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The present and future role of computers in medicineIan FosterComputation InstituteArgonne National Lab & University of Chicago
CreditsThanks for support fromChan Soon-Shiong FoundationDepartment of EnergyNational Institutes of HealthNational Science FoundationAnd for many helpful conversations, Carl Kesselman, Jonathan Silverstein, Steve Tuecke, Stephan Erberich, Steve Graham, Ravi Madduri, and Patrick Soon-Shiong
Biology is shifting from being an observational science to a quantitative molecular science   Old biology: measure one/two things in two/three conditionsHigh cost per measurementAnalysis straightforward as little dataEnormously difficult to work out pathways due to inadequate data   New biology: measure 10,000 things under many conditionsLow cost per measurementAnalysis no longer straightforwardPayoff can be bigger: potential to understand a complex systemAjay Jain, UCSF
Change health care from an empirical, qualitative systemof silos of informationto a model ofpredictive, quantitative, shared,evidence-based outcomes
The health care information technology chasm Health care IT [is] rarely used to provide clinicians with evidence-based decision support and feedback; to support data-driven process improvement; or to link clinical care and research.Computational Technology for Effective Health Care, NRC, 2009
AAPM Foster July 2009
AAPM Foster July 2009
AAPM Foster July 2009
Digital power =computing x communicationxstorage x contentMoore’s lawdoubles every 18 months   disk lawdoublesx  every 12    months       fiber lawdoublesxevery9       monthscommunity lawnx    2    where n is    # peopleJohn SeelyBrown
(Intel)
Microprocessor trendsAMDDual core (April 2005)Quad core (October 2007)IntelDual core (July 2005)Quad core (December 2006)SunNiagara: 8 cores * 4 threads/core (November 2005)Niagara2: 8 cores * 8 threads/core (August 2007)IBM POWER62 cores * 4 threads/core (May 2007)Tilera 64 coresDan Reed, Microsoft11
Marching towards manycoreIntel’s 80 core prototype2-D mesh interconnect62 W powerTilera 64 core system8x8 grid of cores5 MB coherent cache4 DDR2 controllers2 10 GbE interfacesIBM CellPowerPC and 8 coresDan Reed, Microsoft12
1E+17multi-PetaflopPetaflopBlue Gene/L1E+14ThunderRed StormEarthBlue PacificASCI White, ASCI QSX-5ASCI Red OptionASCI RedT3ESX-4NWTCP-PACS1E+11CM-5ParagonT3DDeltaSX-3/44Doubling time = 1.5 yr.i860 (MPPs)VP2600/10SX-2CRAY-2Y-MP8S-810/20X-MP4Peak Speed (flops)Cyber 205X-MP2 (parallel vectors)1E+8CRAY-1CDC STAR-100 (vectors)CDC 7600ILLIAC IVCDC 6600 (ICs)IBM Stretch1E+5IBM 7090 (transistors)IBM 704IBM 701UNIVACENIAC (vacuum tubes)1E+219401950196019701980199020002010Year IntroducedThe evolution of the fastest supercomputerArgonneMy laptop
The Argonne IBM BG/P
www.top500.org123-4>128K
Simulation of the human arterial tree on the TeraGridG. Karniadakis et al.
AAPM Foster July 2009
Storage costs(PC Magazine, Oct 2, 2007)
Informationbig bangAll informationper year100ExabytesUniqueinformationper yearAll human documentsproduced last 40,000 years(to 1997)12 Exabytes    2000       2001     2002      2003      2004     2005     2006      2007     2008      2009© Stuart Card, basedon Lesk, Berkeley SIMS, Landauer, EMC
Growth of Genbank(1982-2005)Broad Institute
More data does not always mean more knowledgeFolker Meyer, Genome Sequencing vs. Moore’s Law: Cyber Challenges for the Next Decade, CTWatch, August 2006.
The Red Queen’s race  "Well, in our country," said Alice … "you'd generally get to somewhere else — if you run very fast for a long time, as we've been doing.”  "A slow sort of country!" said the Queen. "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!"
Computing ondemandPublic PUMA knowledge baseInformation about proteins analyzed against ~2 million gene sequencesBack officeanalysis on GridMillions of BLAST, BLOCKS, etc., onOSG and TeraGridNatalia Maltsev et al.
