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Comparable and Consistent Data from
     Heterogeneous Systems
  Health IT Summit Conference iHT2
   San Francisco, March 27, 2012
  Christopher G. Chute, MD DrPH,
  Professor, Biomedical Informatics, Mayo Clinic
  Chair, ISO TC215 on Health Informatics
  Chair, International Classification of Disease, WHO
From Practice-based Evidence
              to Evidence-based Practice
                Clinical
                                      Registries et al.
Data           Databases
               Comparable and Consistent
                                                          Inference

  Patient           Standards                    Medical Knowledge
Encounters        Vocabularies &
                  Terminologies
Decision        Expert                      Clinical     Knowledge
                                           Guidelines
support        Systems                                  Management
                  “Secondary Use”
                      © 2012 Mayo Clinic
The Challenge
Most clinical data in the United States
 is heterogeneous – non-standard
  – Within Institutions
  – Between Institutions
Meaningful Use is mitigating, but has
 not yet “solved” the problem
  – Achieving standardization in Meaningful
    Use is sometimes minimized
                 © 2012 Mayo Clinic
© 2012 Mayo Clinic
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




                        © 2012 Mayo Clinic
Cross-integrated suite of
                                                                                        projects and products
             Themes                                                                           Projects                                Players
                                             Data Quality & Evaluation Frameworks
Data Normalization
                     Phenotype Recognition


                                                                                    Clinical Data Normalization         IBM, Mayo, Utah, Agilex, Regenstrief

                                                                                                                        Harvard, Group Health, IBM, Utah,
                                                                                                                        Mayo, MIT, SUNY, i2b2, Pittsburgh,
                                                                                    Natural Language Processing         Colorado, MITRE

                                                                                    High-Throughput
                                                                                    Phenotyping                         CDISC, Centerphase, Mayo, Utah

                                                                                    UIMA & Scaling Capacity             IBM, Mayo, Agilex, Mirth

                                                                                    Data Quality                        Mayo, Utah

                                                                                    Evaluation Framework                Agilex, Mayo, Utah

                                                                                                   © 2012 Mayo Clinic
Mission

To enable the use of       Leverage health informatics to:
EHR data for secondary     • generate new knowledge
purposes, such as          • improve care
clinical research and
public health.             • address population needs

To support the community • open-source tools
of EHR data consumers by • services
developing:              • scalable software

                    © 2012 Mayo Clinic
Normalization Options
Normalize data at source
  – Fiat, regulation, patient expectations
Transformation and mapping
  – Soul of “ETL” in data warehousing
Hybrid (graceful evolution to source)
  – “New” systems normalize at source
  – Transformation of legacy system data

                 © 2012 Mayo Clinic
Modes of Normalization
Generally true for both structured and
 un-structured data
Syntactic transformation
  – Clean up message formats
  – HL7 V2, CCD/CDA, tabular data, etc
  – Emulate Regenstrief HOSS pipeline
Semantic normalization
  – Typically vocabulary mapping
                © 2012 Mayo Clinic
Transformation Target?
Normalization begs a “normal form”
Extant national and international
 standards do not fully specify
  – Focus on HIE or internal messaging
  – Canonical data representation wanting
  – Require fully machine manageable data



                © 2012 Mayo Clinic
Clinical Data Normalization
                     Dr. Huff on Data Normalization
                           Stanley M. Huff, M.D.; SHARPn Co-
                           Principal Investigator; Professor
                           (Clinical) - Biomedical Informatics at
                           University of Utah - College of
                           Medicine and Chief Medical
                           Informatics Officer Intermountain
                           Healthcare. Dr. Huff discusses the
                           need to provide patient care at the
                           lowest cost with advanced decision
                           support requires structured and
                           coded data.


      © 2012 Mayo Clinic
Clinical Data Normalization
Data Normalization
– 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.
                  © 2012 Mayo Clinic
© 2012 Mayo Clinic
Data Element Harmonization
     http://informatics.mayo.edu/CIMI/
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
                  © 2012 Mayo Clinic
Core CEMs
Recognize that use-case specific
 work-flow enters into CEM-like objects
  – Clinical EHR implementation
  – CLIA or FDA regulatory overhead
Secondary Use tends to focus on data
Create “core” CEMs
  – Labs, Rxs, Dxs, Pxs, demographics

                © 2012 Mayo Clinic
That Semantic Bit…
Canonical semantics reduce to
 Value-set Binding to CEM objects
Value-sets should obviously be
 drawn from “standard” vocabularies
  – SNOMED-CT and ICD
  – LOINC
  – RxNorm
  – But others required: HUGO, GO, HL7
               © 2012 Mayo Clinic
On Mapping
Semantic mapping from local codes
 to “standard” codes is required
  – Not magic, humanly curated
Reality of idiosyncratic local codes is
 perverse
  – Why does every clinical lab in the
    country insist on making up its own lab
    codes?

