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Data Warehousing with Semantic Ontologies April 13, 2015, Session: 54 Richard E. Biehl, Ph.D. CSQE, CSSBB Data-Oriented Quality Solutions DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

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Page 1: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Data Warehousing with Semantic Ontologies

April 13, 2015, Session: 54 Richard E. Biehl, Ph.D. CSQE, CSSBB

Data-Oriented Quality Solutions DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

Page 2: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Conflict of Interest Richard E. Biehl, Ph.D. Ownership Interest: Richard is the sole proprietor of Data-Oriented Quality Solutions (DOQS), an IT/Quality consulting practice founded in 1988 and operating out of Orlando, Florida, USA. Consulting Fees: Richard earns approximately 15% of his income from consulting engagements that involve the heuristics included in this presentation. Other: The heuristics in this presentation can be immediately and directly implemented by attendees. Nothing has been held back that would necessitate engaging DOQS for implementation.

© HIMSS 2015 2

Page 3: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Learning Objectives 1. Demonstrate how the HIT human-machine interface relies on the semantic

abilities of human participants

2. Categorize the three semantic layers relevant to clinical data warehouse design

3. Employ an ontological framework for mapping and modeling system-practice-phenotype data

4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion problem as an example

5. Propose a reasoning-based warehouse design that can learn on behalf of human participants who are increasingly overwhelmed by the flow of big data

3

Page 4: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Mapping to STEPS™

http://www.himss.org/ValueSuite

We start here!

4

Page 5: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Previous HIMSS Presentations Fundamentals of Data Warehousing in Healthcare 2013 HIMSS Annual Conference, New Orleans Implementing a Healthcare Data Warehouse in One year (Or Less) 2012 HIMSS Annual Conference, Las Vegas Standardizing Data Dimensions of Healthcare Data Warehouses 2010 HIMSS Annual Conference, Atlanta Success by Design: Effective Data Quality Measurements in a Hospital Data Warehouse 2008 HIMSS Annual Conference, Orlando

Data Quality

Data Dimensions

Project Management

Warehouse Design

5

Page 6: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

“Big Data” is about… • New data base architectures and performance challenges, • New analytical paradigms and ways of seeing the world through

data, • New design patterns for bringing together and using vast amounts of

data, • New social and ethical challenges that need to be addressed within

all of these new opportunities, • And all of the everyday mundane issues of systems and software

engineering that we’ve always been challenged to address, writ large.

6

Page 7: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

The Central Challenge • “Big Data” increases the urgency of having strong control over our

information. • The human-machine interface relies on the semantic abilities of the

human participant. • We need to engineer controlled semantics into our systems… • We want systems that can reason and learn on behalf of the human

participant that is increasingly overwhelmed by the volume and flow of big data.

X

7

Page 8: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Semantic Layers in a Biomedical Data Warehouse

• System – What is in the dataset or message?

• Practice – What is the provider doing or thinking?

• Phenotype – What’s right or wrong with the patient?

8

Page 9: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

An Ontological Framework

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

Ontology of General Medical Science (OGMS)

Ontology for Biomedical

Investigation (OBI)

Hypotheses & Conclusions Observations

Biomedical Semantics Biomedical Syntax

Biomedical Epistemology

9

Page 10: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Continuant Occurrent

Entity

Basic Formal Ontology

10

Page 11: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Independent Continuant

Continuant

Spatial Region

Dependent Continuant

Specifically Dependent Continuant

Generically Dependent Continuant

Quality Realizable Entity

Disposition Function Role

Material Entity

Object Boundary Site

Object Aggregate Object Fiat

Object Part

Basic Formal Ontology

11

Page 12: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Basic Formal Ontology

Occurrent

Processual Entity

Spatiotemporal Region

Temporal Region

Process Aggregate Process Fiat

Process Part Processual

Context Processual Boundary

12

Page 13: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Basic Formal Ontology

13

HIMSS10

HIMSS13

Page 14: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

What is in the dataset or message?

14

Page 15: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Independent Continuant

Specifically Dependent Continuant

Generically Dependent Continuant

Material Information

Bearer

Information Content Entity

Information Carrier

Dependent Continuant

Continuant

Basic Formal Ontology (BFO)

Information Artifact Ontology (IAO)

15

Page 16: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

Ontology for Biomedical

Investigation (OBI)

What is in the dataset or message?

What is the provider doing or thinking?

