1. Data Governance and Normalization with the Mayo Clinic Enterprise
Classifications and Terminology
CG Chute
Data Governance and Normalization with the Mayo Clinic Enterprise
Christopher G Chute MD DrPH, Mayo Clinic
Chair, ICD11 Revision, World Health Organization
Chair, ISO TC215 on Health Informatics
CTSA Ontology Workshop
Baltimore
25 April 2012
© Mayo Clinic College of Medicine 2007

2. From Practice-based Evidence to Evidence-based Practice
Classifications and Terminology
CG Chute
From Practice-based Evidence to Evidence-based Practice
Inference
Data
Clinical
Databases
Registries et al.
Medical Knowledge
Shared Semantics
Patient
Encounters
Standards
Vocabularies & Terminologies
Decision
support
Expert
Systems
Knowledge
Management
Clinical
Guidelines
© Mayo Clinic College of Medicine 2007

3. Mayo Clinic – Normalization Needs Variations on Secondary Use
Clinical Decision Support
Quality measurement and improvement
Best practice discovery and application
New knowledge discovery (research)
Genotype to phenotype association
Outcomes Research
Comparative Effectiveness Analyses
© 2007 Mayo Clinic College of Medicine

4. Comparable and Consistent Data
Inferencing from data to information requires sorting information into categories
Statistical bins
Machine learning features
Accurate and reproducible categorization depends upon semantic consistency
Semantic consistency is the vocabulary problem
Almost always manifest as the “value set” problem
© 2007 Mayo Clinic College of Medicine

5. LOINC, RxNorm, and SNOMED We’re not done yet…
Picking high-level, Meaningful Use conformant, and fashionable vocabularies is not hard
Finding which subsets (value sets) map to
For each application and interface
For each data element and variable
Implies thousands of value sets for the average enterprise
Imposes a normalization and mapping challenge
© 2007 Mayo Clinic College of Medicine

6. Data Governance Support Services
Policies
Data Standards
Data Quality
Data Architecture and Infrastructure
Data Governance Support Services

7. Terminology Vision Mayo Clinic Production Terminology Support
To incrementally standardize Mayo Clinic terminology
To access and use standardized terminology in applications, interfaces, warehouses, and processes across Mayo Clinic
To reduce cost and effort of developing and maintaining redundant and disparate terminologies and mappings for operations, analytics, and decision support
To support business needs and strategies that require or benefit from standardized terminology (e.g., ICD-10 conversion, Meaningful Use, Health Information Exchange)

8. Principles
Leverage external standards
Support Mayo needs that are not yet part of an external standard and promote them to the industry
Align with the Enterprise Data Model and other types of Mayo Clinic Data Standards
Maintain a balance between short-term and strategic needs and solutions

9. High-Level Context Enterprise Terminology External Terminologies
LOINC, CPT, ICD-9,
ICD-O, SNOMED-CT,
UMLS, other
Enterprise
Managed
Metadata
Environment
Enterprise
Data Model
Enterprise
Terminology
Analytic
Systems
EDT, Admin,
Nursing,
DSS, other
Operational
Systems
EMRs. Lab,
Radiology, other
Research and
Collaborating
Organizations

10. Solution Vision
Tools and Services: Load, Author, Map, Store, Search, Browse, Publish, Access
Code Systems
Mayo Clinic Backbone Terminology, ICD-9 CM, ICD-10 CM, SNOMED CT, RxNorm, LOINC, etc.
Mappings
Clinical Problems to ICD-9 CM, ICD-10 CM, and SNOMED CT, Lab Orders/Results to LOINC
Value Sets
Clinical Problem, Body Structure, Body Side, Administrative Gender, Unit of Measure
Picklists
Admitting Diagnosis, Lab Unit of Measure
Enterprise
Terminology

11. Value Set and Mappings Example
Code Systems
Clinical Problems
Hypertension
Hyperlipidemia
Depression
Hypothyroidism
Obesity
Osteoporosis
Coronary artery disease
Anemia
Diabetes mellitus
Obstructive sleep apnea
Clinical Facing
SNOMED CT
Maps
Health
Information
Exchanges
Administrative Billing
ICD-9
ICD-10
Payers

12. Problem List Terminology Example
Develop and approve
Provide in an accessible format
Use in clinical and admin processes
Distribute and Load
EMR’s, Scheduling, Orders, etc.

13. Terminology Management Offerings
Methodologies and Best Practices
Terminology Standardization Facilitation
External Standards Expertise
Terminology Mapping (and 3rd party recommendations)
External Terminology Management
Mayo Clinic Terminology Standards Management

14. Tools and Terminology Services
Health Language Inc.
Central authoring tool (LExScape)
Mapping assistance tool (LEAP)
Web-based browser (LExPlorer)
Importer/Exporter and Web Services
Mayo Developed & Supported
Exports
Web Services (as needed)

15. Terminology Resources
Data Governance Support Services Terminology Management
Technical support: IT Reference Repositories Operational Systems
Data Governance Committee

16. Approved Data Standards and Policy
Approved Data Standards on Web
Mayo Clinic Data Standards
Enterprise Data Model
Data Standards Policy
Data Governance Policy for Data Standards

17. Data Granularity Dissonance Practice vs. Research vs. Public Health
Clinical data is impressionistic
Not protocol driven – quaint rambling text…
Detail and consistency is a happy coincidence
However, can be holistic; intuit the unanticipated
Research data is rigid though rigorous
Detailed, structured, complete (ideally), coherent
May miss the forest for the chlorophyll
Public Health comprehends broader interests
Metrics of life-style, high risk behavior, fitness
Non-clinical circumstances, settings, environment
Can there be reconciliation among use-cases?

