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NIBIB Health IT Initiatives and Medical Image Sharing

NIBIB Health IT Initiatives and Medical Image Sharing. James Luo Ph.D. Program Director, Biomedical Informatics Programs National Institute of Biomedical Imaging and Bioengineering National Institutes of Health. The 4th US-China Roundtable Conference on Scientific Data Cooperation

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NIBIB Health IT Initiatives and Medical Image Sharing

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  1. NIBIB Health IT Initiativesand Medical Image Sharing James Luo Ph.D. Program Director, Biomedical Informatics Programs National Institute of Biomedical Imaging and Bioengineering National Institutes of Health The 4th US-China Roundtable Conference on Scientific Data Cooperation March 29-30, 2010

  2. US Healthcare Expenditures • US total healthcare expenditures reached $2.3 trillion in 2008 • $7,681 per person • 16.2% of total GDP • Projection: it will reach $4.48 trillions (19.3% of GDP) in 2019 Source: DHHS

  3. Health IT & ARRA • A 2005 RAND study projects that the adoption of Health IT in healthcare sectors: • A mean annual savings of almost $42 billion in US • The American Recovery and Reinvestment Act of 2009 (ARRA) provides over $19 billion stimulus funds for the development and adoption of Health IT • $2 billion for ONC to set up the standards and “meaningful use” • Healthcare Information Technology Standards Panel (HITSP) • IHE, HL7, DICOM, etc. • Nationwide Health Information Network (NHIN) • Establishment of Certification Programs for Health IT • Certification Commission for Healthcare IT (CCHIT) • $17 billion incentives for adoption of EHR

  4. Benefits of Health IT • Interoperable Health IT can improve individual patient care in numerous ways: • Provide complete and accurate health information at the point of care. • Allow secure exchange between patients and providers. • Allow more informed decision making to enhance the quality and reliability, while reducing errors • Provide increased efficiencies in care and administration • Reduce unnecessary or repetitive tests. • Improve population health. • Integrated EHR systems with image, genomics, pharmacogenomics (PGx) data and AHRQ clinical guidelines support evidence-based clinical decision and personalized medicine

  5. Kaiser Case Study • A Kaiser study published in Health Affairs showed that using EHR in 2004 to 2007: • office visit rate in Kaiser’s Hawaii region dropped 26.2 percent. • “phone visits” increased more than 8 fold, • online messaging rose nearly 600 percent. • In 2007 • office visits 66% (compare to ~ 100% in 2004) • phone “visits” 30% • online consultations 4% • The use of EHR and better connectivity with patients (phone, online) has made Kaiser more efficient

  6. Kaiser Case Study 2 • With complete patient information available to them in the EHR, physicians can respond to patients’ questions about minor problems without seeing them. • These modes of patient contact don’t lower either patient satisfaction or the quality of care. • it reduces errors. • Integrated EHR systems reap the benefits due to increased efficiency, reducing office visits, avoiding redundant tests and prescriptions

  7. Image in Health IT • Medical images play a critical role in: • diagnosis and prognosis of diseases • therapeutic planning • medical decision-making, safety assessment, and risk management • clinical research to discover effective technologies, therapeutics, diagnostics, and prevention strategies for different populations • tracking specific diseases and response to drug • analyses the effectiveness of therapeutic • Important part of electronic health records (EHR)

  8. NIBIB’s Initiatives in Health IT and Clinical Image Sharing • NIBIB launched its Health IT and clinical image sharing program in 2009 • NIBIB–RSNA research project: develop a network for patient-controlled medical image sharing built upon the IHE (Integrating the Healthcare Enterprise) – HITSP and ONC accepted standards. • Allow image sharing across RHIOs: UCSF, U. Maryland, Mayo, U. Chicago, Mount Sinai. • NIBIB awarded Grand Opportunity (GO) grants in clinical image sharing. • Address image sharing in RHIOs: • U. Alabama, Birmingham • Wake Forest U.

  9. Objectives of Clinical Image Sharing • To enable the sharing of radiology images across health care institutions and vendor systems. • To aim toward increasing the speed and accuracy of data on which medical decisions are based, • To reduce imaging redundancy and overutilization. • To improve the quality of patient care by making images immediately available. • A key feature: patients control the access to and sharing of the images, e.g. • Consumer based control and ownership of their imaging exams through Personal Health Records (PHRs) • Rural, underserved populations or academic patient care environment image sharing is encouraged.