16

Page 17: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Independent Continuant

Specifically Dependent Continuant

Age

Basic Formal Ontology (BFO)

Ontology for Biomedical

Investigations (OBI)

Dependent Continuant

Continuant

Organism

Diagnosis Generically Dependent Continuant

Patient Role

Biological Sex

Role

Quality

Alive

17

Page 18: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Process

Performing a diagnosis

Performing an assessment

Collection of specimen

Administration of material

Processual Context

Hospital Encounter

Office Visit Occurrent

Basic Formal Ontology (BFO)

Ontology for Biomedical Investigations (OBI)

Process Aggregate Laboratory

Test

Medication Course

Transplant Surgery

18

Page 19: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

Ontology of General Medical Science (OGMS)

Ontology for Biomedical

Investigation (OBI)

What is in the dataset or message?

What is the provider doing or thinking?

What’s right or wrong with the patient?

19

Page 20: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Object Aggregate

Organism Population

Continuant Extended Organism

Object Organism

Basic Formal Ontology (BFO)

Ontology for General Medical Science

(OGMS)

Occurrent

Process

Disease Course

Pathological Bodily

Process

Processual Context Lifespan

Process Aggregate

20

Page 21: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Independent Continuant

Extended Organism

Specifically Dependent Continuant

Disease

Occurrent

Processual Entity

Disease Course

Pathological Bodily Process

Sign or Symptom

Continuant

Extended Organism experiences Disease Course instance of a Disease resulting from a Disorder with disposition toward Pathological Bodily Process which produces Pathological Anatomical Structure recognized as Sign or Symptom

Disorder

Pathological Anatomical Structure

Process Aggregate

Basic Formal Ontology (BFO)

Ontology for General Medical Science

(OGMS)

21

Page 22: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Independent Continuant

Specifically Dependent Continuant

Disorder

Disease

Basic Formal Ontology (BFO)

Ontology for General Medical Science

(OGMS)

Dependent Continuant

Continuant Extended Organism

Occurrent Processual Entity

Disease Course

Diagnostic Process

Pathological Bodily

Process

Diagnosis Generically Dependent Continuant

Information Content Entity

Pathological Anatomical Structure

Sign or Symptom

expe

rienc

es

Age

Ontology for Biomedical

Investigations (OBI)

Organism

Performing a diagnosis

Diagnosis

Patient Role

Biological Sex

Alive

Role

Quality

Material Information

Bearer

Information Carrier

Information Artifact Ontology (IAO)

22

Page 23: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

Ontology of General Medical Science (OGMS)

Ontology for Biomedical

Investigation (OBI)

Hypotheses &

Conclusions Observations

Biomedical Epistemology

Biomedical Semantics Biomedical

Syntax

23

Page 24: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

Ontology of General Medical Science (OGMS)

Ontology for Biomedical

Investigation (OBI)

Biomedical Semantics Biomedical

Syntax Raw message

Data from a clinical process about a patient

A clinical view of the patient

Inbound messages (e.g., CCD) are mapped as field-level information artifacts back to the clinical processes that evaluated the patient.

The contents of those messages – the values of those information artifacts – are then mapped into a clinical picture of the patient.

The three mid-level ontologies are mapped to each other through the common BFO framework. The values in the messages end up being at a different ontological level than the semantic meaning of those values, allowing for translation, harmonization, and quality control to intervene as systems data in messages is translated into clinical data in systems.

top level

mid-level

24

Page 25: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology

(IAO)

Basic Formal Ontology (BFO)

Ontology of General Medical Science (OGMS)

Ontology for Biomedical

Investigation (OBI)

Biomedical Semantics Biomedical

Syntax

top level

mid-level

domain -level

SNOMED, ICD, CPT, RxNORM, LOINC, MeSH, etc.

25

Page 26: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Ontology-Based Design

•Source ETL analysis & mapping •Warehouse logical database design •Post-load spider processing •Conducting queries & analysis

