Part I: Introduction to SHARPn Normalization Hongfang Liu, PhD, Mayo Clinic Tom Oniki, PhD,...

Part I: Introduction toSHARPn Normalization

Hongfang Liu, PhD, Mayo ClinicTom Oniki, PhD, Intermountain Healthcare

Data Normalization

Goals– To conduct the science for realizing semantic

interoperability and integration of diverse data sources

– To develop tools and resources enabling the generation of normalized EMR data for secondary uses

Data Normalization

Information Models

Target Value Sets

Raw EMR Data

Tooling

Normalized EMR Data

Normalization Targets

Normalization Process

Normalization Targets

Clinical Element Models– Intermountain Healthcare/GE Healthcare’s

detailed clinical modelsTerminology/value sets associated with

the models– using standards where possible

CEM Models

Different models for different use cases“CORE” models

“Core” Models

CORELab CEM modelattribute 1

attribute 2

attribute 3

attribute 4

CEM A

CEM C

CEM B

CEM D

“Core” Models

CORELab CEM modelattribute 1

attribute 2

attribute 3

attribute 4

Secondary Use Lab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

CEM A

CEM C

CEM B

CEM D

“Core” Models

CORELab CEM modelattribute 1

attribute 2

attribute 3

attribute 4

Secondary Use Lab CEM model

CEM A

CEMC

CEM B

CEMD

attribute 1

attribute 2

attribute 3

attribute 4

Clinical TrialLab CEM model

CEMA

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4CEM A

CEM C

CEM B

CEM D

“Core” Models

CORELab CEM modelattribute 1

attribute 2

attribute 3

attribute 4

Secondary Use Lab CEM model

CEM A

CEMC

CEM B

CEMD

attribute 1

attribute 2

attribute 3

attribute 4

Clinical TrialLab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4CEM A

CEM C

CEM B

CEM D

EMRLab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

CEM Models

Different models for different use cases“CORE” models

– CORENotedDrug -> SecondaryUseNotedDrug

– COREStandardLab -> SecondaryUseStandardLab (+ 6 data type-specific models)

– COREPatient -> SecondaryUsePatient

Generating XSDsSecondary Use Lab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

Generating XSDsSecondary Use Lab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

SHARP“reference class”

attribute 5

attribute 6

attribute 7

attribute 8

CEM E

CEM G

CEM F

CEM H

Generating XSDsSecondary Use Lab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

SHARP“reference class”

attribute 5

attribute 6

attribute 7

attribute 8

CEM E

CEM G

CEM F

CEM H

COMPILE

Secondary Use Lab XSDattribute 1. . . . . . . . .attribute 3. . . . . . . . .attribute 5. . . . . . . . .attribute 6. . . . . . . . .attribute 7. . . . . . . . .attribute 8. . . . . . . . .

Terminology/Value SetsTerminology value sets define the valid values used

in the modelsTerminology standards are used wherever possible

Terminology/Value SetsTerminology value sets define the valid values used

in the modelsTerminology standards are used wherever possible

Secondary UsePatient CEM model

CEM B

CEM A

CEM C

administrativeGender

attribute X

attribute Y

attribute Z

Gender CEM GenderValue Set:

HL7AdminGender

{M, F}

CEM Request Site and Browser

https://intermountainhealthcare.org/CEMrequests

Normalization Process

Prepare Mapping UIMA Pipeline to transform raw EMR data

to normalized EMR data based on mappings

Mappings

Two kinds of mappings needed:– Model Mappings– Terminology Mappings

Model MappingsHL7 CEM

Secondary Use Patient CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

MSHPID

12…

OBROBX

123456…

Secondary Use Lab CEM model

CEM A

CEM C

CEM B

CEM D

attribute 1

attribute 2

attribute 3

attribute 4

Model Mappings

Terminology Mappings

HL7 from Mayo CEM

Local Gender Codes1 = MALE2 = FEMALE

HL7 AdministrativeGenderM = MALEF = FEMALE

Terminology MappingsCEM Fields LocalCode TargetCode TargetCodeSystemGender M M HL7 GenderGender F F HL7 GenderRace 2 2106-3 CDC RaceRace W 2106-3 CDC RaceRouterMethodDevice ORAL PO HL7 Route DoseFreq BID &0800,173 229799001 SNOMED DoseFreq BID &0800,220 229799001 SNOMEDDoseFreq DAILY &0830 69620002 SNOMEDDoseFreq Q24HRS 396125000 SNOMEDDoseFreq ONE TIME ORDER 422114001 SNOMEDDoseUNIT Puff 415215001 SNOMEDDoseUNIT TABLET 428673006 SNOMEDDoseUNIT tsp 415703001 SNOMEDDoseUNIT CAPSULE (HA 415215001 SNOMEDDoseUNIT patch 419702001 SNOMEDDoseUNIT gr 258682000 SNOMEDDoseUNIT mL 258773002 SNOMED

Pipeline

Implement in UIMA (Unstructured Information Management Architecture)

Configurable – Data sources – HL7, CCD, CDA, and Table format – Model mappings (different EMR systems may have

different formats)– Terminology mappings– Inference mappings – infer ingredients from clinical

drugs