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  12. Integrating Data, Models, and Reasoning in Critical Care Challenges: “data overload”, false alarms, poor data organization in the ICU early warning signs often difficult to recognize • Opportunity: • Richness of ICU data makes possible advanced monitoring systems to track and predict pathophysiologic state of patients. Roger Mark, Massachusetts Institute Technology, R01 EB001659 (BRP)

  13. Roger Mark, Massachusetts Institute Technology, RO1-EB001659 (BRP) Predictive alerts for impending hemo-dynamic instability Hypotensive episode occurred approximately 2 hours after alert Nursing notes and discharge summaries are de-identified automatically for the research database. Techniques to assess signal quality

  14. MIMIC II Database: Multi-parameter Intelligent Monitoring for Intensive Care a massive research-enabling database supports development and evaluation of advanced patient monitoring systems contents: 30,000 patient records; 4,000 include waveforms data includes: physiologic trends; discharge summaries; nurses’ notes; IV meds; physician orders; lab reports; ventilator settings; etc. De-identified database is made freely available to research community via PhysioNet (www.physionet.org) Roger Mark, Massachusetts Institute Technology, R01 EB001659 (BRP)

  15. PhysioNet the research resource for complex physiologic signals

  16. Design of the PhysioNet Website Scientific Community-at-Large PhysioNet Gateway to the Resource PhysioBank Archive of Physiologic Signals and Time Series PhysioToolkit Open Source Software For Data Analysis

  17. What is PhysioBank? PhysioBank currently includes: >40 collections of cardiopulmonary, neural, and other biomedical signals from healthy subjects and patients with a variety of conditions with major public health implications, including sudden cardiac death, congestive heart failure, epilepsy, gait disorders, sleep apnea, and aging.

  18. >30,000 researchers, students, manufacturers, educators, each month From all 50 US states and DC Users from >100 other countries Who Uses PhysioNet / Where?

  19. Image Data Sharing in Research • Alzheimer's disease neuroimaging initative (ADNI) – >$60 million, PPP • Goal: collection of data and samples (800 cases) • to establish a brain imaging biomarker, • to identify the best markers for following disease progression and monitoring treatment response • Determine the optimum methods for acquiring, processing, and distributing images and biomarkers in conjunction with clinical and neuropsychological data. • “Validate” imaging and biomarker data by correlating with neuropsychological and clinical data. • Provide public access to all data and bio-specimens http://www.loni.ucla.edu/ADNI/

  20. Hippocampal Atrophy as a Quantitative Trait in a Genome-Wide Association Study Identifying Novel Susceptibility Genes for Alzheimer’s Disease UC Irvine: S. Potkin, G Guffanti, A Lakatos, JA Turner, F Kruggel, JH Fallon, Other Contributors: AJ Saykin, A Orro, S Lupoli, E Salvi, M Weiner, F Macciardi, ADNI • The case-control analysis identified APOE and a recent risk gene, TOMM40, at a genome-wide significance level of p-value ≤ 10−6 • The quantitative trait analysis identified 21 genes or chromosomal areaswith at least one SNP with a p-value ≤ 10−6. • Apoptosis, cell cycle impairment and the alteration of protein folding and degradation through ubiquination are among the candidate pathophysiological mechanisms Adapted from: Potkin, Guffanti, et al. (2009) PLoS ONE 4(8): e6501

  21. Shen et al 2010: Overview QC’ed genotyping data FreeSurfer: 56 volume or cortical thickness measures R L L R 530,992 SNPs Baseline MRI Scans 142 QTs GWAS of Imaging Phenotypes R L R Strong associations represented by heat maps VBM: 86 GM density measures Refined modeling of candidate association GWAS of candidate QT VBM of candidate SNP

  22. Shen et al 2010: Findings • Whole genome, whole brain ROI analysis • As expected, SNPs in the APOE and TOMM40 genes were confirmed as markers strongly associated with multiple brain regions. • Other top SNPs were proximal to the EPHA4, TP63 and NXPH1 genes. • Refined analysis for a candidate SNP • rs6463843 (flanking NXPH1) was associated with reduced global and regional GM density across diagnostic groups in TT relative to GG homozygotes. • Interaction analysis indicated that AD patients homozygous for the T allele showed differential vulnerability to right hippocampal GM density loss. • NXPH1 codes for a protein implicated in promotion of adhesion between dendrites and axons, a key factor in synaptic integrity, the loss of which is a hallmark of AD. • A genome wide, whole brain search strategy has the potential to reveal novel candidate genes and loci warranting further investigation and replication.

  23. ADNI Genetics: UCLA, Thompson Lab Voxelwise GWAS: Ran genome-wide association for a quarter of a million points across 700 subjects - new gene discovery method; many new SNPs; power calculations for replication (Jason Stein et al, NeuroImage, in press) GRIN2b, a common glutamate receptor genetic variant, is associated with greater temporal lobe atrophy and with AD; NMDA-receptor is a target for memantine therapy (Jason Stein et al, NeuroImage, in press) FTO, an obesity risk gene carried by 46% of Europeans, is associated with 10-15% frontal and occipital atrophy, and with a ~1.7kg weight gain, on average (April Ho et al, PNAS, in revision) Stein et al, NeuroImage, in press

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