Transcript CEMs_for_SHARP_100621_v3

Clinical Element Models (CEMs)
SHARP F2F Meeting
Mayo Clinic
June 21, 2010
Stanley M Huff, MD
#1
A Simple Model
#2
Use of detailed clinical models in SHARP
• Guide for data normalization widgets
• Target for structured output from NLP
• Logical structure for data payload in
NHIN Connect services
• Reference for data that participates in the
phenotype logic and queries
#3
Model Classes Created
• Patient, Employee, Provider, Organization, ContactParty,
PatientContact (visit), ServiceDeliveryLocation,
AdmitDiagnosis
• HealthIssue (Problem), Allergy, Intolerance, Document
• Order
– OrderLab, OrderLabMicro, OrderBloodProduct
– OrderMedAmb, OrderMedCont, OrderMedInt, OrderMedPCA,
OrderMedReg
– OrderNutrition, OrderRadiology, OrderNursing, OrderRepiratory,
OrderTherapies
• LabObs, MicroLabObs, Assert, Eval, Meas, Proc
• Qualifiers, Modifiers (Subject), Attributions, Panels
#4
Model Subtypes Created
• Number of models created - 4384
– Laboratory models – 2933
– Evaluations – 210
– Measurements – 353
– Assertions – 143
– Procedures – 87
– Qualifiers, Modifiers, and Components
• Statuses – 26
• Date/times – 27
• Others – 400+
– Panels – 79
#5
Access to the models
• Send me an email and I will send
you a zip file
–[email protected]
• Web browser
–www.clinicalelement.com
–Works best with Mozilla Firefox
browser
#6
What if there is no model?
Site #1
Dry Weight: 70 kg
Site #2
Weight: 70 kg
Dry
Wet
Ideal
#7
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?
#8
Model Centered Data Representation
SNOMED
LOINC
FDB
RxNorm
ICD-10
CPT
SNOMED
LOINC
FDB
RxNorm
ICD-10
CPT
Context Specific
Mapping Tables
Internal
Terminology
(ECIDS)
Models
Models and Concepts
ECIS
Thesaurus
Mayo
Thesaurus
IH
Thesaurus
LexGrid Terminology Server
#9
We assume that the
model is used in
association with a
terminology server.
# 10
Model and Terminology
Model
MedicationOrder ::= SET {
drug
Drug,
dose
Decimal,
route
DrugRoute,
frequency
DrugFrequency,
startTime
DateTime,
endTime
DateTime,
orderedBy
Clinician,
orderNumber OrderNumber}
Instance data
MedicationOrder {
drug
PenVK,
dose
250,
route
Oral,
frequency
Q6H,
startTime
09/01/95 10:01,
endTime
09/11/95 23:59,
orderedBy
Don Jones, M.D.,
orderNumber A234567 }
If the medicationOrder.drug is_a “antibiotic”
then notify the infection control officer.
Concept Semantic Network
Drugs
Antibiotics
Penicillins
Pen VK
Analgesics
Cephalosporins
Amoxicillin
Cardiovascular
Aminoglycosides
Nafcillin
# 12
Denormalized Semantic Network
Drugs
Drugs
Drugs
Antibiotics
Antibiotics
Antibiotics
Penicillins
Penicillins
Penicillins
has-child
has-child
has-child
has-child
has-child
has-child
has-child
has-child
has-child
Antibiotics
Analgesics
Cardiovascular
Penicillins
Cephalosporins
Aminoglycosides
Pen VK
Amoxicillin
Nafcillin
Drugs
Drugs
Drugs
Drugs
Drugs
has-member
has-member
has-member
has-member
has-member
Antibiotics
Penicillins
Pen VK
Amoxicillin
Nafcillin
# 13
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.
