1. Query Health Concept-to-Codes (C2C) SWG Meeting #6
January 17, 2012

2. Today’s Agenda Topic Time Allotted
Quick Review of Updated Timeline and Future Meeting Times
2:30 – 2:35
Presentation by Subject Matter Experts
Shaun Shakib – 3M
2:35 - 3:00
Zeshan Rajput- RELMA (Regenstrief LOINC Mapping Assistant)
3:00 – 3:30
David Baorto – NY Presbyterian Terminology Services
3:30 - 4:00

3. Meeting times extended from 2:30-4:00pm
Proposed Timeline
Meeting times extended from 2:30-4:00pm
TODAY
Meeting 1 –
Dec 6
Meeting 2 –
Dec 13
Meeting 3 –
Dec 20
Meeting 4 –
Jan 03
Meeting 5 –
Jan 10
Meeting 6 –
Jan 17
Meeting 7 –
Jan 24
Meeting 8 –
Jan 31
Meeting 9 –
Feb 7
Meeting 10 –
Feb 14
Tasks
Introductions
Scope
Proposed Approach
Identify SME and presentation timeline for next few meetings
Presentation
hQuery
i2b2
Presentation
I2b2 (Cont.)
Intermountain Health
DOQS (Data Warehousing / Mapping)
Presentation
DOQS (Data Warehousing / Mapping) Cont.
PopMedNet
NLM
Presentation
Ibeza
CDISC SHARE
Presentation
RELMA (LOINC) 3M
NY Presbyterian Hospital Vocab Team
Tasks
AHIMA
LexEVS and CTS2
NQF
Tasks
Preliminary review of presentation summaries and Draft Deliverable
Tasks
Review of presented concept mapping frameworks to select a proposed approach
Begin Consensus Voting process
Tasks
Consensus Voting Finalized
Coordinate offline activities to summarize approaches and develop draft deliverable from presentations

4. Announcement – Follow up Questions from 1/3 DOQS presentation by Rick Biehl
Document posted under Presentation / Reference Materials

5. Concept Mapping Approach 3M Healthcare Data Dictionary
Shaun Shakib, MPH
3M Health Information Systems

6. History: HELP System
Developed by Dr. Homer Warner, first chair of the University of Utah Department of Medical Informatics, with Dr. Allan Pryor and Dr. Reed Gardner, and others
Origin goes back to 1954 (in the LDS Hospital’s Cardiovascular Laboratory), but the system is first described in 1972
The vision was not only to acquire and present data, but also to interpret data so as to “help” the physician in patient care
Integrated database
Data dictionary
Decision support
Functional applications
Interfaces and data capture
Reports
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7. Example: HDD in support of a CDR
Interface Engine
Presentation Layer
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8. What is the 3M Healthcare Data Dictionary?
ICD-10-CM
ICD-10-PCS
ICD-9-CM
MS-DRG
CPT
HCPCS
APC
SNOMED CT
LOINC
FDB
RxNorm
HDD
Lab, Rx, Radiology, Dental, Demographics, etc.
2.4 million concepts 26 million representations
15.9 million relationships
A system that organizes, defines, and encodes concepts
A practical, enterprise solution for implementing and maintaining reference terminology and knowledge
A critical data governance tool
A metadata repository that defines the logical structure of instance data to make it computable
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9. Data Standardization Using the 3M HDD
Standardization, Simplicity and Flexibility for End Users
Encode data – Crosswalk between any code/term (standard or legacy)
Exchange data – Semantic interoperability
Query, analyze and reason – Sharable logic
Customize or extend content – Support local needs
Manage Complexity in the 3M HDD Content
Contexts
Representations
Concepts
Relationships
SNOMED CT ID
Ampicillin
*NCID
Antibiotic
*NCID
SNOMED CT
Full Name
Ampicillin
(Substance)
Has Child
RxNORM RxCUI
Cloxacillin
*NCID
733
Penicillin
*NCID
RxNORM Name
Ampicillin
Treatment For
Hospital A
Interface Code
A AM
Mapping …
Staphylococcus
*NCID
2625
Hospital A Name
CLOX … …
*NCID = 3M HDD’s Numerical Concept Identifier
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10. Concept Mapping Defined
Herpesvirus 3, Human
C (UMLS ID)
3M HDD
Herpes-zoster Virus
Display 2
Chickenpox Virus
Display 1
Varicella-zoster Virus (3M HDD ID)
VZV 243 = Ft Hood Lab Interface Context
Healthcare Data Dictionary
Concept Mapping
SMEs, Expert Tools
Process of building links among concepts to integrate various vocabularies
Context: the purpose of the map
Are these drugs being mapped for inventory, allergens, or as clinical drugs?
Matching technologies/tools/process to avoid duplicate concepts and increase consistency and efficiency
String comparison mapping and semantic mapping
Requires subject matter expertise
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11. Semantic Mapping for Drugs
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12. HDD Mapping Result – Rosetta Stone
Protime Concept 3M HDD ID 26277
LOINC® Code
Fort Sill Interface Code
DoD Long Name Protime
DoD Short Name PT
Camp LeJeune Interface Code
Manages ongoing updates from standard terminologies as well as local additions and changes from health care facilities
Translates/transforms an enterprise’s data to standards such as LOINC®, so it can be interpreted by all other systems using standards
Moves Syntactic Interoperability to the level of Semantic Interoperability and enables Meaningful Use
HDD ID
26277
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13. 3M HDD and HL7 Common Terminology Services
4/11/2017
3M HDD and HL7 Common Terminology Services
HL7 CTS provides functional specifications to query terminology servers in a standard way
HL7 CTS frees the application developers from the constraints of the terminology service.
If the EMR applications are CTS compliant, they can work with any CTS compliant terminology service
HL7 CTS v1 query only
HL7 CTS2, adds distributed authoring and versioning capabilities
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MDINF 6220: Medical Standards and Terminology

