Usage of EHR billing code to identify Clinical Expertise

1. From billing codes to expertise:
mining, representing and sharing
clinical research profiles in the
Linked Data Cloud
Carlo Torniai
Shahim Essaid, Chris Barnes, Stephen Williams, Janos Hajagos
Nicole Vasilevsky, Melissa Haendel

2. CTSAConnect Project
Needs:
– Identify potential collaborators, relevant resources, and
expertise across scientific disciplines
– Assemble translational teams of scientists to address specific
research questions
Approach:
Create a semantic representation of clinician and basic science
researcher expertise to enable
– more effective linking of information about clinicians and basic
science researchers
– publication of expertise data as Linked Data (LD) for use in
other applications
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

3. Integrating VIVO and eagle-i
VIVO
eagle-i
 VIVO is an ontology-driven application . . . for collecting and
displaying information about people
 eagle-i is an ontology-driven application . . . for collecting and
searching research resources
 Both publish Linked Data. Neither addresses clinical expertise
www.ctsaconnect.org
8/23/2012 CTSAconnect 3
Reveal Connections. Realize Potential.

4. Extending eagle-i and VIVO to represent
clinical expertise
Semantic
VIVO
Clinical
eagle-i activities
Researcher Characterization Clinician Characterization
• Organizational affiliations • Research resources • Training and credentials
• Grant and project participation – Reagents • Clinical research topic
• Activities – Biospecimens • Specialization inferred from EHR
– Teaching courses – Animal models – Procedures
– Mentoring students – Instruments – Diagnosis
– (Co)-authoring publications – Techinque – Prescriptions
CTSAconnect will produce a single Integrated Semantic Framework that includes
clinical expertise
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

5. ISF Clinical module
ARG: Agents, Resources, Grants ontology
CM: Clinical module
IAO: Information Artifact Ontology
OBI: Ontology for Biomedical
Investigations
OGMS: Ontology for General Medical
Science
FOAF: Friend of a Friend vocabulary
BFO: Basic Formal Ontology
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

6. ISF Clinical module: encounter
ARG: Agents, Resources, Grants ontology
CM: Clinical module
OGMS: Ontology for General Medical
Science
FOAF: Friend of a Friend vocabulary
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

7. ISF Clinical module: encounter output
CM: Clinical module
OBI: Ontology for Biomedical
Investigations
OGMS: Ontology for General
Medical Science
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

8. Collecting and publishing clinical expertise
as represented by encounter
Step 1 Step 2 Step 3 Step 4
Aggregate Map Data to Compute Publish Linked
Clinical Data ISF Expertise Data
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

9. Aggregate clinical data
Step 1 Step 2 Step 3 Step 4
Aggregate Map Data to Compute Publish Linked
Clinical Data ISF Expertise Data
Provider ICD Code Unique Patient
ID Code Value Count Count Code Label
Unilateral or unspecified femoral hernia
1234567 552.00 1 1 with obstruction (ICD9CM 552.00)
Bilateral femoral hernia without mention
1234567 553.02 8 6 of obstruction or gangrene (ICD9CM
553.02)
Regional enteritis of large intestine
1234567 555.1 4 1 (ICD9CM 555.1)
Corrected transposition of great vessels
1234568 745.12 10 5 (ICD9CM 745.12)
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

10. Map data to ISF
Step 1 Step 2 Step 3 Step 4
Aggregate Map Data to Compute Publish Linked
Clinical Data ISF Expertise Data
Java scripts RDF
Unique
Provider ID ICD Code Value Code Count
Patient
Count Code Label OWL API triples
Unilateral or unspecified
femoral hernia with
1234567 552.00 1 1
obstruction (ICD9CM
552.00)
Bilateral femoral hernia
without mention of
1234567 553.02 8 6
obstruction or gangrene
(ICD9CM 553.02)
Regional enteritis of large
1234567 555.1 4 1
intestine (ICD9CM 555.1)
Corrected transposition of
1234568 745.12 10 5 great vessels (ICD9CM
745.12)
Aggregated
Clinical Data
ISF
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

11. Compute Expertise
Step 1 Step 2 Step 3 Step 4
Aggregate Map Data to Compute Publish Linked
Clinical Data ISF Expertise Data
• Unified Medical Language System (UMLS) aggregates Medical
Subjects Heading (MeSH) and other terminologies by linking them
to UMLS concept unique identifiers (CUI)
• UMLS CUIs will be used to map ICD9 and CPT codes to MeSH
• Expertise indexed by MeSH will enable meaningful connections
between clinicians, basic researchers, and biomedical knowledge
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

12. Compute Expertise: Mapping ICD9 to MeSH
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

13. Compute Expertise: weighting
Step 1 Step 2 Step 3 Step 4
Aggregate Map Data to Compute Publish Linked
Clinical Data ISF Expertise Data
• Provider X has 500 patients
• S/he has used Syndactyly
(ICD9: 755.12) for 30 unique
patients 75 times
Percentage of patients with
code: 30/500*100 = 6%
Code frequency: 75/30 = 2.5
Code weight: 6 * 2.5 = 15
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

14. Publish Linked Data
Step 1 Step 2 Step 3 Step 4
Aggregate Map Data to Compute Publish Linked
Clinical Data ISF Expertise Data
Other APIs
Endpoints
SPARQL
…
Linked Data Several means
Triple Stores to access and
cloud
query data
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

15. Sample encounter data published as LOD
Health care encounter
Annotations and Instance URI
Properties
Inferred Types
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

16. Querying the data
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

17. Beyond expertise
• Encounter data represented using ISF and published as Linked
Data, in addition to enhance linkage between clinical and basic
expertise, will enable integration with multiple datasets which
could be used in a variety of ways to discover useful clinical
associations and patterns
www.ctsaconnect.org CTSAconnect
Reveal Connections. Realize Potential.

18. Information
 CTSAconnect project  Carlo Torniai
torniai@ohsu.edu
ctsaconnect.org
 CTSAconnect ontology source  Shahim Essaid
http://code.google.com/p/connect-isf/ essaids@ohsu.edu
 The clinical module can be directed  Chris Barnes
accessed at http://bit.ly/clinical-isf cpb@ufl.edu
 Linked Data generation code
http://bit.ly/isf-lod-code  Janos Hajagos
janos.hajagos@stonybrook.edu
 eagle-i federated search
eagle-i.net  Stephen V Williams
 VIVO integrated search swilliams@ctrip.ufl.edu
vivosearch.org  Nicole Vasilevski
 CTSA ShareCenter vasilevs@ohsu.edu
ctsasharecenter.org
 Melissa Haendel
haendel@ohsu.edu
CTSA 10-001: 100928SB23
www.ctsaconnect.org CTSAconnect
PROJECT #: 00921-0001 Reveal Connections. Realize Potential.