# 14
A Panel containing 2 Observations
# 15
The use of Qualifiers
# 16
The use of Modifiers
# 17
XML Model with Term Binding
The name of this model
Binding to a single
<cetype name="BloodPressurePanel" kind="panel">
“observable” concept
<key code="BloodPressurePanel_KEY_ECID" />
<item name="systolicBloodPressureMeas" type="SystolicBloodPressureMeas" card="0-1" />
<item name="diastolicBloodPressureMeas" type="DiastolicBloodPressureMeas" card="0-1" />
<item name="meanArterialPressureMeas" type="MeanArterialPressureMeas" card="0-1" />
<qual name="methodDevice" type="MethodDevice" card="0-1" />
<qual name="bodyLocationPrecoord" type="BodyLocationPrecoord" card="0-1" />
<qual name="bodyPosition" type="BodyPosition" card="0-1" />
<qual name="relativeTemporalContext" type="RelativeTemporalContext" card="0-M" />
<qual name="patientPrecondition" type="PatientPrecondition" card="0-M" />
<mod name="subject" type="Subject" card="0-1" />
<att name="observed" type="Observed" card="0-1" />
<att name="reportedReceived" type="ReportedReceived" card="0-1" />
<att name="verified" type="Verified" card="0-1" />
…
</cetype>
# 18
Binding to a “domain” (value set)
Path to the coded element
<constraint path="qual.methodDevice.data.cwe.domain"
value="BloodPressureMeasurementDevice_DOMAIN_ECID" />
The name of the terminology “domain” that
the element is “bound” to
<constraint path="qual.bodyLocationPrecoord.data.cwe.domain"
value="BloodPressureBodyLocationPrecoord_DOMAIN_ECID" />
# 19
Compiler
XML Template
Java Class
“In Memory” Form
HTML
CEML
Source
File
CE
Translator
UML?
openEHR Archetype?
HL7 RIM Static Models?
OWL?
# 20
Decomposition Mapping
Precoordinated Model (User Interface Model)
SystolicBPRightArmSittingObs
data
SystolicBPRightArmSitting
138 mmHg
Post coordinated Model (Storage Model)
SystolicBP
SystolicBPObs
data
138 mmHg
quals
BodyLocation
BodyLocation
data
Right Arm
PatientPosition
PatientPosition
data
Sitting
# 21
How much data in a single record?
• “Chest pain made worse by exercise”
– Two events, but very close association
– Normally would go into a single finding
• “Ate a meal at a restaurant and 30 minutes later
he felt nauseated, and then an hour later he began
vomiting blood.”
– Discrete events with known time and potential causal
relationships
– May need to be represented by multiple associated
findings
• Semantic links are used to represent relationships
between distinct event instances
# 22
Representation of Semantic Links
InstanceId 1
(123) Nausea
Relationship
followed-by
InstanceId 2
(987) Vomiting
• Semantic links can also have certainty and
attribution
– Certainty
– Attribution (who or what asserted the relationship,
when, and why?)
# 23
Area 6 Discussion and Planning
# 24
Terminology Services
Detailed CEMs)
Model
(including
Using NHIN for
transmitting data
And the Internet for
managing Content Internet
Terminology, Models, Logic, NLP Semantics, etc.
Normalized Data Instances
NHIN
Canonical
EMR+
Normalized
Data
Instances
Normalized
Data
Instances
ETL
EDW
Staging
NLP Widgets
Normalization
Widgets
ETL +
Rules
EMR 1a
Patient
Billing
Imaging
EMR 2a
Providr
Claims
Sched
Lab
Facility
Rx
….
Facility a
NLP Widgets
Analytic
Health
Repository
Decision Support
CER
HTA
QI
CDS
Normalization
Widgets
EMR 1b
Patient
Billing
Imaging
EMR 2b
Providr
Claims
Sched
Lab
Facility
Rx
….
Facility b
Discussion
• Evaluation projects
– Sharing data through NHIN Connect and/or NHIN Direct
• What, who, when, where?
– Comparison of data processed through SHARP to data in existing Mayo
and Intermountain data trust, EDW, AHR
• What, who, when, where?
– Others?
• Evaluation of NLP outputs and value? Focus on a specific domain: X-rays,
operative notes, progress notes, sleep studies?
• Questions
–
–
–
–
What is the target set of normalization widgets that we want to build?
Can we do the evaluations on de-identified data?
Do we need patient consent to do the evaluations?
Others?
# 26