14. HL7 CTS Application Programming Interface
Terminology Services
4/11/2017
HL7 CTS Application Programming Interface
. . .
Applications
Interface
Services
Data
CTS
Modified From HL7 Service-Oriented Architecture SIG, OMG Healthcare Domain Task Force, January 2008
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MDINF 6220: Medical Standards and Terminology

15. HL7 CTS Application Programming Interface
Terminology Services
4/11/2017
HL7 CTS Application Programming Interface
Vocabulary
Browser
Applications
Interface
Service
Data
Find all the children of “Units of Measure”
CTS
3M HDD
Terminology
Server
select * from rsform, rsform_context, concept_relation where concept_relation.CONCEPT_RELATION_NCID=1110 and concept_relation.RELATIONSHIP_NCID=363 and
concept_relation.CONCEPT_NCID=rsform.NCID and rsform.RSFORM_ID=rsform_context.RSFORM_ID and rsform_context.CONTEXT_NCID=2000 and rsform_context.PREFERRED_SCORE=0;
3M HDD
Modified From HL7 Service-Oriented Architecture SIG, OMG Healthcare Domain Task Force, January 2008
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MDINF 6220: Medical Standards and Terminology

16. Exchanging Standard Codes Two Implementation Strategies - Comparison
Encode with the identifiers of standard terminologies
Encode with internal identifiers, “translate” to standard codes for external exchange
2823-3
**HDD
Internal code:
LOINC®:
*CDR
Patient:
John Doe
Lab Result: 1234
*CDR
Patient:
John Doe
Lab Result:
2823-3
*CDR=Clinical Data Repository **HDD=Healthcare Data Dictionary
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17. The Mapping Challenge VS. Mappings = 9 Adding 1 source adds 1 map
SNOMED
Target 1
LOINC
Source
Source
SNOMED
LOINC
Target 2
Source
HDD
Source
Source
Source
Source
Source
VS.
Source
Source
Source
Source
Source
Source
Mappings = 9
Adding 1 source adds 1 map
Mappings = 15
Adding 1 source adds 2 maps
Adding 1 target adds 9 maps
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18. Shift the Meaning of a Standard Code Encoding Data Using Standard Codes
NDC
Before July 2002
After July 2002
CDR
CDR
Patient:
John Doe
Medication:
Liposyn (Fat Emulsions), 10%, IV Solution, IV, Abbott Hospital, 200ml Bag
Paclitaxel (Paclitaxel, Semi-Synthetic), 6mg/ml, Vial, Injection, Abbott Hospital, 5ml Vial
Patient:
John Doe
Medication:
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19. Shift the Meaning of a Standard Code Encode With Internal Identifiers, Map to Standards (HDD Approach)
HDD ID
Before July 2002
After July 2002
CDR
CDR
Patient:
John Doe
Medication:
Active NDC Liposyn (Fat Emulsions), 10%, IV Solution, IV, Abbott Hospital, 200ml Bag
Inactive NDC Liposyn (Fat Emulsions), 10%, IV Solution, IV, Abbott Hospital, 200ml Bag
Patient:
John Doe
Medication:
Concept Permanence
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20. Removal of a Standard Code Encoding Data Using Standard Codes
CPT 0002T
Before December 31, 2003
After December 31, 2003
CDR
CDR
Patient:
John Doe
Procedure: 0002T
Endovascular repair of infrarenal abdominal aortic aneurysm or dissection; aorto-uni-iliac or aorto-unifemoral prosthesis ?
Patient:
John Doe
Procedure: 0002T
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21. Removal of a Standard Code Encode With Internal Identifiers, Map to Standards (HDD Approach)
HDD ID
Before December 31, 2003
After December 31, 2003
CDR
CDR
Patient:
John Doe
Procedure:
Active CPT 0002T Endovascular repair of infrarenal abdominal aortic aneurysm or dissection; aorto-uni-iliac or aorto-unifemoral prosthesis
