Data Simplification and D

Visualization for CognitivVisualization for Cognitiv

Support in clinical care

by Andrew D. Boyd, MD

Research Assistant Professor

Biomedical and Health Information Sciences

College of Applied Health Sciences College of Applied Health Sciences

University of Illinois at Chicago

boyda@uic.edu

February 16, 2010 
Objectives

• The data challenge in clinical care a

The data challenge in clinical care a

medical research

• Designing cognitive support to meet 

Designing cognitive support to meet 

challenge

• Vi li ti th d f iti Visualization methods for cognitive 

support

February 16, 2010 
Growth in electronic data i

outstri i pp ng storage

• General stora

g

e

– By 2011, the amount of digital information in 

existence will exceed the total storage space available by almost 50 percent according to available by almost 50 percent, according to 

IDC.1

• Bioinformatics

– Genbank doubling every 18 months.

2

• One drop of blood can generate

– 60 i b t f t t d d t f t 

60 giga

b

y

tes o

f uns

truc

ture

d

d

a

ta 

for pro

teom

studies

3

• 2 hours in the ICU can generate

~2 GB o

February 16, 2010

2 hours in the ICU can generate

2 GB o
Future trends Future trends

• $500 personal genome expected in 

$500 personal personal genome expected expected in 

years4

• Federal Government has funded $56

Federal Federal Government Government has funded $56

million5 to fund state Health Informat

Exchanges increasing the amount oExchanges Exchanges, increasing increasing the amount o

information to clinicians

• D i d i f ti l d t b 

Does increase ncreased information lead to b

care?

February 16, 2010 
Recent report from Nation

R h C il Researc

h

Council

• Com

p gy utational Technology for Effective 

Healthcare

6

– Immediate Steps and Strategic Directions

• An Overarching Research Grand Challen

Patient-Centered Cognitive Support

– The clinician interacts with models and abstra

The clinician interacts with models and abstra

the patient that place the raw data into contexsynthesize them with medical knowledge in w

make clinical sense for that patient make clinical sense for that patient. 

– Raw data is still available, but they are not th

focus of the clinician. 

February 16, 2010
Poll

• In the current clinical environment w

In the current clinical environment, w

level of information do the clinicians 

about the patients at their fingertips: about the patients at their fingertips:

– Too much information– Just the right amount of information Just the right amount of information

– Not enough information

February 16, 2010 
Data simplification Data simplification

• One solution is to reduce the quantit

One solution is to reduce the quantit

data that must be digested to the pro

so informed decisions can be made so informed decisions can be made.

• The data does not disappear but bec

molded into paradigms that the healmolded into paradigms that the heal

system can process.

February 16, 2010
Information Theory Information Theory

• Branch of applied mathematics Branch of applied mathematics

– To find fundamental limits on compres

storing and communicating data storing and communicating data

• Data simplification

– Applies the medical knowledge and do

Applies the medical knowledge and do

expertise to reduce the complexity of th

to aid support to aid support

February 16, 2010
Cognitive Support vs Decisi

Support

• Decision Support in electronic health reco

Decision Support

in electronic health reco

– active knowledge systems which use two or m

items of patient data to generate case-specifi

• Cognitive Support in clinical care

– Patient data integrated to provide clinicians s

i f ti t id d i i ki biliti in

formation 

to aid

decision making abilities

• Difference is no advice given in Cognitive 

Support

February 16, 2010
People and Organizational Iss

People and Organizational Iss

• Peo

ple and Or

ganizational Issues

8 p g

– To apply the knowledge of human behaviors 

the use of information technology within a heenvironment. 

– To effectively describe the benefits and impa

information technology before paradigm shiftoccur. 

– To incorporate organizational change manag

and human concerns into information technoprojects. 