26

Page 27: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

<ClinicalDocument xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="urn:hl7-org:v3“xmlns:cda="urn:hl7-org:v3" xmlns:sdtc="urn:hl7-org:sdtc“><realmCode code="US"/><typeId root="2.16.840.1.113883.1.3" extension="POCD_HD000040"/><!-- US General Header Template --><templateId root="2.16.840.1.113883.10.20.22.1.1“ /><templateId root="2.16.840.1.113883.10.20.22.1.2"/><id extension="TT988" root="2.16.840.1.113883.19.5.99999.1"/><code codeSystem="2.16.840.1.113883.6.1" codeSystemName="LOINC" code="34133-9“ displayName="Summarization of Episode Note"/><title>Community Health and Hospitals: Health Summary</title><effectiveTime value="201209150000-0400"/><confidentialityCode code="N" codeSystem="2.16.840.1.113883.5.25"/><languageCode code="en-US"/><setId extension="sTT988“ root="2.16.840.1.113883.19.5.99999.19"/><versionNumber value="1"/> <component><structuredBody><component><section><templateId root="2.16.840.1.113883.10.20.22.2.5"/><templateId root="2.16.840.1.113883.10.20.22.2.5.1"/><code code="11450-4" codeSystem="2.16.840.1.113883.6.1" codeSystemName="LOINC“ displayName="PROBLEM LIST"/><title>PROBLEMS</title><entryRelationship typeCode="SUBJ" inversionInd="true“><observation classCode="OBS" moodCode="EVN“><templateId root="2.16.840.1.113883.10.20.22.4.31"/><code code="445518008“ codeSystem="2.16.840.1.113883.6.96“ displayName="Age At Onset"/> <statusCode code="completed"/> <value xsi:type="PQ" value="57" unit="a"/> </observation></entryRelationship></entry></section></component><structuredBody></component></clinicalDocument>

Age at Onset 57 years

27

Page 28: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

section

<type> " CD" <codeSystem> “2.16.840.1.113883.6.96”

<displayName> “Pneumonia”

value

<code> " 233604007"

statusCode <code> “completed”

effectiveTime

Low <value> " 20080103" High <value> "20080103"

<code> "409586006"

<codeSystem> “2.16.840.1.113883.6.96”

<displayName> “Complaint”

code

id <root> "ab1791b0-5c71-11db-b0de-0800200c9a66"

templateId <root> "2.16.840.1.113883.10.20.22.4.4"

observation

<classCode> “OBS"

<moodCode> “EVN"

entryRelationship <typeCode> “SUBJ"

<title> “PROBLEMS"

<codeSystemName> " CD"

<codeSystem> “2.16.840.1.113883.6.1”

<displayName> “PROBLEM LIST”

code

<code> "11450-4"

templateId <root> "2.16.840.1.113883.10.20.22.2.5"

templateId <root> "2.16.840.1.113883.10.20.22.2.5.1“

entry <typeCode> “DRIV"

act

id <root> "ec8a6ff8-ed4b-4f7e-82c3-e98e58b45de7"

templateId <root> "2.16.840.1.113883.10.20.22.4.3” <classCode> “ACT"

<moodCode> “EVN"

<code> “CONC"

<codeSystem> “2.16.840.1.113883.5.6”

<displayName> “Concern”

code

statusCode <code> “completed”

effectiveTime

Low <value> " 20080103" High <value> "20080103"

Page 29: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

observation

<code> "33999-4"

<codeSystem> “2.16.840.1.113883.6.1”

<displayName> “Status”

code

<classCode> “OBS"

<moodCode> “EVN"

templateID <root> “2.16.840.1.113883.10.20.22.4.6”

statusCode <code> “completed”

entryRelationship <typeCode> “REFR"

id <root> “ab1791b0-5c71-11db-b0de-0800200c9a66”

effectiveTime

Low <value> "20080103" High <value> “20090227130000+0500"

<type> " CD" <codeSystem> “2.16.840.1.113883.6.96”

<displayName> “Resolved”

value

<code> " 413322009”

observation

<value> “57” <unit> “a” <type> “PQ” Value

<code> "445518008"

<codeSystem> “2.16.840.1.113883.6.96”

<displayName> “Age At Onset”

code

<classCode> “OBS"

<moodCode> “EVN" <root> “2.16.840.1.113883.10.20.22.4.31”

templateID

statusCode <code> “completed”

entryRelationship <typeCode> “SUBJ" <inversionInd> “true"

act

Page 30: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

observation

<code> “11323-3" <codeSystem> “2.16.840.1.113883.6.1”

<displayName> “Health Status”

code

<classCode> “OBS"

<moodCode> “EVN"

templateID <root> “2.16.840.1.113883.10.20.22.4.5”

statusCode <code> “completed”

entryRelationship <typeCode> “REFR"

id <root> “ab1791b0-5c71-11db-b0de-0800200c9a66”

effectiveTime

Low <value> " 20080103" High <value> " 20090227130000+0500"

<type> " CD" <codeSystem> “2.16.840.1.113883.6.96”

<displayName> “Alive and well”

value

<code> "81323004”

<codeSystemName> "LOINC"