AAPM Foster July 2009
AAPM Foster July 2009
1.6 TbpsCost perGigabit-Mile320 GbpsMoore’sLaw2.5 Gbps50 Mbps1984199419982000199319982002Opticalnetworkingbreakthrough!RevolutionCapacity increase and new economicsNortel
Optical switchesLucent
EmpiricismTheorySimulationDataNew ways of knowing300 BCE170019501990Enhanced by the power of collaboration
AAPM Foster July 2009
Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomesPatients with same diagnosis
Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomesNon-responderstoxic respondersNon-toxic respondersPatients with same diagnosisMisdiagnosed
Leukemia and LymphomaAfter Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA
Leukemia and LymphomaAfter Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA
Currently, 17% of Burkitt's Lymphoma are incorrectly diagnosed as Diffuse Large B Cell LymphomaClassicBurkitt’sLymphomaAtypicalBurkitt’sLymphomaDiffuse LargeB Cell LymphomaLouis Staudt, National Cancer Institute
Classic Burkitt Lymphoma    Cure RateDiagnosisAtypical Burkitt Lymphoma Cure RateTreatmentPatients’ Actual DiseaseBurkitt’sLymphoma60 %80 %Intensive chemotherapyBurkitt’sLymphomaDiffuse Large B Cell Lymphoma0 %15 %CHOPregimenBurkitt’sLymphoma
Survival estimates for patients with Burkitt's LymphomaBest treatment for Burkitt’s LymphomaBest treatment for Diffuse Large B Cell LymphomaDave et al, NEJM, June 8, 2006.
Burkitt’sLymphomaDiffuse Large B-cell LymphomaClassic   AtypicalLouis Staudt, National Cancer Institute
Imaging biomarkers: Diffusion Tensor Imaging and brain injuryKraus et al., Brain (2007), 130, 2508-2519
Enabling quantitative medicineCollect a lot of patient dataAnalyze data to infer effective treatmentsIdentify personalized treatment plansClinical practiceBasic researchClinical trials
ChallengesIncreasing volumes of data, types of data: genomics, blood proteins, imaging, …New science and treatments are hidden in the data, not the biology (biomarkers)Too much for the individual physician or researcher to absorb … have to pay attention to cognitive support … computer-based tools and systems that offer clinicians and patients assistance for thinking about and solving problems related to specific instances of health care.NRC Report on Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions, 2009
Bridging silos to enable quantitative medicineBasic researchongoing investigative studiesOutcomes, tissue bankscreening testspathways libraryClinical practiceClinical trialstrial subjects, outcomes
Addressing urban health needs
Important characteristicsWe must integrate systems that may not have worked together beforeThese are human systems, with differing goals, incentives, capabilitiesAll components are dynamic—change is the norm, not the exceptionProcesses are evolving rapidly tooWe are not building something simple like a bridge or an airline reservation system
Healthcare is acomplex adaptive systemA complex adaptive system is a collection of individual agents that have the freedom to act in ways that are not always predictable and whose actions are interconnected such that one agent’s actions changes the context for other agents.Crossing the Quality Chasm, IOM, 2001; pp 312-13Non-linear and dynamic
Agents are independent and intelligent
Goals and behaviors often in conflict
Self-organization through adaptation and learning
No single point(s) of control
Hierarchical decomp-osition has limited valueWe need to function in the zone of complexityLowChaosZone ofcomplexityAgreementaboutoutcomesPlan and controlHighLowHighCertainty about outcomesRalph Stacey, Complexity and Creativity in Organizations, 1996
We need to function in the zone of complexityLowChaosAgreementaboutoutcomesPlan and controlHighLowHighCertainty about outcomesRalph Stacey, Complexity and Creativity in Organizations, 1996
We call these groupingsvirtual organizations (VOs)A set of individuals and/or institutions engaged in  the controlled sharing of resources in pursuit of a common goal      But U.S. health system is marked by fragmented  and inefficient VOs with insufficient mechanisms for controlled sharing    Healthcare = dynamic, overlapping VOs, linkingPatient – primary careSub-specialist – hospitalPharmacy – laboratoryInsurer – …   I advocate … a model of virtual integration rather than true vertical integration … G. Halvorson, CEO Kaiser
The Grid paradigmPrinciples and mechanisms for dynamic VOsLeverage service oriented architecture (SOA)Loose coupling of data and servicesOpen software,architectureEngineeringBiomedicineComputer sciencePhysicsHealthcareAstronomyBiology1995             2000            2005            2010
The Grid paradigm and healthcare information integration[Grid architecture joint work with Carl Kesselman,   Steve Tuecke, Stephan Erberich, and others] Manage who can do whatMake data usable and usefulPlatform servicesName data and move it aroundMake data accessible over the networkData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