– To distinguish between the human and techn

issues wh

en system successes

or f

ail

u

res oc

February 16, 2010 

ssues e syste successes o a u es oc
Human Factors Human Factors

• Human Factors Engineering Human Factors Engineering

– The application of this understanding t

design development and deployment design, development and deployment 

systems and services

• Application requires skills of both Application requires skills of both

disciplines and appreciation of limita

February 16, 2010 
DARPA Virtual Soldier Pro

DARPA Virtual Soldier Pro

• Defense Advanced Research Projec

Defense Advanced Research Projec

Agency (DARPA) funded a larger 

interdisciplinary project called the Viinterdisciplinary project called the Vi

Soldier Project (VSP)

• > 15 senior scientists across industr

> 15 senior scientists across industr

academia, and governmental labs

February 16, 2010 
History of Penetrating Missi

M li orta ty

• Mortality of penetrating injuries Mortality of penetrating injuries

– Siege of Troy 92%– Vietnam 71% Vietnam 71%

• Hospital Mortality 

– 28% C in American 

Civil Wa

r

– 3-14 % in Vietnam

Riyad Karmy Jones Prof Surgery University of Washingt

February 16, 2010 

Riyad Karmy-Jones Prof. Surgery, University of Washingt
DARPA Virtual Soldier Pro

DARPA Virtual Soldier Pro

• Penetrating wounds to the thorax and its 

Penetrating wounds to the thorax and its 

are a major contributor to injury and mortthe combat setting. 

• The purpose of this proposed project is to 

transform the way we view, study, and int

with information about the human body acomplex and interrelated systems, and in so create an applied system that will so create an applied system that will

revolutionize medical care to the soldier. 

February 16, 2010 
DARPA Virtual Soldier Project 

ll i eve overview

• Computer simulations of physiologic

Computer simulations of physiologic

responses

• Statistical analysis of physiological s

Statistical analysis of physiological s

from surrogate models

• Ontological representation of norma

Ontological representation of norma

abnormal anatomy

• Morphometric analysis of CT scans 

Morphometric analysis of CT scans 

and post injury

• Data integration across multiple mod

February 16, 2010 

Data integration across multiple mod
DARPA Virtual Soldier Proje

R dbl k Roadbloc

k

s

• Roadblocks

– All collaborators were forbidden from u

the term model the term model

• Everyone had a different definition of mod

– Time zones and scheduling necessary Time zones and scheduling necessary

meetings

– Multi

py g le wa

ys of reachin

g the same res

were pursued leading to confusion at t

February 16, 2010 
One Goal of the DARPA Vir

S ldi P j S

oldier 

Pro

ject

• Increase the knowledge the Field Me

Increase the knowledge the Field Me

has during the process of triage

– Using state of the art modeling and sta

Using state of the art modeling and sta

– Reduce the complexity of variables to 

rapid assessment of casualty rapid assessment of casualty

February 16, 2010 
High Level Overview of th

DARPA Vi l S ldi P DARPA Virtua

l

S

oldier 

Pro

• Prior to Deployment into the field: Prior to Deployment into the field:

– Collect over 12 baseline physiological variabl

individual

– Obtain cardiac gated Computed Tomography – Create a “Holomer”

• A i t ti f i d h i l i l A in

tegration o

f image an

d p

hysiological

data

• Stored on the Personal Information Carrier

(PIC) d l h (PIC) an

d elsew

here

February 16, 2010 
High Level Overview of th

DARPA Virtual Soldier ProDARPA Virtual Soldier Pro

(part 2)

• Post

-injury

– The Highly Integrated Physiological (HIP) com

models are used to model cardiac injury

• Introduction of few hundred variables and coefficie

– Kalman Filters are used to process the physi

signals of the injured heart signals of the injured heart

• Produces a few outputs• Reports forecasts with associated statistical uncert

– Examine blood values to assist modeling and 

February 16, 2010 
WPSM IC Sensor Interfaces

Fluid Intake

Sleep and Performance

Actigraph

Life Signs

& Ballistic Impact

System

Health Hub & 

Medical 

Encounter Log

WPSMWarrior Physiological Status MonitorInitial Capabilities for Future Force Warrior 

Core Temperature

February 16, 2010 
Experiment

• Complex protocol for each test subject is followed a total o

subjects

• Pertinent Highlights for Data Analysis

– Blood values collected before experiment, and at 5, 15, 30, 

120 minutes of the experiments

– Experiment begins with a penetrating wound to the heart

• Experiments lasts 120 minutes• Subjects that had blood exsanguination with in 10 minutes 

not included in the study not included in the study

• Result Grouped into two groups compared

– Subjects that lived for the full 120 minutes– Subjects that died between 10 and 120 minutes

• Animal experiments conducted at US Army Institute for Su

Research, data shared collaboratively across collaborator

February 16, 2010 
Lactate vs Time Lactate vs Time

.

