SHARPn Data Normalization November 18, 2013

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SHARPn Data Normalization November 18, 2013

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

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

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

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

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

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

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

Secondary Use Clinical Element Models GenericStatement Core CEMs GenericComponent Links AdministrativeGender, Severity, Status SecondaryUse CEMs Embracing the fact that data may not be able to be normalized and enabling bottom-up and top-down

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)

SecondaryUseNotedDrug – Output (1/2)

SecondaryUseNotedDrug – Output (2/2)

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

End-to-end DN framework

Data Normalization version 2 tree/

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

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

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 realtime clinical decision support

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