Inactive CPT 0002T Endovascular repair of infrarenal abdominal aortic aneurysm or dissection; aorto-uni-iliac or aorto-unifemoral prosthesis
Patient:
John Doe
Procedure:
Concept Permanence
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22. Lack of Comprehensive Standard Codes Encoding Data Using Standard Codes
LOINC®
April 1995
Database
6,500 lab results from the master data files of 7 U.S. labs
December 2004
Database
28,000 lab results + 14,000 clinical observations
Updates
2000: Feb, June
2001: Jan, Jul
2002: Jan, Feb,
Aug, Sep
2003: May, Oct
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23. 3M Healthcare Data Dictionary
C2C Questions for Consideration
Frameworks
3M Healthcare Data Dictionary
Overview and Current Status
• How do you define concept mapping within your system (e.g. are you mapping in between standards, or are you mapping from standards to your local data dictionary)?
Mapping to local data dictionary = centralized concept mapping; not point-to-point
• Are there any internal mechanism?
Quality Control? – Inter-rator agreement; internal terminology models; db triggers and constraints; business logic in domain specific tools
• Do you use any external tools?
We do use external technologies, but all our tooling is in-house
• Are you able to maintain the integrity of the original data in its native form (i.e. data as collected and not modified)?
Yes
Integration and Infrastructure
• How can you integrate with external tools for mapping? JavaScript library? Java? Web Services API?
Web services API = CTS v1.2
• How do you see your framework integrating with the QH Reference Implementation solution?
Native integration or through the API
Alignment to Query Health
• Where does the mapping occur? Is it at the Data Source level? Or at the Information Requestor level? Or Both?
Both; Local terminologies (terms and codes) are integrated with standards through concept mapping
• Can it be easily implemented elsewhere?
Maintenance
• Who maintains your concept mapping tool?
Domain specific mapping tools maintained by a Development team
• Who maintains the mappings and how often are they released?
A team of nearly 30 SMEs with domain specific clinical expertise and informatics training
• What is the associated cost with maintenance?
N/A
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24. Questions?
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25. Clinical Concept Management Mapping For Flexibility
David Baorto, MD, PhD
New York Presbyterian
Terminology Service
Query Health Working Group Meeting
January 17, 2012 25

26. Terminology Management at NYP
At NYP, terminology management began over 20 years ago with “the MED” (Medical Entities Dictionary) developed by Jim Cimino.
Still have that tool, but it has developed into a “Terminology Service” that serves the multi-hospital system. 26

27. How do you define concept mapping?
Tasks:
Local to local codes.
Local to standard codes.
Standard to Standard.
Types:
Equivalency. (HR = Heart Rate)
Hierarchical. (“Attending Physician Cardiology Consult Note” is a subclass of “Cardiology Consult Note”)
Relational. (“What LOINC codes test for this SNOMED code”)
Concept Mapping need not always mean finding equivalency. 27

28. How do you view criteria for concept mapping?
Goal – oriented.
“I want to plot T3 (triiodothyronine) values from different labs on a single graph.”
“Please show me all testing for RSV on a patient.”
The supporting concept maps for these 2 use cases have different requirements.
Flexible.
To support different goals.
To support changes to evolving standards.
To support updated information about local codes. 28

