1. SHARPn Data Normalization November 18, 2013

2. Data-driven Healthcare Big Data Knowledge Research Practice Analytics Domain Pragmatics Experts

3. A framework for clinical data reuse Replicate Query Production Systems Production Databases Enterprise Repository/ Data Warehouse Workgroup Datamarts Query Workflow or goal specific Data Analytics NLP and Data Normalization

4. SHARPn Data Normalization  Goals –To conduct the science for realizing semantic interoperability and integration of diverse data sources –To develop tools and resources enabling the generation of normalized EMR data for portable and scalable secondary uses

5. Data Normalization Information Models Target Value Sets Raw EMR Data Tooling Normalized EMR Data Normalization Targets Normalization Process

6. Normalization Targets  Clinical Element Models –Based on Intermountain Healthcare/GE Healthcare’s detailed clinical models  Terminology/value sets associated with the models –Using standards where possible

7. Normalization Process  Configuration of Model (Syntactic) and Terminology (Semantic) Mapping  UIMA Pipeline to transform raw EMR data to normalized EMR data based on mappings

8. Four Subprojects  Clinical Information Modeling  Value Sets Management  End-to-End Pipeline  Normalized Data Representation and Store

9. Secondary Use Clinical Element Models GenericStatement GenericComponent Core CEMs SecondaryUse CEMs Links AdministrativeGender, … Severity, Status AdministrativeGender, … Severity, Status Embracing the fact that data may not be able to be normalized and enabling bottom-up and top-down http://www.clinicalelement.com

10. Status of Secondary Use CEMs  Model specification is final  CEM Browser is in production  Manuscript is in preparation Future: Secondary Use CEMs and CEM Browser will be maintained through Clinical Information Modeling Initiative (CIMI)

13. SecondaryUseNotedDrug – Output (1/2)

14. SecondaryUseNotedDrug – Output (2/2)

15. NLP in data normalization  A large amount of clinical information is in clinical narratives, NLP is a critical component in data normalization  cTAKES has been wrapped into the data normalization pipeline to normalize data in clinical narratives

16. End-to-end DN framework

17. Data Normalization version 2 http://sourceforge.net/p/sharpn/datan/code/HEAD/tre e/

18. DN activities after SHARPn (1) – Clinical Information Model Initiatives

19. DN activities after SHARPn (2) – Open Health Natural Language Processing (OHNLP)  Use of the Data Normalization information model as the base to define a Common Type System to capture basic clinical information models  Use of the Data Normalization pipeline to improve interoperability of various clinical information models

20. DN activities after SHARPn (3) – Clinical decision support and phenotyping  The use of NLP and Big Data for Late Binding Data Normalization  Practical implementation of Late Binding Data Normalization and Drools for real- time clinical decision support