18. electronic MEdical Records and GEnomics NHGRI eMERGE (U01) Goals
GWAS – Genome Wide Association Study
600k Affy chip
High-throughput Phenotyping
Disease algorithm scans across EMRs
“catch up” with high throughput genomics
Generalize Phenotypes across the Consortium
Measure reproducibility of algorithms among members
Vanderbilt, Northwestern, Marshfield, Group Health Seattle, Mayo
11/16/10

19. SHARP: Area 4: Secondary Use of EHR Data A $15M National Consortium
16 academic and industry partners
Develop tools and resources that influence and extend secondary uses of clinical data
Cross-integrated suite of project and products
Clinical Data Normalization
Natural Language Processing (NLP)
Phenotyping (cohorts and eligibility)
Common pipeline tooling (UIMA) and scaling
Data Quality (metrics, missing value management)
Evaluation Framework (population networks)

20. SHARP Area 4: Secondary Use of EHR Data
Agilex Technologies
CDISC (Clinical Data Interchange Standards Consortium)
Centerphase Solutions
Deloitte
Group Health, Seattle
IBM Watson Research Labs
University of Utah
University of Pittsburgh
Harvard Univ.
Intermountain Healthcare
Mayo Clinic
Mirth Corporation, Inc.
MIT
MITRE Corp.
Regenstrief Institute, Inc.
SUNY
University of Colorado

21. Themes & Projects

22. Clinical Data Normalization
Clinical data comes in all different forms even for the same kind of information.
Comparable and consistent data is foundational to secondary use
Clinical Data Models – Clinical Element Models (CEMs)
Basis for retaining computable meaning when data is exchanged between heterogeneous computer systems.
Basis for shared computable meaning when clinical data is referenced in decision support logic.

23. A diagram of a simple clinical model
Clinical Element Model for Systolic Blood Pressure
SystolicBPObs
data
138 mmHg
quals
BodyLocation
data
Right Arm
PatientPosition
data
Sitting

24. In order to represent detailed clinical data models, we have designed The Clinical Element Model (CEM). When we state “The Clinical Element Model” we are referring to the global modeling effort as a whole, or in other words, our approach to representing detailed clinical data models and the instances of data which conform to these models.
How are Clinical Models used?
Data entry screens, flow sheets, reports, ad hoc queries
Basis for application access to clinical data
Computer-to-Computer Interfaces
Creation of maps from departmental/foreign system models to the standard database model
Core data storage services
Validation of data as it is stored in the database
Decision logic
Basis for referencing data in decision support logic
Does NOT dictate physical storage strategy

25. Data Element Harmonization
Stan Huff – CIMI
Clinical Information Model Initiative
NHS Clinical Statement
CEN TC251/OpenEHR Archetypes
HL7 Templates
ISO TC215 Detailed Clinical Models
CDISC Common Clinical Elements
Intermountain/GE CEMs

26. Normalization Pipelines
Input heterogeneous clinical data
HL7, CDA/CCD, structured feeds
Output Normalized CEMs
Create logical structures within UIMA CAS
Serialize to a persistence layer
SQL, RDF, “PCAST like”, XML
Robust Prototypes Q1 2012
Early version production Q3 2010

27. This slide is obvious Physical Quantity 2. Coded Values 3. Text field
Determine payload
Is laboratory data

28. Natural Language Processing
Information extraction (IE): transformation of unstructured text into structured representations and merging clinical data extracted from free text with structured data
Entity and Event discovery
Relation discovery
Normalization template: Clinical Element Model
Improving the functionality, interoperability, and usability of a clinical NLP system(s), namely the Clinical Text Analysis and Knowledge Extraction System (cTAKES)

29. High-Throughput Phenotyping
Phenotype - identifying a set of characteristics of about a patient, such as:
A diagnosis
Demographics
A set of lab results
Phenotyping – overload of terms
Originally for research cohorts from EMRs
Obvious extension to clinical trial eligibility
Note relevance to quality metrics
Numerator and denominator constitute phenotypes
Clinical decision support
Trigger criteria

30. EMR Phenotype Algorithms
Typical components
Billing and diagnoses codes; Procedure codes
Labs; Medications
Phenotype-specific co-variates (e.g., Demographics, Vitals, Smoking Status, CASI scores)
Pathology; Imaging?
Organized into inclusion and exclusion criteria
Experience from eMERGE Electronic Medical Records and Genomics Network (

31. SHARP Area 4: More information…

32. Where is This Going?
Standards and interoperability are crucial to the operation and success of academic medical centers
The boundaries among clinical and research standards are eroding
Information standards, especially vocabularies, are the foundation for scientific synergies
Data Governance, at an enterprise (and arguably a national) level is critical