aapm foster july 2009

Ian Foster

Computation Institute

Argonne National Lab & University of Chicago

The present and future role of computers in medicine

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

3Biology 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 system of 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 communication x storage x content

Moore’s law

doubles every 18 months

John Seely Brown

community law

nx 2 where n is # people

disk law

doublesx every 12 months

fiber law

doublesx every 9 months

10

(Intel)

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

12Dan Reed, Microsoft

13

1940 1950 1960 1970 1980 1990 2000 2010

Year Introduced

1E+2

1E+5

1E+8

1E+11

1E+14

1E+17

Pe

ak

Sp

ee

d (flo

ps

)

Doubling time = 1.5 yr.

ENIAC (vacuum tubes)UNIVAC

IBM 701IBM 704

IBM 7090 (transistors)

IBM Stretch

CDC 6600 (ICs)

CDC 7600

CDC STAR-100 (vectors) CRAY-1Cyber 205 X-MP2 (parallel vectors)

CRAY-2X-MP4 Y-MP8

i860 (MPPs)

ASCI White, ASCI Q

Petaflop

Blue Gene/L

Blue Pacific

DeltaCM-5 Paragon

NWT

ASCI Red OptionASCI Red

CP-PACS

Earth

VP2600/10SX-3/44

Red Storm

ILLIAC IV

SX-2

SX-4

SX-5

S-810/20

T3D

T3E

multi-Petaflop

Thunder

The evolution of the fastest supercomputer

Argonne

My laptop

14

The Argonne IBM BG/P

15

www.top500.org

1

3-42

>128K

16

G. Karniadakis et al.

Simulation of the human

arterial tree on the TeraGrid

18Storage costs

(PC Magazine, Oct 2, 2007)

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!"

23Computing on demand

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

1993 1998 20021984 1994 1998 2000

Cost perGigabit-

Mile

Capacity increase and new economics

Optical networking breakthrough!

50 Mbps2.5 Gbps

1.6 Tbps

320 Gbps

Moore’sLaw

Revolution

Nortel

27

Optical switches

Lucent

28

New ways of knowing

300 BCE 1700 1950 1990

Empiricism

Data

Theory

Simulation

Enhanced by the power of collaboration

30Quantitative medicine is the key to reducing healthcare costs and

improving healthcare outcomes

Patients with same diagnosis

31Quantitative medicine is the key to reducing healthcare costs and

improving healthcare outcomes

Patients with same diagnosis

Misdiagnosed

Non-responderstoxic responders

Non-toxic responders

32

Leukemia and Lymphoma

After Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA

33

Leukemia and Lymphoma

After Mara Aspinall, GenzymeGenetics; Felix W. Frueh, FDA

34Currently, 17% of Burkitt's Lymphoma are incorrectly diagnosed as

Diffuse Large B Cell Lymphoma

ClassicBurkitt’s Lymphoma

AtypicalBurkitt’s Lymphoma

Diffuse LargeB Cell Lymphoma

Louis Staudt, National Cancer Institute

36

Dave et al, NEJM, June 8, 2006.

Survival estimates for patients with Burkitt's Lymphoma

Best treatment for Diffuse Large B Cell

Lymphoma

Best treatment for Burkitt’s Lymphoma

37Burkitt’s

LymphomaDiffuse Large

B-cell Lymphoma

Louis Staudt, National Cancer Institute

Classic Atypical

38Imaging 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

41Bridging silos to enable quantitative medicine

Basic research

Clinical practice

Clinical trials

trial subjects, outcomes

library

Outco

mes

,

tissu

e ba

nksc

reen

ing

test

s

ongoing

investigative

studies

pathways

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

44

Healthcare is acomplex adaptive system

A 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 312-13

Non-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 value

45

Low

LowHigh

High

Agreementabout

outcomes

Certainty about outcomes

We need to function in the zone of complexity

Plan and

control

Chaos

Zone of

complexity

Ralph Stacey, Complexity and Creativity in Organizations, 1996

46

Low

LowHigh

High

Agreementabout

outcomes


We need to function in the zone of complexity

Plan and

control

Chaos


47We call these groupingsvirtual organizations (VOs)

Healthcare = dynamic, overlapping VOs, linking Patient – primary care Sub-specialist – hospital Pharmacy – laboratory Insurer – …

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

I advocate … a model of virtual integration rather than true vertical integration … G. Halvorson, CEO Kaiser

