reminders
• Blood ordering
• Mammography
• Immunizations
Protocols
• Ventilator weaning
• ARDS protocol
• Prophylactic use of
antibiotics in surgery
Critiquing
Interpretation
• Blood gas interpretation
Management – purpose
specific aggregation and
presentation of data
• DVT management
• Diabetic report
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Sharing of data
Sharing within the enterprise
• Between ADT/Registration, LIS, RIS, Labor and Delivery
Sharing outside the enterprise
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Adverse event reporting (drugs and devices)
Morbidity and mortality reporting
Patient safety reporting
Quality of care reports - HEDIS measures
Regional Health Information Networks
Bio-surveillance, infectious disease reports
Cancer registries and disease specific repositories
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Clinical and administrative research
and analysis
Clinical research at Intermountain
•
•
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Effects of inducing labor prior to 39 weeks
Length of stay with TURPs
Whole blood use
Human genomic/proteonomic correlations
Health population statistics
Clinical trials
• Post-marketing information on drugs and devices
• Enrollment
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National and international sharing of
decision support modules
There are more rules and knowledge to
represent than a single entity can create
Initiatives to allow sharing
• Arden syntax
• HL7 Decision Support Technical Committee
• SAGE – Shared Active Guideline Environment
– $18 million dollar NIST contract to IDX
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Creating a new kind of Healthcare IT
market place
Separate application development (front end)
from data persistence (back end)
Common detailed models and terminology are
shared public infrastructure, not market
advantage or product discriminator
Competition is based on making the best
application and/or providing the best back end
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Order Entry API (adapted from Harold Solbrig)
Application
Interface
Service
Data
IHC Order
Entry
Update Medication Order
COS
VA
Order
Services
Update PharmacyOrder
WHERE orderNumber = “4674” …
MUMPS
Database
Order Entry API – Different Client,
Same Service (adapted from Harold Solbrig)
Application
Interface
Service
Data
Dept
of
Defense
Update Medication Order
COS
VA
Order
Services
Update PharmacyOrder
WHERE orderNumber = “4674” …
MUMPS
Database
Order Entry API (adapted from Harold Solbrig)
...
Application
Interface
Service
Data
COS
What things need to be in place to
create a new market place?
Standard set of detailed clinical data models
coupled with…
Standard coded terminology
Standard API’s (Application Programmer
Interfaces) for healthcare related services
Open sharing of models, coded terms, and
API’s
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DCM – Detailed Clinical Models
Create a national and international
collaboration
• Independent Not-For-Profit organization, or as part
of IHE or HL7
• Create an open shared library of clinical models
• Associated vocabulary content linked to the models
• Providers as the primary participants/drivers
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Agenda
What are the capabilities we want in the new system?
Why are detailed clinical models essential?
What are the implications for software development if
we do detailed clinical models?
The clinical element model in a nutshell
Relationship of CE’s to other models
Detailed clinical models and the PRD process
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Arden Syntax, “the curly braces problem”
data:
/* total calcium in mg/dL */
calcium := read last
{'06210519','06210669','CALCIUM'}
Etc.
evoke
storage_of_calcium;
logic:
/* if creatinine is present and greater than 6, then
stop now */
IF creatinine is present THEN
IF creatinine is greater than 6.0 THEN
conclude false
ENDIF
ENDIF
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The goal
Have a shared logical model for detailed clinical data
that is the basis for data referenced in shared
guidelines
The model should link information models and
standardized coding schemes/reference
terminologies
There should be a standard logical model and syntax
for these models
There should be a repository where these models
can be accessed
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Need for coded data
Tom East’s experience with “Oral meds”
• Oral, ORAL, Oral, ORALLY, Orally, ORALY, OR, or, PO,
P.O., P.O, PO., po, per os, by mouth, … (26 variants)
Observation #1: You can not anticipate all of the ways
that information can be recorded in free text.