The Grid paradigm and healthcare information integrationEnhance user cognitive processesSecurity and policyIncorporate into business processesTransform data into knowledgeIntegrationPlatform servicesManagementPublicationData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
The Grid paradigm and healthcare information integrationCognitive supportSecurity and policyValueservicesApplicationsAnalysisIntegrationPlatform servicesManagementPublicationData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
We partition the multi-faceted interoperability problemProcess interoperabilityIntegrate work across healthcare enterpriseData interoperabilitySyntactic: move structured data among system elementsSemantic: use information across system elementsSystems interoperabilityCommunicate securely, reliably among system elementsApplicationsAnalysisIntegrationManagementPublication
Publication:Make information accessibleMake data available in a remotely accessible, reusable mannerLeave mediation for integration layerGateway from local policy/protocol into wide area mechanisms (transport, security, …)
Imaging clinical trialsNeuroblastomaCancerFoundationChildrens Oncology Group
Stephan Erberich,Carl Kesselman, et al.
As of Oct19, 2008:122 participants105 services70 data35 analytical
Data movement in clinical trials(Center for Health Informatics)
Community public health:Digital retinopathy screening network(Center for Health Informatics)
Integration:Making data usable and useful?Adaptive approach100%Degree of communicationLoosely coupled approachRigid standards-based approach0%0%                          100%  Degree of prior syntactic  and semantic agreement
Integration via mediationMap between models
Scoped to domain use
Multiple concurrent use
Bottom up mediation
between standards and versions

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AAPM Foster July 2009

  • 1. The present and future role of computers in medicineIan FosterComputation InstituteArgonne National Lab & University of Chicago
  • 2. CreditsThanks for support fromChan Soon-Shiong FoundationDepartment of EnergyNational Institutes of HealthNational Science FoundationAnd for many helpful conversations, Carl Kesselman, Jonathan Silverstein, Steve Tuecke, Stephan Erberich, Steve Graham, Ravi Madduri, and Patrick Soon-Shiong
  • 3. Biology is shifting from being an observational science to a quantitative molecular science Old biology: measure one/two things in two/three conditionsHigh cost per measurementAnalysis straightforward as little dataEnormously difficult to work out pathways due to inadequate data New biology: measure 10,000 things under many conditionsLow cost per measurementAnalysis no longer straightforwardPayoff can be bigger: potential to understand a complex systemAjay Jain, UCSF
  • 4. Change health care from an empirical, qualitative systemof silos of informationto a model ofpredictive, quantitative, shared,evidence-based outcomes
  • 5. The health care information technology chasm Health care IT [is] rarely used to provide clinicians with evidence-based decision support and feedback; to support data-driven process improvement; or to link clinical care and research.Computational Technology for Effective Health Care, NRC, 2009
  • 9. Digital power =computing x communicationxstorage x contentMoore’s lawdoubles every 18 months disk lawdoublesx every 12 months fiber lawdoublesxevery9 monthscommunity lawnx 2 where n is # peopleJohn SeelyBrown
  • 11. Microprocessor trendsAMDDual core (April 2005)Quad core (October 2007)IntelDual core (July 2005)Quad core (December 2006)SunNiagara: 8 cores * 4 threads/core (November 2005)Niagara2: 8 cores * 8 threads/core (August 2007)IBM POWER62 cores * 4 threads/core (May 2007)Tilera 64 coresDan Reed, Microsoft11
  • 12. Marching towards manycoreIntel’s 80 core prototype2-D mesh interconnect62 W powerTilera 64 core system8x8 grid of cores5 MB coherent cache4 DDR2 controllers2 10 GbE interfacesIBM CellPowerPC and 8 coresDan Reed, Microsoft12
  • 13. 1E+17multi-PetaflopPetaflopBlue Gene/L1E+14ThunderRed StormEarthBlue PacificASCI White, ASCI QSX-5ASCI Red OptionASCI RedT3ESX-4NWTCP-PACS1E+11CM-5ParagonT3DDeltaSX-3/44Doubling time = 1.5 yr.i860 (MPPs)VP2600/10SX-2CRAY-2Y-MP8S-810/20X-MP4Peak Speed (flops)Cyber 205X-MP2 (parallel vectors)1E+8CRAY-1CDC STAR-100 (vectors)CDC 7600ILLIAC IVCDC 6600 (ICs)IBM Stretch1E+5IBM 7090 (transistors)IBM 704IBM 701UNIVACENIAC (vacuum tubes)1E+219401950196019701980199020002010Year IntroducedThe evolution of the fastest supercomputerArgonneMy laptop
  • 16. Simulation of the human arterial tree on the TeraGridG. Karniadakis et al.
