I gave this talk in the "Presidential Symposium" at the annual meeting of the American Association of Physicists in Medicine, in Annaheim, California. The President of AAPM, Dr. Maryellen Giger, wanted some people to give some visionary talks. She invited (I kid you not) Foster, Gates, and Obama. Fortunately Bill and Barack had other commitments, so I did not need to share the time with them.

1. The present and future role of computers in medicine Ian Foster Computation Institute Argonne National Lab & University of Chicago

2. Credits Thanks for support from Chan Soon-Shiong Foundation Department of Energy National Institutes of Health National Science Foundation And 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 conditions High cost per measurement Analysis straightforward as little data Enormously difficult to work out pathways due to inadequate data New biology: measure 10,000 things under many conditions Low cost per measurement Analysis no longer straightforward Payoff can be bigger: potential to understand a complex system Ajay Jain, UCSF

4. Change health care from an empirical, qualitative systemof silos of information to a model of predictive, 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 content Moore’s law doubles every 18 months disk law doubles x every 12 months fiber law doubles xevery9 months community law n x 2 where n is # people John SeelyBrown

10. (Intel)

11. Microprocessor trends AMD Dual core (April 2005) Quad core (October 2007) Intel Dual core (July 2005) Quad core (December 2006) Sun Niagara: 8 cores * 4 threads/core (November 2005) Niagara2: 8 cores * 8 threads/core (August 2007) IBM POWER6 2 cores * 4 threads/core (May 2007) Tilera 64 cores Dan Reed, Microsoft 11

12. Marching towards manycore Intel’s 80 core prototype 2-D mesh interconnect 62 W power Tilera 64 core system 8x8 grid of cores 5 MB coherent cache 4 DDR2 controllers 2 10 GbE interfaces IBM Cell PowerPC and 8 cores Dan Reed, Microsoft 12

13. 1E+17 multi-Petaflop Petaflop Blue Gene/L 1E+14 Thunder Red Storm Earth Blue Pacific ASCI White, ASCI Q SX-5 ASCI Red Option ASCI Red T3E SX-4 NWT CP-PACS 1E+11 CM-5 Paragon T3D Delta SX-3/44 Doubling time = 1.5 yr. i860 (MPPs) VP2600/10 SX-2 CRAY-2 Y-MP8 S-810/20 X-MP4 Peak Speed (flops) Cyber 205 X-MP2 (parallel vectors) 1E+8 CRAY-1 CDC STAR-100 (vectors) CDC 7600 ILLIAC IV CDC 6600 (ICs) IBM Stretch 1E+5 IBM 7090 (transistors) IBM 704 IBM 701 UNIVAC ENIAC (vacuum tubes) 1E+2 1940 1950 1960 1970 1980 1990 2000 2010 Year Introduced The evolution of the fastest supercomputer Argonne My laptop

14. The Argonne IBM BG/P

15. www.top500.org 1 2 3-4 >128K

16. Simulation of the human arterial tree on the TeraGrid G. Karniadakis et al.

18. Storage costs (PC Magazine, Oct 2, 2007)

19. Informationbig bang All informationper year 100Exabytes Uniqueinformationper year All 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

20. Growth of Genbank(1982-2005) Broad Institute

21. More data does not always mean more knowledge Folker 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 ondemand Public PUMA knowledge base Information about proteins analyzed against ~2 million gene sequences Back officeanalysis on Grid Millions of BLAST, BLOCKS, etc., onOSG and TeraGrid Natalia Maltsev et al.

26. 1.6 Tbps Cost perGigabit-Mile 320 Gbps Moore’sLaw 2.5 Gbps 50 Mbps 1984 1994 1998 2000 1993 1998 2002 Opticalnetworkingbreakthrough! Revolution Capacity increase and new economics Nortel

27. Optical switches Lucent

28. Empiricism Theory Simulation Data New ways of knowing 300 BCE 1700 1950 1990 Enhanced by the power of collaboration

30. Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes Patients with same diagnosis

31. Quantitative medicine is the key to reducing healthcare costs and improving healthcare outcomes Non-responderstoxic responders Non-toxic responders Patients with same diagnosis Misdiagnosed

32. Leukemia and Lymphoma After Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA

33. Leukemia and Lymphoma After Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA

34. Currently, 17% of Burkitt's Lymphoma are incorrectly diagnosed as Diffuse Large B Cell Lymphoma Classic Burkitt’sLymphoma Atypical Burkitt’sLymphoma Diffuse Large B Cell Lymphoma Louis Staudt, National Cancer Institute

35. Classic Burkitt Lymphoma Cure Rate Diagnosis Atypical Burkitt Lymphoma Cure Rate Treatment Patients’ Actual Disease Burkitt’sLymphoma 60 % 80 % Intensive chemotherapy Burkitt’sLymphoma Diffuse Large B Cell Lymphoma 0 % 15 % CHOPregimen Burkitt’sLymphoma

36. Survival estimates for patients with Burkitt's Lymphoma Best treatment for Burkitt’s Lymphoma Best treatment for Diffuse Large B Cell Lymphoma Dave et al, NEJM, June 8, 2006.