Observation #2: You can not reliably execute real
time decision logic against free text data
Conclusion #1: You need coded data
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Need for a standard model
A stack of coded items is ambiguous (SNOMED CT)
• Numbness of right arm and left leg
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–
–
–
–
Numbness (44077006)
Right (24028007)
Arm (40983000)
Left (7771000)
Leg (30021000)
• Numbness of left arm and right leg
–
–
–
–
–
Numbness (44077006)
Left (7771000)
Arm (40983000)
Right (24028007)
Leg (30021000)
• Observation#3: You need to specify the order and roles of
the codes
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What if there is no model?
Site #1
Dry Weight: 70 kg
Site #2
Weight: 70 kg
Dry
Wet
Ideal
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Too many ways to say the same
thing
A single name/code and value
• Dry Weight is 70 kg
Combination of two names/codes and
values
• Weight is 70 kg
– Weight type is dry
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Model fragment in XML
Pre-coordinated representation
<observation>
<cd>Dry weight (LOINC 8340-2) </cd>
<value>70 kg</value>
</observation>
Post-coordinated
representation
(compositional)
<observation>
<cd>Weight (LOINC 3141-9) </cd>
<qualifier>
<cd> Weight type (LOINC 8337-8) </cd>
<value> Dry (SNOMED CT 13880007) </value>
<qualifier>
<value>70 kg</value>
</observation>
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Relational database implications
Patient
Identifier
Date and Time
Observation Type
Observation
Value
Units
123456789
7/4/2005
Dry Weight
70
kg
123456789
7/19/2005
Current Weight
73
kg
Patient
Identifier
Date and Time
Observation
Type
Weight type
Observation
Value
Units
123456789
7/4/2005
Weight
Dry
70
kg
123456789
7/19/2005
Weight
Current
73
kg
How would you calculate the desired weight loss
during the hospital stay?
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I have presented the most simple
examples. More complicated items:
Signs, symptoms
Diagnoses
Problem list
Family History
Use of negation – “No Family Hx of Cancer”
Description of a heart murmur
Description of breath sounds
“Rales in right and left upper lobes”
“Rales, rhonchi, and egophony in right lower lobe”
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Some Observations
• Note that what we’ve talked about are differences at the
“detail” level.
• The high level “model” of an Observation or a Med
Administration wasn’t the question.
• It was the codes, the value constraints, the qualifiers, etc.
that caused problems.
• We need consistent coded terminology and explicit,
consistent models.
• Even a single enterprise needs multiple models for a single
kind of data to support different user interfaces and different
levels of pre-coordination
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Pre and post coordinated models,
families of “iso-semantic” models
Model A (pre coordinated)
Model B (post coordinated)
Key: “Systolic BP Right Arm
Sitting”
Key: “Systolic BP”
Data: 120 mm/Hg
Data: 120 mm/Hg
Location: “Right Arm”
Position: “Sitting”
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Agenda
What are the capabilities we want in the new system?
Why are detailed clinical models essential?
What are the implications for software development if
we do detailed clinical models?
The clinical element model in a nutshell
Relationship of CE’s to other models
Detailed clinical models and the PRD process
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What do we model using CE’s?
All data in the patient’s EMR, including:
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Allergies
Problem lists
Laboratory results
Medication and diagnostic orders
Medication administration
Physical exam and clinical measurements
Signs, symptoms, diagnoses
Clinical documents
Procedures
Family history, medical history and review of symptoms
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How are Clinical Element models used?
Computer-to-Computer Interfaces
• Creation of maps from departmental/foreign system models
to the standard storage model
Core services
• Validation of data as it is stored in the database
Decision logic
• Basis for referencing data in decision support logic
Data entry screens, flow sheets, reports, ad hoc
queries
• Basis for application access to clinical data
Models do NOT dictate physical storage strategy
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Validation in data storage service (DSS)
Incoming
Message
CE models
DSS
Validate
DB
Tables
Codes and
Terms
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Implications
Data must be modeled before it is stored in the
database
Adequate resources must be allocated to
support the modeling
Modeling must be an essential part of the
development process
Tools need to be created to integrate use of
the models with development processes
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Agenda
What are the capabilities we want in the new system?
Why are detailed clinical models essential?
What are the implications for software development if
we do detailed clinical models?