  • 19. Informationbig bangAll informationper year100ExabytesUniqueinformationper yearAll human documentsproduced last 40,000 years(to 1997)12 Exabytes 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009© Stuart Card, basedon Lesk, Berkeley SIMS, Landauer, EMC
  • 21. More data does not always mean more knowledgeFolker Meyer, Genome Sequencing vs. Moore’s Law: Cyber Challenges for the Next Decade, CTWatch, August 2006.
  • 22. The Red Queen’s race "Well, in our country," said Alice … "you'd generally get to somewhere else — if you run very fast for a long time, as we've been doing.” "A slow sort of country!" said the Queen. "Now, here, you see, it takes all the running you can do, to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!"
  • 23. Computing ondemandPublic PUMA knowledge baseInformation about proteins analyzed against ~2 million gene sequencesBack officeanalysis on GridMillions of BLAST, BLOCKS, etc., onOSG and TeraGridNatalia Maltsev et al.
  • 26. 1.6 TbpsCost perGigabit-Mile320 GbpsMoore’sLaw2.5 Gbps50 Mbps1984199419982000199319982002Opticalnetworkingbreakthrough!RevolutionCapacity increase and new economicsNortel
  • 28. EmpiricismTheorySimulationDataNew ways of knowing300 BCE170019501990Enhanced by the power of collaboration
  • 30. Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomesPatients with same diagnosis
  • 31. Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomesNon-responderstoxic respondersNon-toxic respondersPatients with same diagnosisMisdiagnosed
  • 32. Leukemia and LymphomaAfter Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA
  • 33. Leukemia and LymphomaAfter Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA
  • 34. Currently, 17% of Burkitt's Lymphoma are incorrectly diagnosed as Diffuse Large B Cell LymphomaClassicBurkitt’sLymphomaAtypicalBurkitt’sLymphomaDiffuse LargeB Cell LymphomaLouis Staudt, National Cancer Institute
  • 35. Classic Burkitt Lymphoma Cure RateDiagnosisAtypical Burkitt Lymphoma Cure RateTreatmentPatients’ Actual DiseaseBurkitt’sLymphoma60 %80 %Intensive chemotherapyBurkitt’sLymphomaDiffuse Large B Cell Lymphoma0 %15 %CHOPregimenBurkitt’sLymphoma
  • 36. Survival estimates for patients with Burkitt's LymphomaBest treatment for Burkitt’s LymphomaBest treatment for Diffuse Large B Cell LymphomaDave et al, NEJM, June 8, 2006.
