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Building the Electronic Data Infrastructure:  Lessons from Indiana PROSPECT

Building the Electronic Data Infrastructure:  Lessons from Indiana PROSPECT. Paul Dexter, MD Chief Medical Information Officer, Wishard Health Services Regenstrief Institute Scientist Supported by AHRQ Grant R01 HS19818-01 Dr Dexter has no conflict of interest.

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Building the Electronic Data Infrastructure:  Lessons from Indiana PROSPECT

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  1. Building the Electronic Data Infrastructure:  Lessons from Indiana PROSPECT Paul Dexter, MD Chief Medical Information Officer, Wishard Health Services Regenstrief Institute Scientist Supported by AHRQ Grant R01 HS19818-01 Dr Dexter has no conflict of interest.

  2. Enhancing health care IT infrastructure • A learning health system • Coordinated clinical, research, and quality improvement efforts • Outcomes important to patients • CER, PCOR • Leveraging EHRs and other data sources • Rapid, comprehensive, hypothesis-generating results

  3. Local opportunities and challenges • A large health information exchange • Creation of new software • Study enrollment challenges, PBRN • Investigator access to preliminary data • Integration of clinical and genetic research • Capture of patient reported outcomes • Improved support of standards

  4. Specific aims Enhance existing information technology infrastructure: • Support providers, caregivers, and researchers by providing new tools for communication and co-management • Provide de-identified access to the INPC database for CER work • Capture and store health care outcomes important to patients and their caregivers Comparative effectiveness clinical trial of medication treatment for behavioral symptoms of Alzheimer’s disease

  5. INPC Data • 80 hospitals signed up • 46 hospitals “live” •  1,400 interfaces •  12 million individuals •  4 billion structured results • Also includes: • Laboratories • Radiology centers • Public health • 5 large payors

  6. Realtime Record Count as denominator

  7. Automated study recruitment

  8. Informatics decision support research

  9. Study design tool Realtime Record Count as denominator

  10. Study design tool Realtime Record Count as denominator

  11. Integration and enhancements to eMR-ABC

  12. Automated biospecimen tracking Scan patient barcode caTrack Scan blood tube barcode caTrack Business Logic caTissue Application Phlebotomist Scan centrifuge barcode Scan blood tube barcode PDA with Scanner Web Service BioSpecimen Database Scan box barcode Lab Technician Scan aliquot barcode

  13. De-identified I2b2 queries

  14. Biospecimen Tissue Tracking Biospecimen Tissue Tracking INPC INPC myTrack Molecular Data Global ID Global ID Staging Server De-identification I2B2 Database

  15. “Vending machine” concepts Diabetes mellitus Coronary artery disease Myocardial infarction Heart failure COPD Asthma Hypertension Breast cancer Prostate cancer Lung cancer Colorectal cancer Ovarian cancer Esophageal cancer Stroke Chronic kidney disease GI bleeding HIV/AIDS Schizophrenia Hyperlipidemia Osteoarthritis Rheumatoid arthritis Falls ADHD Etc….

  16. UIMA/cTAKES open source NLP

  17. Real-time NLP

  18. Integration of LOINC survey instruments

  19. Striving for sustainability

  20. Thank you

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