                 © 2012 Mayo Clinic
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 exist
  – Early version production Q3 2012
                © 2012 Mayo Clinic
Normalization Flow




   © 2012 Mayo Clinic
NLP Deliverables and Tools
      http://informatics.mayo.edu/sharp/index.php/Tools
 cTAKES Releases
  – Smoking Status Classifier – cTAKES Side Effects module
  – Medication Annotator      – Modules for relation extraction

 Integrated cTAKES(icTAKES)
  – an effort to improve the usability of cTAKES for end users
 NLP evaluation workbench
  – the dissemination of an NLP algorithm requires performance
    benchmarking. The evaluation workbench allows NLP investigators
    and developers to compare and evaluate various NLP algorithms.
 SHARPn NLP Common Type
  – SHARPn NLP Common Type System is an effort for defining common
    NLP types used in SHARPn; UIMA framework.
                            © 2012 Mayo Clinic
High-Throughput Phenotyping
Phenotype - a set of patient characteristics :
  – Diagnoses, Procedures
  – Demographics
  – Lab Values, Medications
Phenotyping – overload of terms
  – Originally for research cohorts from EMRs
  – Obvious extension to clinical trial eligibility
  – Quality metric Numerators and denominators
  – Clinical decision support - Trigger criteria
                      © 2012 Mayo Clinic
Drools-based Architecture
                                        Jyotishman Pathak, PhD.
                                             discusses leveraging
                                          open-source tools such
 Clinical               Data Access                     as Drools.
Element                    Layer
Database

                                              Business Logic

                      Transformation              Inference
                          Layer                 Engine (Drools)


                                                   Service for       List of
  Transform physical representation             Creating Output      Diabetic
   Normalized logical representation           (File, Database,
  (Fact Model)                                                       Patients
                                                       etc)


                                        © 2012 Mayo Clinic
SHARP and Beacon Synergies
SHARP will facilitate the mapping of comparable
 and consistent data into information and knowledge



SE MN Beacon will facilitate the population-based
 generation of best evidence and new knowledge
SE MN Beacon will allow the application of Health
 Information Technology to primary care practice
  – Informing practice with population-based data
  – Supporting practice with knowledge
                   © 2012 Mayo Clinic
                                                     23
Medication Reconciliation
         a Pan-SHARP Project
Test data between and within enterprise
Medications lists with demographics
De-identified (age, dates)
Create Normalized CEMs
  – RxNorm, strength, form, route, ISO dates,
Move into SmartAccess RDF rendering
Reconciliation as “App” in SHARP 3
                  © 2012 Mayo Clinic
More Information

      SHARP Area 4: (SHARPn)
     Secondary Use of EHR Data
          www.sharpn.org

Southeast Minnesota Beacon Community
        www.semnbeacon.org
              © 2012 Mayo Clinic

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Presentation“Comparable and Consistent Patient Data from Heterogeneous Systems”