  • 37. Burkitt’sLymphomaDiffuse Large B-cell LymphomaClassic AtypicalLouis Staudt, National Cancer Institute
  • 38. Imaging biomarkers: Diffusion Tensor Imaging and brain injuryKraus et al., Brain (2007), 130, 2508-2519
  • 39. Enabling quantitative medicineCollect a lot of patient dataAnalyze data to infer effective treatmentsIdentify personalized treatment plansClinical practiceBasic researchClinical trials
  • 40. ChallengesIncreasing volumes of data, types of data: genomics, blood proteins, imaging, …New science and treatments are hidden in the data, not the biology (biomarkers)Too much for the individual physician or researcher to absorb … have to pay attention to cognitive support … computer-based tools and systems that offer clinicians and patients assistance for thinking about and solving problems related to specific instances of health care.NRC Report on Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions, 2009
  • 41. Bridging silos to enable quantitative medicineBasic researchongoing investigative studiesOutcomes, tissue bankscreening testspathways libraryClinical practiceClinical trialstrial subjects, outcomes
  • 43. Important characteristicsWe must integrate systems that may not have worked together beforeThese are human systems, with differing goals, incentives, capabilitiesAll components are dynamic—change is the norm, not the exceptionProcesses are evolving rapidly tooWe are not building something simple like a bridge or an airline reservation system
  • 44. Healthcare is acomplex adaptive systemA complex adaptive system is a collection of individual agents that have the freedom to act in ways that are not always predictable and whose actions are interconnected such that one agent’s actions changes the context for other agents.Crossing the Quality Chasm, IOM, 2001; pp 312-13Non-linear and dynamic
  • 45. Agents are independent and intelligent
  • 46. Goals and behaviors often in conflict
  • 48. No single point(s) of control
  • 49. Hierarchical decomp-osition has limited valueWe need to function in the zone of complexityLowChaosZone ofcomplexityAgreementaboutoutcomesPlan and controlHighLowHighCertainty about outcomesRalph Stacey, Complexity and Creativity in Organizations, 1996
  • 50. We need to function in the zone of complexityLowChaosAgreementaboutoutcomesPlan and controlHighLowHighCertainty about outcomesRalph Stacey, Complexity and Creativity in Organizations, 1996
  • 51. We call these groupingsvirtual organizations (VOs)A set of individuals and/or institutions engaged in the controlled sharing of resources in pursuit of a common goal But U.S. health system is marked by fragmented and inefficient VOs with insufficient mechanisms for controlled sharing Healthcare = dynamic, overlapping VOs, linkingPatient – primary careSub-specialist – hospitalPharmacy – laboratoryInsurer – … I advocate … a model of virtual integration rather than true vertical integration … G. Halvorson, CEO Kaiser
  • 52. The Grid paradigmPrinciples and mechanisms for dynamic VOsLeverage service oriented architecture (SOA)Loose coupling of data and servicesOpen software,architectureEngineeringBiomedicineComputer sciencePhysicsHealthcareAstronomyBiology1995 2000 2005 2010
  • 53. The Grid paradigm and healthcare information integration[Grid architecture joint work with Carl Kesselman, Steve Tuecke, Stephan Erberich, and others] Manage who can do whatMake data usable and usefulPlatform servicesName data and move it aroundMake data accessible over the networkData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
  • 54. The Grid paradigm and healthcare information integrationEnhance user cognitive processesSecurity and policyIncorporate into business processesTransform data into knowledgeIntegrationPlatform servicesManagementPublicationData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
  • 55. The Grid paradigm and healthcare information integrationCognitive supportSecurity and policyValueservicesApplicationsAnalysisIntegrationPlatform servicesManagementPublicationData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
  • 56. We partition the multi-faceted interoperability problemProcess interoperabilityIntegrate work across healthcare enterpriseData interoperabilitySyntactic: move structured data among system elementsSemantic: use information across system elementsSystems interoperabilityCommunicate securely, reliably among system elementsApplicationsAnalysisIntegrationManagementPublication
  • 57. Publication:Make information accessibleMake data available in a remotely accessible, reusable mannerLeave mediation for integration layerGateway from local policy/protocol into wide area mechanisms (transport, security, …)
  • 60. As of Oct19, 2008:122 participants105 services70 data35 analytical
  • 61. Data movement in clinical trials(Center for Health Informatics)
  • 62. Community public health:Digital retinopathy screening network(Center for Health Informatics)
  • 63. Integration:Making data usable and useful?Adaptive approach100%Degree of communicationLoosely coupled approachRigid standards-based approach0%0% 100% Degree of prior syntactic and semantic agreement
  • 70. in absence of agreementGlobal Data ModelQuery reformulationQuery in union of exported source schemaQuery optimizationDistributed query executionQuery execution engineWrapperWrapperQuery in the sourceschemaAlon Halevy, 2000
  • 71. Analytics:Transform data into knowledge “The overwhelming success of genetic and genomic research efforts has created an enormous backlog of data with the potential to improve the quality of patient care and cost effectiveness of treatment.”— US Presidential Council of Advisors on Science and Technology, Personalized Medicine Themes, 2008
  • 73. Query and retrieve microarray data from a caArray data service:cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/CaArrayScrubNormalize microarray data using GenePattern analytical servicenode255.broad.mit.edu:6060/wsrf/services/cagrid/PreprocessDatasetMAGEServiceHierarchical clustering using geWorkbench analytical service: cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/HierarchicalClusteringMageMicroarray clustering using TavernaWorkflow in/outputcaGrid services“Shim” servicesothersWei Tan et al.
