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Biomedical Informatics Data Governance and Normalization with the Mayo Clinic Enterprise Christopher G Chute MD DrPH, Mayo Clinic Chair, ICD11 Revision,

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Presentation on theme: "Biomedical Informatics Data Governance and Normalization with the Mayo Clinic Enterprise Christopher G Chute MD DrPH, Mayo Clinic Chair, ICD11 Revision,"— Presentation transcript:

1 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

2 Biomedical Informatics 2 From Practice-based Evidence to Evidence-based Practice Patient Encounters Clinical Databases Registries et al. Clinical Guidelines Medical Knowledge Expert Systems Data Inference KnowledgeManagement Decisionsupport Standards Shared Semantics Vocabularies & Terminologies

3 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 Medicine3

4 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 Medicine4

5 Biomedical Informatics LOINC, RxNorm, and SNOMED Were 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 Medicine5

6 Biomedical Informatics Data Governance Data Standards Data Quality Data Architecture and Infrastructure Data Governance Support Services Policies

7 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)

8 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

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

10 Biomedical Informatics Solution Vision Enterprise Terminology 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

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

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

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

14 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)

15 Biomedical Informatics Terminology Resources Data Governance Support Services Terminology Management Terminology Management Technical support: IT Reference Repositories Operational SystemsIT Reference Repositories Data Governance Committee

16 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

17 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? 17

18 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 1811/16/10

19 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) 19

20 Biomedical Informatics 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

21 Biomedical Informatics Themes & Projects

22 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.

23 Biomedical Informatics 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

24 Biomedical Informatics

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 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 Q Early version production Q3 2010

27 Biomedical Informatics This slide is obvious Determine payload Is laboratory data 1.Physical Quantity 2. Coded Values 3. Text field

28 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)

29 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

30 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 (

31 Biomedical Informatics SHARP Area 4: More information…

32 Biomedical Informatics 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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