37. Burkitt’s Lymphoma Diffuse Large B-cell Lymphoma Classic Atypical Louis Staudt, National Cancer Institute

38. Imaging biomarkers: Diffusion Tensor Imaging and brain injury Kraus et al., Brain (2007), 130, 2508-2519

39. Enabling quantitative medicine Collect a lot of patient data Analyze data to infer effective treatments Identify personalized treatment plans Clinical practice Basic research Clinical trials

40. Challenges Increasing 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 medicine Basic research ongoing investigative studies Outcomes, tissue bank screening tests pathways library Clinical practice Clinical trials trial subjects, outcomes

42. Addressing urban health needs

43. Important characteristics We must integrate systems that may not have worked together before These are human systems, with differing goals, incentives, capabilities All components are dynamic—change is the norm, not the exception Processes are evolving rapidly too We are not building something simple like a bridge or an airline reservation system

45. Agents are independent and intelligent

46. Goals and behaviors often in conflict

47. Self-organization through adaptation and learning

48. No single point(s) of control

50. We need to function in the zone of complexity Low Chaos Agreement about outcomes Plan and control High Low High Certainty about outcomes Ralph 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, linking Patient – primary care Sub-specialist – hospital Pharmacy – laboratory Insurer – … I advocate … a model of virtual integration rather than true vertical integration … G. Halvorson, CEO Kaiser

52. The Grid paradigm Principles and mechanisms for dynamic VOs Leverage service oriented architecture (SOA) Loose coupling of data and services Open software,architecture Engineering Biomedicine Computer science Physics Healthcare Astronomy Biology 1995 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 what Make data usable and useful Platform services Name data and move it around Make data accessible over the network Data sources Radiology Medical records Pathology Genomics Labs RHIO

54. The Grid paradigm and healthcare information integration Enhance user cognitive processes Security and policy Incorporate into business processes Transform data into knowledge Integration Platform services Management Publication Data sources Radiology Medical records Pathology Genomics Labs RHIO

55. The Grid paradigm and healthcare information integration Cognitive support Security and policy Valueservices Applications Analysis Integration Platform services Management Publication Data sources Radiology Medical records Pathology Genomics Labs RHIO

56. We partition the multi-faceted interoperability problem Process interoperability Integrate work across healthcare enterprise Data interoperability Syntactic: move structured data among system elements Semantic: use information across system elements Systems interoperability Communicate securely, reliably among system elements Applications Analysis Integration Management Publication

57. Publication:Make information accessible Make data available in a remotely accessible, reusable manner Leave mediation for integration layer Gateway from local policy/protocol into wide area mechanisms (transport, security, …)

58. Imaging clinical trials NeuroblastomaCancerFoundation Childrens Oncology Group

59. Stephan Erberich, Carl Kesselman, et al.

60. As of Oct19, 2008: 122 participants 105 services 70 data 35 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 approach 100% Degree of communication Loosely coupled approach Rigid standards-based approach 0% 0% 100% Degree of prior syntactic and semantic agreement

65. Scoped to domain use

66. Multiple concurrent use

67. Bottom up mediation

68. between standards and versions

69. between local versions

70. in absence of agreementGlobal Data Model Query reformulation Query in union of exported source schema Query optimization Distributed query execution Query execution engine Wrapper Wrapper Query in the sourceschema Alon 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

72. Published The imagepyramid Enrolled/ evaluated Eligible patients Created Michael Vannier

73. Query and retrieve microarray data from a caArray data service:cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/CaArrayScrub Normalize microarray data using GenePattern analytical servicenode255.broad.mit.edu:6060/wsrf/services/cagrid/PreprocessDatasetMAGEService Hierarchical clustering using geWorkbench analytical service: cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/HierarchicalClusteringMage Microarray clustering using Taverna Workflow in/output caGrid services “Shim” services others Wei Tan et al.

74. Many many tasks:Identifying potential drug targets 2M+ ligands Protein xtarget(s) Benoit Roux et al.

75. 6 GB 2M structures (6 GB) ~4M x 60s x 1 cpu ~60K cpu-hrs FRED DOCK6 Select best ~5K Select best ~5K ~10K x 20m x 1 cpu ~3K cpu-hrs Amber Select best ~500 ~500 x 10hr x 100 cpu ~500K cpu-hrs GCMC ZINC 3-D structures Manually prep DOCK6 rec file Manually prep FRED rec file NAB scriptparameters (defines flexible residues, #MDsteps) NAB Script Template DOCK6 Receptor (1 per protein: defines pocket to bind to) FRED Receptor (1 per protein: defines pocket to bind to) PDB protein descriptions 1 protein (1MB) BuildNABScript Amber prep: 2. AmberizeReceptor 4. perl: gen nabscript NAB Script start Amber Score: 1. AmberizeLigand 3. AmberizeComplex 5. RunNABScript For 1 target: 4 million tasks500,000 cpu-hrs (50 cpu-years) end report ligands complexes

76. DOCK on BG/P: ~1M tasks on 118,000 CPUs CPU cores: 118784 Tasks: 934803 Elapsed time: 7257 sec Compute time: 21.43 CPU years Average task time: 667 sec Relative 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 healthcare Patient-centered cognitive support Modeling—an individualized virtual patient Automation—integrated use, adaptivity Data sharing and collaboration Data management at scale Automated full capture of physician-patient interactions Computational Technology for Effective Health Care, NRC, 2009

79. Six research challenges for information technology and healthcare Patient-centered cognitive support Modeling—an individualized virtual patient Automation—integrated use, adaptivity Data sharing and collaboration Data management at scale Automated full capture of physician-patient interactions Computational Technology for Effective Health Care, NRC, 2009

80. Functioning in the zone of complexity Low Chaos Agreement about outcomes Plan and control High Low High Certainty about outcomes Ralph Stacey, Complexity and Creativity in Organizations, 1996

81. The Grid paradigm and healthcare information integration Cognitive support Security and policy Valueservices Applications Analysis Integration Platform services Management Publication Data sources Radiology Medical records Pathology Genomics Labs RHIO

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

83. Thank you! Computation Institutewww.ci.uchicago.edu