<codeSystemName> “SNOMED CT"

30

Page 31: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Material Information

Bearer CCD XML 31

Page 32: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Content Entity

Problem Observation

<code>

Age Observation <value>

Problem Observation <start>

Problem Observation <stop>

Generically Dependent Continuant

Material Information

Bearer CCD XML 32

Page 33: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Complaint (409586006)

Start Time (398201009)

Stop Time (397898000)

Information Content Entity

Problem Observation

<code>

Age Observation <value>

Problem Observation <start>

Problem Observation <stop>

is a

bout

is a

bout

is a

bout

Age At Onset (445518008)

is a

bout

Generically Dependent Continuant

Material Information

Bearer CCD XML

SNOMED (Functional

Classes)

33

Page 34: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Complaint (409586006)

Start Time (398201009)

Stop Time (397898000)

Information Content Entity

Problem Observation

<code>

Age Observation <value>

Problem Observation <start>

Problem Observation <stop>

is a

bout

is a

bout

is a

bout

Information Carrier

“2006-03-22” “2006-04-22" "409586006“ “Complaint”

Age At Onset (445518008)

is a

bout

“29”

Specifically Dependent Continuant

Generically Dependent Continuant

Material Information

Bearer CCD XML

SNOMED (Functional

Classes)

34

Page 35: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Complaint (409586006)

Start Time (398201009)

Stop Time (397898000)

age_at_onset

date_low

date_high

type

ProblemSchema:

coded diagnosis

Information Content Entity

Problem Observation

<code>

Age Observation <value>

Problem Observation <start>

Problem Observation <stop>

is a

bout

is a

bout

is a

bout

Information Carrier

“2006-03-22” “2006-04-22" "409586006“ “Complaint”

Age At Onset (445518008)

is a

bout

“29”

Specifically Dependent Continuant

Generically Dependent Continuant

Material Information

Bearer CCD XML

SNOMED (Functional

Classes)

35

Page 36: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

type Complaint

date_high 2006-04-22

date_low 2006-03-22

Problem: 480.0

Pneumonia due to adenovirus

Complaint (409586006)

Start Time (398201009)

Stop Time (397898000)

age_at_onset

date_low

date_high

type

ProblemSchema:

coded diagnosis

Information Content Entity

Problem Observation

<code>

Age Observation <value>

Problem Observation <start>

Problem Observation <stop>

is a

bout

is a

bout

is a

bout

Information Carrier

“2006-03-22” “2006-04-22" "409586006“ “Complaint”

Age At Onset (445518008)

is a

bout

“29”

Specifically Dependent Continuant

Generically Dependent Continuant

Material Information

Bearer CCD XML

age_at_onset 29

SNOMED (Functional

Classes)

36

Page 37: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Information Artifact Ontology (IAO)

//section/templateId /@root="2.16.840.1.113883.10.20.22.2.5.1”

ClinicalDocument/templateId/@root="2.16.840.1.113883.10.20.22.1.2"

//entry/@typeCode="DRIV“ //act/@classCode="ACT“

//@moodCode="EVN“ //templateId/@root="2.16.840.1.113883.10.20.22.4.3

" //code //effectiveTime

//low //high

@code

@value //entryRelationship/@typeCode="SUBJ“ //observation/@classCode="OBS"

//@moodCode="EVN“ //templateId/@root="2.16.840.1.113883.10.20.22.4.4"

@value

@codeSystem

//code @code @codeSystem

//value @code @ codeSystem

@type

Information Content Entity Information

Carrier

"CONC"

"20070103" "20070103"

"2.16.840.1.113883.5.6

"409586006" "2.16.840.1.113883.6.96"

“233604007” " 2.16.840.1.113883.6.96

“CD”

Concern

Complaint

Problem List

Pneumonia

//entryRelationship/@typeCode=“SUBJ“ @inversionInd“true"

//observation/@classCode="OBS" //@moodCode="EVN“

//templateId/@root="2.16.840.1.113883.10.20.22.4.31" //code @code @codeSystem

"445518008 " "2.16.840.1.113883.6.96"

//value @unit @type

@value “a” “PQ

“57” Age at Onset 57 years

Summarization of Episode

Note

37

Page 38: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Concern

Complaint

Resolved

Age at Onset 57 years

Active

Problem List

Asthma

Pneumonia

Status

Alive & Well

Health Status

38

Page 39: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Concern

Status

Problem List

Resolved

Age at Onset 57 years

Summarization of Episode Note

Class with related value

Class with embedded attribute value

Classes can include classes below them in the ICE mappings Metadata Layer

Defines the meaning of the entryRelationship at the next highest layer

Contains entryRelationships with variable meaning

39

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Concern

Resolved

Active

Status

Concern

Resolved

Active

Status

Status

enRel

enRel

40

Page 41: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Concern

Resolved

Age at Onset 57 years Active

Problem List

Asthma

Pneumonia Alive & Well

Status Complaint Age Health Status

Status Complaint Age Health Status

Under this scenario, there would no longer be classes or tables for the various LOINC codes that define the semantics of each <entryRelationship>. They instead become the schema definition for the <entry> In which they are found.

