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Temporal Exploration of EHRs Using the i2b2 Open Source Platform

Temporal Exploration of EHRs Using the i2b2 Open Source Platform. Vivian Gainer Partners Healthcare. In the Beginning …. Returning Data to Investigators. Identified data is gathered from RPDR and other Partners sources. Output files placed in special directory.

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Temporal Exploration of EHRs Using the i2b2 Open Source Platform

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  1. Temporal Exploration of EHRs Using the i2b2 Open Source Platform Vivian Gainer Partners Healthcare

  2. In the Beginning …

  3. Returning Data to Investigators Identified data is gathered from RPDR and other Partners sources Output files placed in special directory Files include a Microsoft Access Database

  4. Researchers’ Wish List • To view many patients at a time • To have a complete view of a patient’s data • To view a patient over time • To view patient text notes in the application • To add power to the notes via NLP • To annotate the data • To save and reuse work

  5. Our Wish List • An open source framework that is: • Easy to use for a variety of users • Scalable • Extensible • Robust • Interoperable • Platform-independent • HIPAA-compliant

  6. Tools and Architecture i2b2 HIVE: • Service-Oriented Architecture • XML messages • Cells • Eclipse (plug-ins)

  7. Process

  8. i2b2 Workbench

  9. Patient Set Query Diabetes Asthma Browse Concepts Patient Set Diagnosis Asthma Diabetes Test Glucose PFT Demographics Age Sex Patient 1: 63 year Male Patient 2: 48 year Female Patient 3: 24 year Male Patient Data Query Age Sex Patient Data User Interactions

  10. Browse concepts

  11. Create a Query

  12. Create a model for the Timeline

  13. Timeline

  14. View a text report

  15. Parse the text report

  16. HELP

  17. Add another plug-in: TableViewhttp://ccgb.umn.edu/software/java/apps/TableView/

  18. The workbench and timeline allow users to: • survey the data to see what is available • check data integrity for obvious anomalies • compare data coming from different sources • display data concisely on single patients • drill down in term hierarchies • perform time-oriented queries • Show time-oriented averages (trends) within the data using sentinel events to synchronize the starting points.

  19. What are we going to do with it? • Extend phenotypic data collection • Add anonymous specimen repository • Add a Bayesian inference engine • Predictive modeling • Perform clinical trials in-silico • Discovering correlations within data (relationship networks) • Pharmacovigilance • Genomic studies • Geograpic Information Systems (GIS) studies …

  20. Thanks! HCIL i2b2 Shawn Murphy Mike Mendis Lori Phillips Raj Kuttan Wensong Pan Janice Donahoe

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