48

The Grid paradigm

1995 2000 2005 2010

Principles and mechanisms for dynamic VOs Leverage service oriented architecture (SOA) Loose coupling of

data and services Open software,

architecture

Computer science

Physics

Astronomy

Engineering

Biology

Biomedicine

Healthcare

49

The Grid paradigm and healthcare information integration

Radiology Medical records

Name data and move it around

Make data usable and useful

Make data accessible over the network

Pathology Genomics Labs

Man

ag

e w

ho ca

n d

o w

hat

RHIOData

sources

Platform services

[Grid architecture joint work with Carl Kesselman, Steve Tuecke, Stephan Erberich, and others]

50


Transform data into knowledge


Management

Integration

Publication

Enhance user cognitive processes

Incorporate into business processes


Secu

rity a

nd

policy

RHIOData

sources

Platform services

51


Analysis


Management

Integration

Publication

Cognitive support

Applications


Secu

rity a

nd

policy

RHIOData

sources

Platform services

Value services

52

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

Analysis

Management

Integration

Publication

Applications

53

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, …)

54

Childrens Oncology Group

Neuroblastoma Cancer

Foundation

Imaging clinical trials

55

NANTCOG

Stephan Erberich,Carl Kesselman, et al.

56

As of Oct19, 2008:

122 participants105 services

70 data35 analytical

57

Data movement in clinical trials

(Center for Health Informatics)

58Community public health:Digital retinopathy screening network

(Center for Health Informatics)

59

Integration:Making data usable and useful

?

0% 100% Degree of prior syntactic and semantic agreement

Degree of communication

0%

100%

Rigid standards-based approach

Loosely coupled approach

Adaptive approach

60

Integration via mediation

Map between models Scoped to domain use

Multiple concurrent use

Bottom up mediation between standards and

versions between local versions in absence of agreement

Query reformulation

Query optimization

Query execution engine

Wrapper

Query in the source schema

Wrapper

Query in union of exported source schema

Distributed query execution

Global Data Model

Alon Halevy, 2000

61

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

62

Created

Eligible patients

Enrolled/ evaluated

The imagepyramid

Published

Michael Vannier

63Microarray clustering using Taverna

1. Query and retrieve microarray data from a caArray data service:cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/CaArrayScrub

2. Normalize microarray data using GenePattern analytical service node255.broad.mit.edu:6060/wsrf/services/cagrid/PreprocessDatasetMAGEService

3. Hierarchical clustering using geWorkbench analytical service: cagridnode.c2b2.columbia.edu:8080/wsrf/services/cagrid/HierarchicalClusteringMage

Workflow in/output

caGrid services

“Shim” servicesothers

Wei Tan et al.

64

Many many tasks:Identifying potential drug targets

2M+ ligands Protein xtarget(s)

Benoit Roux et al.

65

start

report

DOCK6Receptor

(1 per protein:defines pocket

to bind to)

ZINC3-D

structures

ligands complexes

NAB scriptparameters

(defines flexibleresidues,

#MDsteps)

Amber Score:1. AmberizeLigand

3. AmberizeComplex5. RunNABScript

end

BuildNABScript

NABScript

NABScript

Template

Amber prep:2. AmberizeReceptor4. perl: gen nabscript

FREDReceptor

(1 per protein:defines pocket

to bind to)

Manually prepDOCK6 rec file

Manually prepFRED rec file

1 protein(1MB)

6 GB2M

structures(6 GB)

DOCK6FRED~4M x 60s x 1 cpu

~60K cpu-hrs

Amber~10K x 20m x 1 cpu

~3K cpu-hrs

Select best ~500

~500 x 10hr x 100 cpu~500K cpu-hrsGCMC

PDBprotein

descriptions

Select best ~5KSelect best ~5K

For 1 target:4 million tasks

500,000 cpu-hrs(50 cpu-years)

66DOCK 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.

67

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.


68

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


69

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


70


Low

LowHigh

High

Agreementabout

outcomes


Functioning in the zone of complexity

Plan and

control

Chaos

71


Analysis


Management

Integration

Publication

Cognitive support

Applications


Secu

rity a

nd

policy

RHIOData

sources

Platform services

Value services

72

“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

Thank you!

Computation Institutewww.ci.uchicago.edu

aapm foster july 2009

Health & Medicine

quad core

inteldual core

effective health care

clinical care

healthcare costs

quantitative medicine

ibm power62 cores

healthcare outcomespatients