A schema entry is an IAO Information Carrier that has been mapped to a BFO Generically Dependent Continuant (GDC).

41

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Information Artifact Ontology (IAO)

ClinicalDocument/templateId/@root="2.16.840.1.113883.10.20.22.1.2"

Information Content Entity

Information Carrier

//recordTarget//patientRole

Summarization of Episode Note

@extension

@root

“MRN1234567”

"2.16.840.1.113883.19.5.2"

//Id

Medical Record Number (MRN)

(2.16.840.1.113883.19.5.2)

MRN (398225001)

Patient Role

Patient-related Identification code

(422549004)

//section/templateId /@root="2.16.840.1.113883.10.20.22.2.5.1”

Problem List

Individual organism identifier

MRN

CRID

42

Protected

Page 43: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

“Isabella Jones”

Patient Name (371484003)

Information Content Entity

Information Carrier

Specifically Dependent Continuant

Generically Dependent Continuant recordTarget

MRN <id extension>

MRN (398225001)

SSN <id extension>

"998991" "111-00-2330" is

abo

ut

is a

bout

Tax ID (12345654)

patientRole <name>

Organism

Patient Role

Continuant

Role

Independent Continuant

Taxpayer Role

inheres in

Protected

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Page 44: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

“Isabella Jones”

Patient Name (371484003)

Information Content Entity

Information Carrier

Specifically Dependent Continuant

Generically Dependent Continuant recordTarget

MRN <id extension>

MRN (398225001)

SSN <id extension>

"998991" "111-00-2330" is

abo

ut

is a

bout

Tax ID (12345654)

patientRole <name>

Organism

Patient Role

Continuant

Role

Independent Continuant

Taxpayer Role

inheres in

PHI PHI PHI

Protected

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Page 45: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Organism

Patient Role

Continuant

Role

Independent Continuant

Biospecimen

derived from Object Aggregate

Object

Extended Organism

inheres in

includes

MRN (398225001)

Accession Number

identifies

identifies

extends

Occurrent

Assigning a centrally

registered Id

Organization

Specimen Role

inheres in

participates in

Individual organism identifier

Specimen identifier

Information Content Entity

Generically Dependent Continuant

MRN

is about

is about

CRID instantiates

PHI

PHI Protected

Secure any continuant that identifies something in which a protected role inheres, or anything extending or derived from such a continuant.

Page 46: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

mappingRelation

closeMatch

broadMatch narrowMatch

relatedMatch

exactMatch

inverseOf

symmetric symmetric

symmetric transitive

disjoint

Simple Knowledge Organization System (SKOS)

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Page 47: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

LM

L

LMN

XY closeMatch

1

isA

isA

broadMatch

narrowMatch

Assertion: XY closeMatch1 LM Known: LM isA L LMN isA LM Implied: L narrowMatch XY LMN broadMatch XY By inverse rule: XY broadMatch L XY narrowMatch LMN

broadMatch

narrowMatch

1 Any ontology edge that has been curated to closeMatch would have the same

implications.

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Page 48: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

LOINC 882-1 “ABO + Rh group, Blood”

CPT 86901 “Blood typing;

Rh (D)”

CPT 86900 “Blood typing; ABO”

LOINC 884-7 “ABO + Rh group, Blood Capillary”

LOINC 34474-7 “ABO + Rh group, Cord Blood”

CPT to LOINC

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Page 49: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

84.71 ICD-9CM Application of external fixator device, monoplanar system

79.21 ICD-9CM Open reduction of fracture without internal fixation, humerus

78.12 ICD-9CM Application of

external fixator device,

humerus

0PSD0BZ ICD10PCS Reposition Left Humeral Head with Monoplanar

External Fixation Device, Open Approach

0PSC0BZ ICD10PCS Reposition Right Humeral

Head with Monoplanar External Fixation Device,

Open Approach

ICD-9 to ICD-10

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Page 50: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Rosuvastatin