February 16, 2010 
Delta lactate versus time. Solid L

Survivors, Dashed Line Non-surviv

February 16, 2010 
Brief overview of Triage Brief overview of Triage

• One of the underl

y gp p 

in

g

princi

ples “Tri

establishes the order of treatment, nwhether treatment is 

given”

• Four priority classifications of the ca

– Immediate– Delayed– Minimal– Expectant

February 16, 2010 
Definitions

• Immediate – demand immediate trea

to save life

• Delayed – less risk of loss of life or l

• Minimal – casualties might be self tr• Expectant – only used if resources a

only used if resources a

limited, so critically injured that only 

complicated treatment can improve complicated treatment can improve 

expectancy

February 16, 2010 
Classification of MEDEVA

• Medical Evacuation (MEDEVAC) priority

– Urgent

• Casualties whose status cannot be controlled and have t

opportunity for survival

– Urgent – Surgical 

• Casualties who need far forward surgical intervention to s

– Priority

• Casualties are not stable and at risk of

trauma-related com

plications

– Routine 

• Casualties can be controlled

– Convenience

• Evacuated for convenience, not 

medical necessity

February 16, 2010 
MEDEVAC

• Larger question the medic is solving• How do I transport casualty?

– Factors influencing decision

• Situational awareness of environment• Mission objectives• Distance to Far Forward Field hospital

• Reality

– No computer decision support algorithm possible to in

No computer decision support algorithm possible to in

the above information

• Cognitive support of how long does the casualty 

live

– Aids in helping to select transportation methods– Assists in triaging

• Decision authority resides in the medicFebruary 16, 2010 

Decision authority resides in the medic
Compaq IPAQ handheld Compaq IPAQ handheld

• Battlefield Medical Battlefield Medical

Information System Tactical (BMIST)

– S ft d l d ld S

oftware 

deploye

d world

wide

– Interface with the Personal 

I f ti C i (PIC) In

formation 

Carrier (PIC)

– Capture data from encounter 

with field medic

• Challenges

– Small screen– Modest Computing Power

February 16, 2010 

Modest Computing Power
Challenge of Presenting D

Challenge of Presenting D

• Too much data! Too much data!• 12 baseline variables (wave forms)• 300 output variables from the HIP m

300 output variables from the HIP m

• CT images• Ph i l i l M t B ttl 

Physiological Measuremen

t on 

B

attl

• Forecasts and statistical uncertaintieFebruary 16, 2010 
First Possible View First Possible View

• Small viewer of CT data 

to see anatomy of soldier

• While helpful further in 

medical treatment, limited medical treatment, limited

application in the field

• Field Medic will need 

further training to interpret further training to interpret

CT

• Currently only pre-injury 

CT il bl CT avail

abl

e

• Future availability of 

battle field CT scanne

r

February 16, 2010 
Second Possible View Second Possible View

• Pressure tracin

g py p dis

pla

y out

put 

from the HIP models

• Models are information rich• May aid in projecting loss of May aid in projecting loss of

blood and changes to physiology

• Fi ld M di ill d f th Fi

eld

M

edic will nee

d

further 

training to fully understand all 300 variables

• Too much information for direct 

field use

February 16, 2010 
Third Possible View Third Possible View

• Simple Text screen with vital 

si d i i l di i igns an

d statistical predictions

• Allow Medic to decide the 

ultimate priority of casualty

• Att t t di d Attemp

ts 

to diagnose an

d

forecast time to death

• Most triage protocols do not 

include statistical uncertainty include statistical uncertainty. 

Ours may do so.

