A Vision for Bigger Biomedical Data: Integration of REDCap with Other Data Sources

Transcription

1 A Vision for Bigger Biomedical Data: Integration of REDCap with Other Data Sources Ram Gouripeddi Assistant Professor, Department of Biomedical Informatics, University of Utah Senior Biomedical Informatics Scientist, Biomedical Informatics Core, Center for Clinical and Translational Sciences, University of Utah 2014 REDCapCon Park City September 23 rd, 2014

2 Overview REDCap Landscape University of Utah Usage of REDCap land scape A Vision for Bigger Biomedical Data OpenFurther Moving Clinical Data to REDCap Integrating REDCap and Clinical Data

3 REDCap Landscape 1,173 Active institutional partners 85 Countries 130,000 Projects 173,000 Users Courtesy:

4 Source of Citation Numbers: Google Scholar REDCap Publications Citing: Harris, Paul A., Robert Taylor, Robert Thielke, Jonathon Payne, Nathaniel Gonzalez, and Jose G. Conde. Research Electronic Data Capture (REDCap) - A Metadata-Driven Methodology and Workflow Process for Providing Translational Research Informatics Support. Journal of Biomedical Informatics 42, no. 2 (April 2009): doi: /j.jbi

5 Publication Categories Random sample of publications

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7 REDCap Project UU

8 Project Attributes Data Collection Instruments Number: 6,783 Fields: 263,041 Survey responses: 35,030 People Survey participants: 19,918 Survey invitations sent: 25,339

9 REDCap Integration with Other Biomedical Data Rich Source Potential Secondary Uses

10 The Case of EHR Data E.g. Clinical Trial Recruitment Medical Record Numbers, Patient Identifiers E.g. Surveys, Manual Classification of Radiology Reports (PHIS+)

11 OpenFurther Federating and integrating platform developed by the Biomedical Informatics Core, University of Utah. On-the-fly real-time federation of health information from heterogeneous data sources Data source partners do not need to extract data and/or build a new database Data remains in its native format Is as up-to-date as the data source Join data from multiple sources for research

12 Typical Applications Cohort Finding for Prospective Research Comparative Effectiveness Research (CER) Infrastructure Public Health Surveillance Datasets for Observational Studies Research Networks: Distributed & Centralized

13 Current Implementations University of Utah FURTHeR: Cohort Identification & Datasets for Analysis PHIS+: Aggregated database for performing Comparative Effectiveness Research University of North Carolina: Cohort Identification Air Quality Clinical Data Integration Utah Department of Health: Data Aggregation & Datasets for Analysis

14 Component Overview

15 Virtual Identity Resolution on the GO (VIRGO) On the fly demographics analysis for record linkage No permanent PHI storage Response composed of OpenFurther IDs Algorithms Field-specific weights Adaptable to others Identify unique individuals in REDCap and clinical data

16 Quality & Analytics Framework (QAF) A service oriented architecture that can assess the quality of heterogeneous electronic health data sources in a distributed environment. Adapters Data Sources User Interface Quality Service Quality Analysis Repository Terminology Services Metadata Services

17 Metadata Repository (MDR) Built in-house, standards-based Artifacts (Knowledge) Logical Models, Local Models, model mappings Administrative information Descriptive information XQuery Translation Programs Models supported - Open Source Initiative Local: FURTHeR, UUEDW, UPDBL, Intermountain Healthcare Datamart Public: OMOP, i2b2, OpenMRS

18 Terminology/Ontology Server (TS) Provides tools for: Management standard and local terminologies Mapping local to standards Browsing & Searching terminologies Creation subsets Extension of standards terminologies Versioning of terminologies OpenFurther includes a layer of web-services that leverage DTS APIs

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20 Clinical Data REDCap

21 Integrating REDCap with Clinical Data VIRGO: Links unique individuals QAF: Assesses the quality of data for integration MDR & TS: Enable semantic and syntactic integration of data

22 Metadata & Terminology of REDCap Data Age at a particular time Gestational Age Age Current age Age of mother

23 Standardizing REDCap Data Project Metadata Data Collection Item Metadata Field Metadata Standardizing REDCap Data Field and Value Terminology

24 Metadata & Terminology Matching Machine learning based approaches to discover patterns and match REDCap data. Includes contextualizing projects, data collection items, attributes and values. Contextual matches for linking across REDCap projects and clinical data including federation/integration with OpenFurther.

25 Involvement of the REDCap User? Success of REDCap Ease of use Standardization not a requirement. Standardizing and linking of REDCap data might have to occur as a process not effecting the user s workflow.

26 Conclusion Complex but rich data source. Useful for secondary purposes. Behind the scenes standardization and linkage. Possibility to federate/integrate with other biomedical data to generate big data.

27 Acknowledgements Bernie LaSalle BMIC Team Members Collaborators: University of Utah Enterprise Data Warehouse Center for High Performance Computing PHIS+ Partners Utah Department of Health OpenFurther supported by the NCRR/ NCATS Grants UL1RR and 3UL1RR S2, National Center for Clinical and Translational Science 1UL1TR001067, along with the University of Utah Research Foundation, and grant 1D1BRH20425 (DHHS). PHIS+ funded by grant R01 HS from AHRQ, (DHHS). VIRGO (MPI) supported by NLM Grant 5RC2LM REDCap is supported by Center for Clinical and Translational Sciences grant support 8UL1TR

28 Thank You