  • 74. Many many tasks:Identifying potential drug targets 2M+ ligands Protein xtarget(s) Benoit Roux et al.
  • 75. 6 GB2M structures(6 GB)~4M x 60s x 1 cpu~60K cpu-hrsFREDDOCK6Select best ~5KSelect best ~5K~10K x 20m x 1 cpu~3K cpu-hrsAmberSelect best ~500~500 x 10hr x 100 cpu~500K cpu-hrsGCMCZINC3-DstructuresManually prepDOCK6 rec fileManually prepFRED rec fileNAB scriptparameters(defines flexibleresidues, #MDsteps)NABScriptTemplateDOCK6Receptor(1 per protein:defines pocketto bind to)FREDReceptor(1 per protein:defines pocketto bind to)PDBproteindescriptions1 protein(1MB)BuildNABScriptAmber prep:2. AmberizeReceptor4. perl: gen nabscriptNABScriptstartAmber Score:1. AmberizeLigand3. AmberizeComplex5. RunNABScriptFor 1 target:4 million tasks500,000 cpu-hrs(50 cpu-years)endreportligandscomplexes
  • 76. DOCK on BG/P: ~1M tasks on 118,000 CPUsCPU cores: 118784Tasks: 934803Elapsed time: 7257 secCompute time: 21.43 CPU yearsAverage task time: 667 secRelative Efficiency: 99.7% (from 16 to 32 racks)Utilization: Sustained: 99.6%Overall: 78.3%Time (secs)Ioan Raicu et al.
  • 77. The health care information technology chasm Health care IT [is] rarely used to provide clinicians with evidence-based decision support and feedback; to support data-driven process improvement; or to link clinical care and research.Computational Technology for Effective Health Care, NRC, 2009
  • 78. Six research challenges for information technology and healthcarePatient-centered cognitive supportModeling—an individualized virtual patientAutomation—integrated use, adaptivityData sharing and collaborationData management at scaleAutomated full capture of physician-patient interactionsComputational Technology for Effective Health Care, NRC, 2009
  • 79. Six research challenges for information technology and healthcarePatient-centered cognitive supportModeling—an individualized virtual patientAutomation—integrated use, adaptivityData sharing and collaborationData management at scaleAutomated full capture of physician-patient interactionsComputational Technology for Effective Health Care, NRC, 2009
  • 80. Functioning in the zone of complexityLowChaosAgreementaboutoutcomesPlan and controlHighLowHighCertainty about outcomesRalph Stacey, Complexity and Creativity in Organizations, 1996
  • 81. The Grid paradigm and healthcare information integrationCognitive supportSecurity and policyValueservicesApplicationsAnalysisIntegrationPlatform servicesManagementPublicationData sourcesRadiologyMedical recordsPathologyGenomicsLabsRHIO
  • 82. “People tend to overestimate the short-term impact of change, and underestimate the long-term impact.” — Roy Amara “The computer revolution hasn’t happened yet.” — Alan Kay, 1997

Editor's Notes

  1. Medicine is approaching a profound transition as the methods of molecular medicine start to transform the nature of health care.What is the significance of such methods? For the researcher, it is a paradigm shift, as the number of things that can be measured increases dramatically.
  2. Researchers express a vision for a scientific revolution in health care, from the qualitative to the quantitative-- A revolution based on information and thus computing
  3. However, even as we talk about transformation and revolution, we must recognize that computing is poorly used in health care today.These are the words of a recent National Research Council report.Thus, I will seek in my remarks today to shed light on three questions: how information technology is evolving, how this evolution may impact medicine, and how changes in medicine and health care will stress information techology.