29. Retrieving Results Individually
K+1 = 4.2
K+3 = 3.3
K+2 = 3.2
K+1 = 3.0
K+4 = 2.6 1 2 3 29

30. Do you Retain the Integrity of the Original Data
Do you Retain the Integrity of the Original Data? Yes, by Retrieving Results according to Class
K+1 = 4.2
K+3 = 3.3
K+2 = 3.2
K+1 = 3.0
K+4 = 2.6 1 2 3 K
K#3
K#1
K#4
Original Data is retained at the granularity of the source system. Semantics about the data are maintained in the central terminology system. .
K#2 30

31. ...or retrieving Results by Sematics Decision Support for Infection Control
POSITIVE FOR STREPTOCOCCUS GROUP B, NEGATIVE FOR YEAST, LISTERIA, N. GONORR.
NYH LAB TERM: STREPTOCOCCUS AGALACTIAE
NYH LAB TERM: STREPTOCOCCUS GROUP B (STREPTOCOCCUS AGALACTIAE)
NYH LAB TERM: POSITIVE FOR STREPTOCOCCUS GROUP B.
MESSAGE: POSITIVE FOR STREPTOCOCCUS GROUP B.
MESSAGE: BACTERIAL ANTIGEN POSITIVE FOR STREPTOCOCCUS GROUP B
ME TERM: STREPTOCOCCUS AGALACTIAE (GROUP B) 1 2
Lab Results
Microorganisms 3
Group B Strep Results
Reported By Result
Group B Strep
SNOMED ID:
Result 1.
Result 2.
Result 4.
Result 6.
Result 3.
Result 5.
Result 7. 31

32. Architecture? Data Terminology Central Termino-logy 32 EHRs
Data Stores
Reporting
The MED connects in real time for production queries to certain systems, and provides batch interval results for other vendor systems.
Data
Terminology 32

33. 30 – Pharmacologic Substance
How does mapping occur?
33 - Hormone
30 – Pharmacologic Substance
Intravascular Chemistry Test
Entity Measured
Intravascular Thyroid Test
Triiodothyronine
Is Display Parameter of
Intravascular Total T3 Tests
WebCIS Thyroid Display
Serum Total T3 Tests (MCNC)
LOINC:
Mapping does not occur at data source level, nor at information requestor level, but at the level of a dedicated terminology server
– Columbia LabTest
CPMC Laboratory Test: T3 Total
NYH Lab Procedure: Triiodothyronine
CPMC Laboratory Test: Triiodothyronine,T3
Cerner ME DTA: Triiodothyronine
Cerner ME DTA: Triiodothyronine, T3 (E) 33

34. Example of Use Case Outside Scope of Standard
33 - Hormone
30 – Pharmacologic Substance
Intravascular Chemistry Test
Entity Measured
Intravascular Total T3 Tests
Intravascular Thyroid Test
Triiodothyronine
Serum Total T3 Tests (MCNC)
LOINC:
Don’t graph here!
Graph at this level!
New - Serum Total T3 Tests ng/ml
New - Serum Total T3 Tests ng/dl
CPMC Laboratory Test: T3 Total
NYH Lab Procedure: Triiodothyronine
Cerner ME DTA: Triiodothronine, T3 (E)
Cerner ME DTA: Triiodothyronine
CPMC Laboratory Test: Triiodothyronine,T3 34

35. Example of how to support Version changes of Standards
Document Class: Endocrinology Consult Note
LOINC: “Endocrinology Consult note“
NEW – Document Class: Pediatric Endocrinology Consult Note
LOINC: “Pediatric Endocrinology Hospital Consult Note“
Eclipsys East Campus Documents: Ped endocrinology Attending Consult Note
Eclipsys West Campus Document: PEDIATRIC ENDOCRINOLOGY CONSULT 35

36. Example of how to support Version changes of Standards
Intravascular Rubella IgG Antibody Tests
LOINC: “Rubella virus IgG Ab [Presence] in Serum“ X X
NEW – Intravascular Rubella IgG Antibody EIA Tests
LOINC: “Rubella virus IgG Ab [Presence] in Serum by Immunoassay“
NYH Lab Procedure: RUBELLA AB, IGG
Cerner ME DTA: Rubella Antibody IgG 36

37. Mapping Tools Internal tools – task specific External tools
Lexical, synonymy, hierarchy and semantics.
Terminology team builds some tools (perl)
Frequently more ‘service’ than ‘tool’.
Sometimes need to be creative, but often tedious.
Sufficient information about source and target codes critical. (No tool will map without it).
=> phone and .
External tools
Available crosswalks.
Standards: ICD – CPT; ICD9 – ICD10
Local: Legacy system maps.
RELMA for LOINC 37

