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Enhancing an Electronic Medical Record System for Use in Clinical Research Trials

Enhancing an Electronic Medical Record System for Use in Clinical Research Trials. Presented to University Health Care By BCM Informatics Consulting November 29, 2011. MMI 498 Capstone Project Chad Hodge, Mary McConville , Bryan Watson. Fundamental Change is Needed.

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Enhancing an Electronic Medical Record System for Use in Clinical Research Trials

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  1. Enhancing an Electronic Medical Record System for Use in Clinical Research Trials Presented to University Health Care By BCM Informatics Consulting November 29, 2011 MMI 498 Capstone Project Chad Hodge, Mary McConville, Bryan Watson

  2. Fundamental Change is Needed • Challenge of translating myriad of scientific advances from workbench to bedside • Clinical trial research participation is flagging (investigators and subjects) • Discovery research stymied by interoperability barriers • Institutional effectiveness plagued by inefficiency • Burgeoning increase in healthcare delivery needs* • Cancer – 1 in 3 will develop some form of cancer • Dementia - 20% age 75+ suffer from Alzheimer’s • Heart Disease – near 50% mortality after 1st heart attack * Source: GE Healthcare

  3. Focus of change • Clinical Research Effectiveness • Improve EMR capabilities to support participation in randomized clinical trials • Improve knowledge engineering support for participation in discover research • Technology Support Improvement through Clinical Engagement • Intensify clinical involvement in all aspects of patient-related HIT • Increase informatics literacy at UHC • Position UHC for Genetics/Genomics Capability • Improve outcomes via genomic support in diagnosis and treatment • Begin with peripheral arterial disease specialty

  4. Personalized Healthcare – the promise of genetics/genomics information in clinical settings • Personalized Healthcare describes processes by which healthcare providers can customize treatment and management plans for patients based on their unique genetic makeup. • Consumers and clinicians gain useful predictive information • Clinicians benefit from linking large, medically – related datasets to individual-level genetic/genomics data. • Educational materials and other guidance is developing. • Genetic/genomic information can be helpful in health maintenance, prevention and disease management. AHIC Personalized Healthcare Detailed Use Case March 21, 2008

  5. Typical Current Intervention Earliest Clinical Detection Baseline Risk Initiating Events Earliest Molecular Detection Evolving Paradigm of care Track Progression Predict Events Inform Therapeutics Decision Support Tools: Predict Diagnose Assess Risk Refine Assessment Cost 1/reversibility Disease Burden Time Baseline Risk Disease Initiation and Progression Preclinical Progression Therapeutic Decision Support Stable Genomics: Single Nucleotide Polymorphisms Haplotype Mapping Gene Sequencing Sources of New Biomarkers: Source: “Personalized Medicine: Current and Future Perspectives,” Patricia Deverka, MD, Duke University, Institute for Genome Sciences and Policy; and Rick J. Carlson, JD, University of Washington

  6. Primary & Secondary Use of Data • Technology Advancements (Better, Faster, Cheaper) • The $1,000 Complete Genome • BioMarkertesting: POC/Continuous • Superior molecular imaging • Integrate with the EHR for early detection and treatment guidance • Automated screening upon visit and initial eligibility determined • Prospective use of research for treatment pathways

  7. How do we improve care with great data…

  8. Holistic Data Approach Knowledge Repository Maps Codes Queries Models Rules Forms Constraints Interface to Model Terminology Transition, Decision Support & Business Rules Unified Data Repository of Models & Terminology Based Data Data Assembled from User Generated Alerts, Queries, and Forms HL7 ATNA XDS Interface Engine PDQ CDA 1 2 3 4 5 Standard Models & Terminology Coded, Computable Clinical Data Configured by Knowledge Workers Shareable & Reusable Assets Source: GE Healthcare

  9. Advanced Integration • Infrastructure • Terminology • Content • Genomic Extensions • Advanced CDSS • eHealth • Portal • HIE • Collaboration • Patient Access • Disruptive and visionary development of integrated genomics/clinical repository • Foundation for Personalized Medicine • Prepares CDR for target analytics, outcomes research, and trials recruitment • Drives bioresearch and discovery Clinicians Researchers Informaticists Community Source: GE Healthcare

  10. NLP Processes Support Computable Knowledge Source: Using electronic health records to drive discovery in disease genomics. Kohane, Isaac S.s.l. : Nature Reviews Genetics, 2011, Vol. 12.

  11. Envisioned Data Workflow Source: Using electronic health records to drive discovery in disease genomics. Kohane, Isaac S.s.l. : Nature Reviews Genetics, 2011, Vol. 12.

  12. Program Management Recommendation

  13. Roadmap Trajectory • Terminology for Genomics • Define and integrate terminology assets • Define key use cases for stakeholders • CDR Genomic Extensions • Extend terminology assets and clinical data model to support genomic content • Implement use cases and overall infrastructure • Repository Utilization and Discovery • Feed system with genomic research data and basic clinical data for proof-of-concept • Implement basic analytics • Extend Use Cases to Clinical Domain • Extend data integration into full, longitudinal record • Implement analytics to support basic data correlations between genomic and clinical information • Pilot Clinical Genomic Platform • Explore targeted opportunities in areas, such as trials recruitment or outcomes research • Access opportunities to rollout integrated offering to target facility

  14. Holistic Informatics Management Approach

  15. Ethical & Legal Implications • Questions to be vetted • Who should get these tests? • Who should pay for these tests? • How should this data be stored? • How will this data be used?

  16. Financial Projections • Estimates through 2016 • Medical education deficit – increase by 18% • Research funding loss – increase by 14% • Clinical care margin – decrease by 8% • Drivers increasing shortfalls • NIH funding reductions • Public economic decline (affects endowments and philanthropy) • Increased infrastructure and regulatory expenses • Medical education costs are outpacing inflation and tuition

  17. Leveraging interoperability specifications…

  18. Personalized Medicine Use Case

  19. Future directions…

  20. Through Aggregated Data Analysis • Risk Prediction • Recommendation - Avoid high fat content in diet • Pharmacogenomics • Rx Treatment - Drug dosages determined by genetic profile • New Therapies Discovered • Gene therapy for PAD

  21. Discussion…

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