The clinical element model in a nutshell
Relationship of CE’s to other models
Detailed clinical models and the PRD process
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EIM Subject Areas
Resource
Healthcare Organization
Agent
Schedule
Location
Financial
Notification
Clinical Activity
Patient
Healthcare Plan
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Which “level” of model to implement
as an object class?
Implement only the core model as a Java class
• Other levels of models represented as constraints
(interpreted metadata) on the core model
Every model is a Java class
• ~10,000+ classes
Something in the middle?
• Classes for patient, encounter, order, result, allergy
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“The” Clinical Element Model
Intermountain’s overall effort in the design of
detailed clinical models
Evolution and refinement of The Clinical Event
Model which Intermountain has been using for
the past 12 years.
• ~200 million instances of clinical data stored in our
repository.
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“A” Clinical Element Model
A conceptual model for representing a piece of
clinical data.
Examples
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Systolic Blood Pressure model
Heart Rate model
Lab Panel model
Order Model
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A Clinical Element Model
describes the constraints for
a piece of Clinical Information
Models describe the constraints for…
Type - The name of a
particular model
Key - Real world
concept. Links model
to an external coded
terminology.
Value Choice Possible ways to
convey the model’s
value.
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Value Choice
Data - Value
conveyed as an HL7
version 3 data type
Items - Value
conveyed by multiple
Clinical Elements
collectively
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A Simple Laboratory Observation
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A Panel containing 2 Observations
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Mods and Quals of the Value Choice
Mods - Component
CE’s which change
the meaning of the
Value Choice.
Quals - Component
CE’s which give more
information about the
Value Choice.
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The use of Qualifiers
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The use of Qualifiers
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The use of Modifiers
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The use of Modifiers
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Modeling with CEML
Naming the new type
<cetype name=“LabObservationQn”>
</cetype>
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Constraining the Key for a
Specializable Clinical Element
<cetype name=“LabObservationQn”>
<key domain=“LabObservationQn_DOMAIN_ECID”/>
</cetype>
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Constraining the Key for a
Specializable Clinical Element
<cetype name=“LabObservationQn”>
<key domain=“LabObservationQn_DOMAIN_ECID”/>
</cetype>
Or in Longhand…
<cetype name=“LabObservationQn”>
<constraint path=“key.domain” value=“LabObservationQn_DOMAIN_ECID”/>
</cetype>
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Constraining the HL7 datatype
<cetype name=“LabObservationQn”>
<key domain=“LabObservationQn_DOMAIN_ECID”/>
<data type=“pq”/>
</cetype>
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Constraining the units of PQ
<cetype name=“LabObservationQn”>
<key domain=“LabObservationQn_DOMAIN_ECID”/>
<data type=“pq”/>
<constraint path=“data.pq.units.domain” value=“UnitsQn_DOMAIN_ECID”/>
</cetype>
Or…
<cetype name=“LabObservationQn”>
<key domain=“LabObservationQn_DOMAIN_ECID”/>
<data type=“pq”>
<constraint path=“pq.units.domain” value=“UnitsQn_DOMAIN_ECID”/>
</data>
</cetype>
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Adding a qualifier
<cetype name=“LabObservationQn”>
<key domain=“LabObservationQn_DOMAIN_ECID”/>
<data type=“pq”/>
<qual name=“specimen” type=“Specimen” card=“0-1”/>
<constraint path=“data.pq.units.domain” value=“UnitsQn_DOMAIN_ECID”/>
</cetype>
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Modeling a Specific Clinical Element
<cetype name=“HematocritQn” base=“LabObservationQn”>
<key code=“Hematocrit_ECID”/>
<data type=“pq”/>
<qual name=“specimen” type=“Specimen” card=“0-1”/>
<constraint path=“data.pq.value” value=“0-100” type=“range” />
<constraint path=“data.pq.units.code” value=“PercentageUnit_ECID”/>
</cetype>
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Hard issues
What level of model becomes a Java class?
How do you make models easy to use in Java?
Opposition to this level of detailed models
Modeling of concepts and quantitative values in a
single language/paradigm
Huge diversity of modeling styles: how to be
consistent?
Defining computable connections between model and
externally defined terminology
Large number of models needed
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Questions?
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