  4. The story of computers is one of exponentials
  5. The story of computers is one of exponentials
  6. The story of computers is one of exponentials
  7. But things are not quite as bad as that
  8. What does this mean for medicine?We will certainly continue to see increasingly sophisticated computer applications aiding the physician in their tasks of observing, diagnosing, and treating – what used to be solely the domain the human senses, the brain, and the hands.More accurate, higher resolution, and more automated data acquisition systems.Computer-aided diagnosis and treatment planning systems that use large-scale data analysis and computer simulations.Automated radiation treatment and surgery systems. However, I want to focus here on some larger systems issues relating to quantitative medicine.
  9. Using gene expression microarrays, we find that these two diseases have quite different phenotypes—that quite different genes are expressed in the two conditions.Here, columns are patients; rows are genes.Not sure what is the significance of the Stage 1/Stage 2.”The beauty of gene expression profiling data is that it is quantitative and highly reproducible. Because of this, these data can be used to generate multivariate statistical models of the clinical behavior of cancer that have great predictive power.” -- http://lymphochip.nih.gov/Staudt_Adv_Immunol_2005.pdf
  10. And of course, we must not forget image-based biomarkers, as used in computer aided diagnosis of breast cancer, or as shown here, in an attempt to identify biomarkers for traumatic brain injury.ROIs used in a study at UIC(A) forceps minor (green), cortico-spinal tract (purple), inferior frontal-occipital fasciculus (red), external capsule (yellow), sagittal stratum (blue) (B) anterior corona radiata (green), superior longitudinal fasciculus (red), posterior corona radiata (blue); (C) cingulum (red), corpus callosum body (blue), splenium (yellow), and genu (green), and forceps major (purple).
  11. Then, by tracking the personalized treatment plan, we collect more patient data.Success demands that we integrate, to a far greater degree than previously possible, clinical practice, basic research, and clinical trials. A profound challenge for health care system and for information technology.
  12. Collecting and managing the enormous quantities of data that are now feasible, and required for EBM, is a huge challenge.However, merely putting in place the systems required to collect large quantities of data is not enough.We then need to make sense of that data. A challenge both for the physician and the researcher.
  13. These problems arise at multiple scales. E.g. …
  14. What these (and other examples that we will not have time to review) have in common …
  15. We cite [Rouse, Health Care as a CAS: Implications for Design… , NAE 2008] for the righthand side aprt.Must supportDynamic composition for a specific purposeEvolving community, function, environmentMessy data, failure, incomplete knowledgeNice, but insufficientData standardsPlatform standardsFederal policies
  16. Another perspective on the problem. A few words of explanation. If we are deploying a hospital IT system, we are (hopefully) in the bottom left hand corner.“You can’t achieve success via central planning.” Quoted in Crossing the Quality Chasm, p. 312In our scenarios, we don’t have that ability to control.
  17. What is the alternative? We can put in place mechanisms that facilitate groups with some common goal to form and function.Over time, things change, these groups evolve.If we are successful, they can expand, perhaps merge.Challenges: make this easy. Leverage scale effects.
  18. These are issues that the grid community has been working on for many years. We call these groupings Virtual Organizations.In healthcare today, there are of course many such “VOs.”But they are hard to form, fragmented, …
  19. Principles and mechanisms that has been under development for some years.First CS, then physical sciences, then biology, most recently biomedicine –
  20. What are these grid mechanisms and concepts, then? Hard to say something sensible in a few minutes.But basically it is about separating out concerns in a way that reduces barriers to entry and permits flexible use.
  21. API vs. protocol? “Illities”?
  22. [Create an image here.]For example DICOM and HL7 combine messaging and data model in the same interoperability standard. People are contextualizing this problem at the data interoperability level.  Systems interoperability often neglected.  An area of differentiation, bringing in best practice in industry and science into health care space. Open source platform.  Experience with systems interoperability standards: IETF, OASIS, W3C, 
  23. Scaling via automating data adaptersRepresentations of those things and semantics of those representations.Talk about how services are published, data modeling, etc.Publish data basesPublish servicesName published objects
  24. Loose coupling and encapsulationInteroperability through integration based on data mediation Evolutionary in nature Set of scalable systems and methods Explicit in architecture – data integration layerDemonstrated in GSI, GridFTP, MDS, ECOG
  25. Most images are never seen—and are not available—outside their originating institution
  26. Recap …
  27. 6 representative challenges
  28. Data sharing is the most important