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Data Governance and Normalization with the Mayo Clinic Enterprise

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Presentation on theme: "Data Governance and Normalization with the Mayo Clinic Enterprise"— Presentation transcript:

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 (http://www.gwas.net)

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


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