biomedical informatics data governance and normalization with the mayo clinic enterprise christopher...

Biomedical Informatics

Data Governance and Normalization with the Mayo Clinic Enterprise

Christopher G Chute MD DrPH, Mayo ClinicChair, ICD11 Revision, World Health OrganizationChair, ISO TC215 on Health Informatics

CTSA Ontology WorkshopBaltimore

25 April 20121


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From Practice-based Evidenceto Evidence-based Practice

PatientEncounters

ClinicalDatabases Registries et al.

ClinicalGuidelines

Medical Knowledge

ExpertSystems

Data Inference

KnowledgeManagement

Decisionsupport

StandardsShared Semantics

Vocabularies & Terminologies


Mayo Clinic – Normalization NeedsVariations 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 3


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



LOINC, RxNorm, and SNOMEDWe’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



Data Governance

Data Standards

Data Quality

Data Architecture and Infrastructure

Data Governance Support Services

Policies


Terminology VisionMayo 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)


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


High-Level Context

External Terminologies

LOINC, CPT, ICD-9, ICD-O, SNOMED-CT,UMLS, other

EnterpriseData Model

EnterpriseManagedMetadataEnvironment

Analytic Systems

EDT, Admin,Nursing,DSS, other

Operational Systems

EMRs. Lab,Radiology, other

Research andCollaboratingOrganizations

EnterpriseTerminology


Solution Vision

EnterpriseTerminology

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


HypertensionHyperlipidemiaDepressionHypothyroidismObesityOsteoporosisCoronary artery diseaseAnemiaDiabetes mellitusObstructive sleep apnea

Clinical ProblemsClinical Facing

Code Systems

SNOMED CTMaps

Administrative Billing

ICD-9

ICD-10

Payers

Health Information Exchanges

Value Set and Mappings Example


Problem List Terminology Example

Develop and approve

Provide in an

accessible format

Distribute and Load

Use in clinical and admin processes

EMR’s, Scheduling, Orders, etc.


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


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)


Terminology Resources

• Data Governance Support Services Terminology Management

• Technical support: IT Reference Repositories Operational Systems

• Data Governance Committee

http://quarterly.mayo.edu/photoListing/default.cfm?reporting_unit_code=57513&reporting_unit_code_descriptio=Terminology%20Management&supervisor_perid=10077367

http://quarterly.mayo.edu/photoListing/default.cfm?reporting_unit_code=56128&reporting_unit_code_descriptio=Reference%20Repositories&per_id=14490773&supervisor_perid=10302956

http://intranet.mayo.edu/charlie/enterprise-information-management-program/committees-groups/teams-mcdgc/


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

http://intranet.mayo.edu/charlie/enterprise-information-management-program/mayo-clinic-data-standards/

http://intranet.mayo.edu/charlie/enterprise-information-management-program/data-governance/enterprise-data-model/

http://mayocontent.mayo.edu/datagovsupp/DOCMAN-0000109932


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Data Granularity DissonancePractice 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?


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, Mayo1811/16/10


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SHARP: Area 4: Secondary Use of EHR DataA $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)


SHARP Area 4: Secondary Use of EHR Data

• Harvard Univ. • Intermountain Healthcare• Mayo Clinic• Mirth Corporation, Inc.• MIT • MITRE Corp. • Regenstrief Institute, Inc.• SUNY • University of Colorado

• Agilex Technologies• CDISC (Clinical Data Interchange

Standards Consortium)• Centerphase Solutions• Deloitte• Group Health, Seattle• IBM Watson Research Labs• University of Utah• University of Pittsburgh


Themes & Projects


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.


A diagram of a simple clinical model

# 23

data 138 mmHg

quals

SystolicBPObs

data Right Arm

BodyLocation

data Sitting

PatientPosition

Clinical Element Model for Systolic Blood Pressure


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


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


This slide is obvious

Determine payloadIs laboratory data

1. Physical Quantity

2. CodedValues

3. Text field


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)


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


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)

http://www.gwas.net/


SHARP Area 4: More information…

http://informatics.mayo.edu/sharphttp://sharpn.org

http://informatics.mayo.edu/sharp

http://sharpn.org/


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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

biomedical informatics data governance and normalization with the mayo clinic enterprise christopher...

Documents

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biomedical informatics

standardized terminology

mayo clinic chair

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needed slide

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