38. Content Provision Tools
All homegrown.
Web-based MED browser.
Active memory versions for real time interaction with clinical information system. (Java web serv and C API)
Perl scripts for regular pre-determined queries. 38

39. Maintenance Slow accrual -> easy New terminology -> difficult
Once a terminology has been incorporated, maintenance consists of regular updates.
Local sources average 20 weekly changes.
New terminology -> difficult
Ex., 2 independent laboratories merged into single laboratory information system.
Importance of terminology issues often unrecognized by source systems at outset.
Communication and Information Critical for knowledge management.
Costs: 3 FTEs and 2 unix servers. 39

40. Zeshan Rajput
RELMA (LOINC)

41. Introduction to RELMA Objectives
Overview of LOINC and RELMA - Very Brief!
Demonstrate the concept mapping functionality in RELMA
Disclaimer
Contractor to ONC to support S&I Framework (Standards Development support team Informatics Lead)
Any opinions are my own and not those of ONC, Accenture, etc.
For More Information
LOINC.org
The Current Lab LOINC Tutorial

42. The Point of this Presentation
Show how (some) concept mapping is done currently
RELMA is an example of a class of concept mapping tools
These tools facilitate translation of a local set of concepts into a standard language
Tools like RELMA can help query composers create queries
But as one off tools, may be better to consume the underlying data structure and combine for uses like QH

43. Background State of the Art in Concept Mapping?
From codebooks to tools like RELMA to terminology services
LOINC - Logical Observation Identifiers Names and Codes
Use to code questions in healthcare
Lab Questions (e.g., "What is my patient's hemoglobin?" )
Clinical Questions (e.g., "How far can my patient walk in 6 minutes?" )
Answers are represented using other standards
Value + UCUM
SNOMED
etc.

44. RELMA Overview Tool distributed with LOINC Same free use (see license)
Provides manual and automated mapping functions
User creates file of local term/names and codes
RELMA tools help assign a LOINC term to local test/battery code
Features
"Common Tests" subset
"Google-Like" search functions

45. Four Ways to Present Local Codes
Direct entry into LMOF from within RELMA (painful)
Local master observation file (in case you were wondering)
Create an access table that mimics LMOF structure (less painful but tedious)
Create a delimited ASCII file from your local test catalog (good)
Load directly from HL7 v2.x messages (best)
Pulls data from OBR and OBX segments
Stores NTE segment data

46. Demonstration Run LOINC Load LMOF One or two manual searches Help file
Auto mapper and conflict reconciliation

47. Tips for Improving Mapping Success
Expand Abbreviations
Standardize Colloquial Terms ("ABG" for arterial blood gas)
Ignore "Administrative" Terms
Standardize Time References
RELMA has built in tools for these

48. Answering the QH C2C Questions
How does your tool function?
Take any of four sources of local observations and map them to LOINC using automated or manual (facilitated) approaches
Are you able to maintain the integrity of the original data in native format?
Since you have to get your information into LMOF format (in one way or another), the mapping would not affect the original data as long as you don’t delete your original data after conversion to LMOF

49. Answering the QH C2C Questions
How can your tool be leveraged? External APIs? How can QH use this tool?
No API or interface (as far as I’m aware)
Representative of tools (another is RxNav) that facilitate adoption of a single standard
Tools tend to be one off, but critical in their role
For measure/query composers, tools like RELMA can facilitate making sure you ‘ask the question’ specifically and in a language your targets likely understand
LOINC also distributed with .mdb
Could be directly imported into another interface and used to facilitate query composition, interpretation, or response

50. Answering the QH C2C Questions
Where does the mapping occur?
Probably at both the query composer and data source
Query composer – makes sure the right question is being asked
Data source – makes sure the question is translated into local terms
Can the mapping be easily implemented elsewhere?
The point of RELMA is to facilitate implementation of the LOINC standard
Constant improvements to its ease of use
Freely available for download

51. Answering the QH C2C Questions
Who maintains the tool?
Regenstrief Institute and multiple partners around the world
Who maintains the mappings?
The mappings are created by each user of RELMA
The tool is updated with LOINC, twice yearly
Associated cost of maintainence
RELMA makes mapping easier, but still requires concept dictionary curation and some ‘elbow grease’