  • 1. Comparable and Consistent Data from Heterogeneous Systems Health IT Summit Conference iHT2 San Francisco, March 27, 2012 Christopher G. Chute, MD DrPH, Professor, Biomedical Informatics, Mayo Clinic Chair, ISO TC215 on Health Informatics Chair, International Classification of Disease, WHO
  • 2. From Practice-based Evidence to Evidence-based Practice Clinical Registries et al. Data Databases Comparable and Consistent Inference Patient Standards Medical Knowledge Encounters Vocabularies & Terminologies Decision Expert Clinical Knowledge Guidelines support Systems Management “Secondary Use” © 2012 Mayo Clinic
  • 3. The Challenge Most clinical data in the United States is heterogeneous – non-standard – Within Institutions – Between Institutions Meaningful Use is mitigating, but has not yet “solved” the problem – Achieving standardization in Meaningful Use is sometimes minimized © 2012 Mayo Clinic
  • 4. © 2012 Mayo Clinic
  • 5. 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 © 2012 Mayo Clinic
  • 6. Cross-integrated suite of projects and products Themes Projects Players Data Quality & Evaluation Frameworks Data Normalization Phenotype Recognition Clinical Data Normalization IBM, Mayo, Utah, Agilex, Regenstrief Harvard, Group Health, IBM, Utah, Mayo, MIT, SUNY, i2b2, Pittsburgh, Natural Language Processing Colorado, MITRE High-Throughput Phenotyping CDISC, Centerphase, Mayo, Utah UIMA & Scaling Capacity IBM, Mayo, Agilex, Mirth Data Quality Mayo, Utah Evaluation Framework Agilex, Mayo, Utah © 2012 Mayo Clinic
  • 7. Mission To enable the use of Leverage health informatics to: EHR data for secondary • generate new knowledge purposes, such as • improve care clinical research and public health. • address population needs To support the community • open-source tools of EHR data consumers by • services developing: • scalable software © 2012 Mayo Clinic
  • 8. Normalization Options Normalize data at source – Fiat, regulation, patient expectations Transformation and mapping – Soul of “ETL” in data warehousing Hybrid (graceful evolution to source) – “New” systems normalize at source – Transformation of legacy system data © 2012 Mayo Clinic
  • 9. Modes of Normalization Generally true for both structured and un-structured data Syntactic transformation – Clean up message formats – HL7 V2, CCD/CDA, tabular data, etc – Emulate Regenstrief HOSS pipeline Semantic normalization – Typically vocabulary mapping © 2012 Mayo Clinic
  • 10. Transformation Target? Normalization begs a “normal form” Extant national and international standards do not fully specify – Focus on HIE or internal messaging – Canonical data representation wanting – Require fully machine manageable data © 2012 Mayo Clinic
  • 11. Clinical Data Normalization  Dr. Huff on Data Normalization Stanley M. Huff, M.D.; SHARPn Co- Principal Investigator; Professor (Clinical) - Biomedical Informatics at University of Utah - College of Medicine and Chief Medical Informatics Officer Intermountain Healthcare. Dr. Huff discusses the need to provide patient care at the lowest cost with advanced decision support requires structured and coded data. © 2012 Mayo Clinic
  • 12. Clinical Data Normalization Data Normalization – 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. © 2012 Mayo Clinic
  • 13. © 2012 Mayo Clinic
  • 14. Data Element Harmonization http://informatics.mayo.edu/CIMI/ 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 © 2012 Mayo Clinic
  • 15. Core CEMs Recognize that use-case specific work-flow enters into CEM-like objects – Clinical EHR implementation – CLIA or FDA regulatory overhead Secondary Use tends to focus on data Create “core” CEMs – Labs, Rxs, Dxs, Pxs, demographics © 2012 Mayo Clinic
  • 16. That Semantic Bit… Canonical semantics reduce to Value-set Binding to CEM objects Value-sets should obviously be drawn from “standard” vocabularies – SNOMED-CT and ICD – LOINC – RxNorm – But others required: HUGO, GO, HL7 © 2012 Mayo Clinic
  • 17. On Mapping Semantic mapping from local codes to “standard” codes is required – Not magic, humanly curated Reality of idiosyncratic local codes is perverse – Why does every clinical lab in the country insist on making up its own lab codes? © 2012 Mayo Clinic
  • 18. 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 exist – Early version production Q3 2012 © 2012 Mayo Clinic
  • 19. Normalization Flow © 2012 Mayo Clinic
  • 20. NLP Deliverables and Tools http://informatics.mayo.edu/sharp/index.php/Tools  cTAKES Releases – Smoking Status Classifier – cTAKES Side Effects module – Medication Annotator – Modules for relation extraction  Integrated cTAKES(icTAKES) – an effort to improve the usability of cTAKES for end users  NLP evaluation workbench – the dissemination of an NLP algorithm requires performance benchmarking. The evaluation workbench allows NLP investigators and developers to compare and evaluate various NLP algorithms.  SHARPn NLP Common Type – SHARPn NLP Common Type System is an effort for defining common NLP types used in SHARPn; UIMA framework. © 2012 Mayo Clinic
  • 21. High-Throughput Phenotyping Phenotype - a set of patient characteristics : – Diagnoses, Procedures – Demographics – Lab Values, Medications Phenotyping – overload of terms – Originally for research cohorts from EMRs – Obvious extension to clinical trial eligibility – Quality metric Numerators and denominators – Clinical decision support - Trigger criteria © 2012 Mayo Clinic
  • 22. Drools-based Architecture Jyotishman Pathak, PhD. discusses leveraging open-source tools such Clinical Data Access as Drools. Element Layer Database Business Logic Transformation Inference Layer Engine (Drools) Service for List of Transform physical representation Creating Output Diabetic  Normalized logical representation (File, Database, (Fact Model) Patients etc) © 2012 Mayo Clinic
  • 23. SHARP and Beacon Synergies SHARP will facilitate the mapping of comparable and consistent data into information and knowledge SE MN Beacon will facilitate the population-based generation of best evidence and new knowledge SE MN Beacon will allow the application of Health Information Technology to primary care practice – Informing practice with population-based data – Supporting practice with knowledge © 2012 Mayo Clinic 23
  • 24. Medication Reconciliation a Pan-SHARP Project Test data between and within enterprise Medications lists with demographics De-identified (age, dates) Create Normalized CEMs – RxNorm, strength, form, route, ISO dates, Move into SmartAccess RDF rendering Reconciliation as “App” in SHARP 3 © 2012 Mayo Clinic
  • 25. More Information SHARP Area 4: (SHARPn) Secondary Use of EHR Data www.sharpn.org Southeast Minnesota Beacon Community www.semnbeacon.org © 2012 Mayo Clinic