Crestor

Crestor . tradenameOf . Rosuvastatin {broadMatch} Rosuvastatin . hasTradename . Crestor {narrowMatch}

RxNorm

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Page 51: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Crestor 320864

RxNORM

Crestor Pill 1173053

has_ingredient

Clinical D

rug Rosuvastatin

301542

Organic C

hemical

Pharm

acologic Substance

Biologically A

ctive Substance

Rosuvastatin calcium 323828

has_form

has_tradename

form_of

precise _ingredient_of

Rosuvastatin calcium 5 MG

859423

has_precise _ingredient

has_ingredient

Rosuvastatin calcium 5 MG Oral

Tablet 859424

consists_of consititutes

Rosuvastatin calcium 5 MG Oral

Tablet [Crestor] 859426

Rosuvastatin calcium 5 MG

[Crestor] 859423

consists_of

consists_of

has_ingredient

isA

Oral Tablet

has_does_form

has_tradename

has_precise _ingredient

isA

Crestor Oral Product

isA

rosuvastatin Oral Tablet [Crestor]

isA

The mess!

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Page 52: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Refractory Migraine

(423894005)

Lower Half Migraine

(26150009)

Migraine (37796009)

Vascular Headache

(128187005)

Headache Disorder

(340461009)

Headache (25064002)

has_definitional_manifestation

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Page 53: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Refractory Migraine

(423894005)

Lower Half Migraine

(26150009)

Migraine (37796009)

Vascular Headache

(128187005)

Headache Disorder

(340461009)

Headache (25064002)

has_definitional_manifestation

Headache (748.0)

Migraine (346)

Migraine, unspecified

(346.9)

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Page 54: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Refractory Migraine

(423894005)

Lower Half Migraine

(26150009)

Migraine (37796009)

Vascular Headache

(128187005)

Headache Disorder

(340461009)

Headache (25064002)

has_definitional_manifestation

Migraine (G43)

Migraine, unspecified

(G43.9)

Migraine, unspecified, not

intractable (G43.909)

Headache (R51)

Headache (748.0)

Other migraine (G43.8)

Lower half migraine

Cluster headache syndrome, unspecified

(G44.00)

synonymOf

excludes

Migraine (346)

Migraine, unspecified

(346.9)

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Page 55: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Refractory Migraine

(423894005)

Lower Half Migraine

(26150009)

Migraine (37796009)

Vascular Headache

(128187005)

Headache Disorder

(340461009)

Headache (25064002)

has_definitional_manifestation

Migraine (G43)

Migraine, unspecified

(G43.9)

Migraine, unspecified, not

intractable (G43.909)

Headache (R51)

Headache (748.0)

Other migraine (G43.8)

Lower half migraine

Cluster headache syndrome, unspecified

(G44.00)

synonymOf

excludes

Migraine (346)

Migraine, unspecified

(346.9)

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Page 56: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Refractory Migraine

(423894005)

Lower Half Migraine

(26150009)

Migraine (37796009)

Vascular Headache

(128187005)

Headache Disorder

(340461009)

Headache (25064002)

has_definitional_manifestation

Migraine (G43)

Migraine, unspecified

(G43.9)

Migraine, unspecified, not

intractable (G43.909)

Headache (R51)

Headache (748.0)

Other migraine (G43.8)

Lower half migraine

Cluster headache syndrome, unspecified

(G44.00)

synonymOf

excludes

Migraine (346)

Migraine, unspecified

(346.9)

ICD-10 excludes Lower Half Migraine from Migraine, but SNOMED still includes it.

Have a migraine?

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Page 57: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Data Warehousing with Semantic Ontologies

• Inclusion and mapping of BFO, IAO, OBI, and OGMS ontologies

• Inclusion and mapping of additional domain ontologies of interest

• Continuous analysis of SKOS consistency and compliance

• Tailoring of query layer to incorporate governance-approved semantic mappings and exceptions

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Page 58: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Mapping to STEPS™

http://www.himss.org/ValueSuite

We ended up here!

We ended up here!

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Page 59: Data Warehousing with Semantic Ontologies · practice-phenotype data 4. Illustrate how semantic ontologies can resolve common problems in warehousing, using the ICD-9 to ICD-10 conversion

Questions You are welcome to contact me

with questions at any time: • Richard E. Biehl, Ph.D.

Data-Oriented Quality Solutions

[email protected]

• LinkedIN: rbiehl

• Twitter: rbiehl

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