Biomedical Informatics
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
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Biomedical Informatics
From Practice-based Evidence
to Evidence-based Practice
Data
Patient
Encounters
Decision
support
Clinical
Databases
Registries et al.
Inference
Shared Semantics
Standards
Medical Knowledge
Vocabularies & Terminologies
Expert
Systems
Clinical
Guidelines
Knowledge
Management
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Biomedical Informatics
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
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Biomedical Informatics
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
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Biomedical Informatics
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
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Biomedical Informatics
Data
Governance
Policies
Data
Standards
Data
Quality
Data Architecture
and
Infrastructure
Data Governance Support Services
Biomedical Informatics
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)
Biomedical Informatics
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
Biomedical Informatics
High-Level Context
External
Terminologies
LOINC, CPT, ICD-9,
ICD-O, SNOMED-CT,
UMLS, other
Enterprise
Managed
Metadata
Environment
Enterprise
Data Model
Enterprise
Terminology
Analytic
Systems
Operational
Systems
EDT, Admin,
Nursing,
DSS, other
EMRs. Lab,
Radiology, other
Research and
Collaborating
Organizations
Biomedical Informatics
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.
Enterprise
Terminology
• 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
Biomedical Informatics
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
Maps
SNOMED CT
Administrative Billing
Health
Information
Exchanges
ICD-9
ICD-10
Payers
Biomedical Informatics
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.
Biomedical Informatics
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
Biomedical Informatics
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)
Biomedical Informatics
Terminology Resources
• Data Governance Support Services
Terminology Management
• Technical support: IT Reference Repositories
Operational Systems
• Data Governance Committee
Biomedical Informatics
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
Biomedical Informatics
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?
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Biomedical Informatics
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
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Biomedical Informatics
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)
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Biomedical Informatics
SHARP Area 4:
Secondary Use of EHR Data
• Agilex Technologies
• Harvard Univ.
• CDISC (Clinical Data Interchange• Intermountain Healthcare
Standards Consortium)
• Mayo Clinic
• Centerphase Solutions
• Mirth Corporation, Inc.
• Deloitte
• MIT
• Group Health, Seattle
• MITRE Corp.
• IBM Watson Research Labs • Regenstrief Institute, Inc.
• University of Utah
• SUNY
• University of Pittsburgh
• University of Colorado
Biomedical Informatics
Themes & Projects
Biomedical Informatics
Clinical Data Normalization
• 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.
Biomedical Informatics
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
# 23
Sitting
Biomedical Informatics
Biomedical Informatics
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
Biomedical Informatics
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
Biomedical Informatics
This slide is obvious
1. Physical
Quantity
2. Coded
Values
3. Text field
Determine payload
Is laboratory data
Biomedical Informatics
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)
Biomedical Informatics
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
Biomedical Informatics
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
(http://www.gwas.net)
Biomedical Informatics
SHARP Area 4: More information…
http://informatics.mayo.edu/sharp
http://sharpn.org
Biomedical Informatics
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
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