• Attempted to highlight HR and 

BP of current tria

g p e 

protocols

February 16, 2010 
Fourth Possible View Fourth Possible View

• Simple color coded graph of st t a

tus

• Allow a field medic an 

instant snap shot of a casualt

y

• Information provided in the 

screen will not help 

distinguish between Urgent distinguish between Urgent

and Urgent-Surgical due to high level abstraction

• Limited information to help Limited information to help

decide between Immediate or Expectant

February 16, 2010 
Fifth View Fifth View

• Text output of the Warfighter 

Ph i l i l St t Physiological Status 

Monitoring (WPSM)

• Allowing medic direct access 

t t o measurements

• Allows location of wound to 

be entered into the handheld

• Requires additional 

processing of data from the medic

• Nl t t o long term outcome 

information

February 16, 2010 
Final View Final View : MEDEVAC : MEDEVAC

Final proposed field Medic Display Final proposed field Medic DisplayThe stop sign is either a green circle, yellow triangle, or red stop sign.Additional information about Estimated time to death is provided for further inspection provided for further inspectionTwo independent signals could be usedto create the stop sign, lactate and bp algorithm.Key goals:1. The human still makes triage decisions2. Provide sufficient information for rapid assessmentFebruary 16, 2010 3. Provide additional information for further refinement of decisions
Poll

• As demonstrated, new information can ch

As demonstrated, new information can ch

how clinicians think and work. When prewith new information (a new lab test, or n

procedure) about a clinical or research scdo you tend to:

– Ignore the new data– Process it and continue traditional work flow– Spend hours to understand the implication of 

Spend hours to understand the implication of 

information and change workflow

– It depends

February 16, 2010 
Final Physician Display Final Physician Display

February 16, 2010 
Visualization methods for 

MEDEVAC

• Used existing paradigms of green ci

Used existing paradigms of green ci

yellow triangle, and red octagon to 

visualize high level data visualize high level data

• Future challenges in making explicit 

uncertainty that is currently excluded uncertainty that is currently excluded 

protocols

• N t ll d t i l t t iti l d 

N

o

t all

d

a

ta is relevan

t

to critical dec

making thoughts

February 16, 2010 
Limitations of existing experim

Limitations of existing experim

• As this project was a prototype As this project was

a prototype

– No formal evaluation of display was bu

• Physician display of data model also Physician display of data model also

needs additional collaboration and 

refinement

• Data models of diseases and physio

states are challenging and required collaboration across multiple discipli

February 16, 2010 
Conclusions/Implication

• The implicit ncertaint of triage ill bec

The implicit uncertainty of triage will bec

explicit in future triage protocols

• Additional training to incorporate new kn

Additional training to incorporate new kn

into the decision tree of the medic is nec

• Input from physicians medics and usab

Input from physicians, medics, and usab

specialists will need to be consulted for interface, y which ma

y be one or more scr

February 16, 2010 
Conclusions

• Transforming data into knowledgeable informat

Transforming data into knowledgeable informat

guide decision making is a challenge

– As interfaces are desig ,p 

ned, specific clinical 

need to be addressed

– Increase access to data is not the solution

• Designing cognitive support to meet this challe

– Must have interviews and dialogues with all 

parties to understand clinical domain and keparties to understand clinical domain and ke

questions

• Visualization methods for cognitive support

February 16, 2010 

– Reuse existing paradigms to increase ease 
DARPA Collaborators DARPA Collaborators

• Partnering Labs and Investigators

– Brian Athey University of Michigan Brian Athey, University of Michigan– Gerry Moses and Tommy Morris, Telemedicine and Advanced Technology R

(TATRC) 

– Chris Johnson, University of Utah– Andrew McCulloch, UC- San Diego

– J B i th i ht d C li R U i it f W hi t James 

Bassingthwaighte an

d

Cornelius 

Rosse, 

U

niversity of Washington

– Richard Ward, Oak Ridge National Laboratory – Henry Kelly, Federation of American Scientists– Mark Musen, Stanford University 

– Bill Lorensen, General Electric Research– Bob Eisler, Mission Research Corp– LTC. James Fudge and CPT Eric Ansorge, US Army Institute for Surgical Re– Tom Menten, Crowley-Davis Research– John Monville, Xtria– Peter Hunter University of Auckland Peter Hunter, University of Auckland– Peter Ratitu, Brigham & Women's Hospital, Harvard University

• Funding

– This work was supported by a grant from DARPA, executed by the U.S. Army 

Research and Materiel Command/TATRC Contract # W81XWH-04-0012

February 16, 2010 
Citations

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February 16, 2010