hunter Whitney The Journal of Precision Medicine

PRECISION TOOLS

A review of the current tools and technologies that are enabling a deeper insight into the mechanism of various diseases.

A review of the current tools and technologies that are enabling a deeper insight into the mechanism of various diseases.

13

High Resolution: bringing a sharper focus to precision medicine through design

W ith so much attention focused on new ways to collect, combine, and mine health data,

it can be easy to overlook the critical need to clearly display the output. The interfaces and

data visualizations for precision medicine need to be intelligible, meaningful, and action

able for the people who depend on them. While the many tried-and-true approaches to

displaying quantitative information will continue to play essential roles, they are unlikely to be sufficient to

meet current and future demands. There are several converging forces driving these new needs including the

massively expanding volume and variety of data and the ever-increasing diversity of people expecting to make

use of it all. Technology may be the main driver but, by definition, medicine’s ultimate impact is on human beings

and not machines. From biomedical researchers engaged in drug discovery to physicians considering treatment

plans to patients making difficult but informed personal decisions, designing user interfaces and visualizations

should not just be a side note. If the goal of precision medicine is to provide more effective, targeted treatments

and tailored preventative care, shouldn’t the information support systems for the participants in these efforts

also be just as tailored and effective? That means thinking deeply about design considerations including:

l Moving toward more fluid, dynamic interactions with data rather than needing to rely on static, fragmentary views.

l Tuning the data presentation for the intended users without stripping out the meaning and value.

l Enabling the representations to reveal, at least to some degree, potential problems in the underlying data.

l Making displays that address questions about data such as - What is the context? What is important here? What can I do with this?

The interviewees in this article have vantage points ranging from patient to health care provider and from a biomedical researcher to a

technology executive whose company has deep roots in Formula One racecars. In each area, there are some emerging commonalities as

well as divergent needs that design will need to address.

The Roads To - and From - Wellness

Joel Dudley, Director of Biomedical Informatics at Mount Sinai School of Medicine, believes that some of the same technological capabilities that are

fueling precision medicine are also the source of its greatest challenges. “This is all really being enabled by an explosion of data and it’s concerning

to me that the things that are making this possible are also presenting real problems in terms of information overload for physicians and patients.”

Dudley believes not enough effort is being put into figuring out how to take all this information to make it digestible and actionable. “I think a lot

of the effort and funding around precision medicine has been spent on collecting more and more data and building algorithms to put data together

and analyze it.” However, he doesn’t see funding put toward designing these systems. He adds, “To my mind, it shows a real lack of appreciation

of the role of design in this area.”

by Hunter Whitney

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Dudley believes new generations of tools are

needed to visualize and explore, for example,

the data about patterns of relationships in

patient populations. These tools would enable

users to ask clinical questions about patients

with shared characteristics. “A lot of precision

medicine will involve representing an individual

patient in the context of other patients.”

Dudley continues, “We just completed a project

in which we took the patient population at

Mount Sinai and took all of the lab tests and

created a similarity score with other patients

based on that clinical data.” Dudley says they

had several hundred variables and developed

a patient-patient similarity network and then

highlighted where all the type 2 diabetics were

positioned. It turned out that there were

several different hot spots and they had different

clinical characteristics. For example, even

though all type 2 diabetics had a heart attack

risk increase, one subgroup of diabetics had a

higher risk than the others. Another hot spot,

he adds, was cancer risk. He wonders, “How

do you efficiently traverse the network space

to move closer to an important cluster of

characteristics in a patient population?” On the

care delivery side, he believes the emphasis

shifts from investigating networks to looking

at health trajectories, inflection points, and

outcomes. In other words, what are the potential

health outcomes for a person following one

course of action versus another? For example,

looking at where care paths of similar patients

diverge, such as the introduction of a medication,

and determining what impact that has. These

care pathway tools, Dudley notes, are more

driven by data rather than only relying on

guidelines.

What’s in a Number? Data, Metadata, and Design

What does the number 280 mean? When it

appears on Manny Hernandez’s continuous

glucose monitor (CGM), it signifies that his

blood sugar level is too high and he’ll need to

take corrective action very soon. The number

is given additional context by an accompanying

Smoking

Exercise

Diet

Prostatecancer

Hypertension Depression

Type 2diabetes

Obesity

Osteoarthritis

Coronaryartery disease

Asthma

Abdonimalaortic aneurysm

StressAlcohol

Antihypertensives

Air pollution

Sodium

Interferon-a

Pesticides

Cocaine

NSAID

Anticoagulants

Statins

Myocardialinfarction

MAO inhibitorsParkinson’s

Disease

Allergens

Antipsychotics

Injury

arrow in either upward, level, or down trend,

with double up arrows and double down arrows

showing, respectively, a more pronounced

trend. As a person with diabetes, the conse-

quences of a mistaken assessment of blood

sugar level, and it’s likely trajectory, can be

severe. Even though he may be taking all the

right actions, the number may still in a

problematic range or it may be inaccurate for

a host of reasons that are not readily apparent.

The more good context the system can provide,

the better. A CGM is only one component of

a system that Hernandez uses to keep track

of his blood sugar levels. The mainstay device

is called a glucose meter or glucometer that

measure the level from a single sample of

blood, but does not show trends in the data.

His CGM needs to be calibrated with a

glucometer on a regular basis but, the glucometer

cannot show multiple readings. (The full

process and nuances of the devices that

Hernandez employs monitor his data takes

some effort and won’t be described in full

A map of health risk. Reprinted from The Lancet,

Vol. 375 (9725), 2010. Ashley E.A., Butte A. J., Wheeler

M. T., Chen R., et al. Clinical assessment incorporating

a personal genome, pp. 1525-1535, with permission

from Elsevier.

15

here.) However, viewing data and taking action

in real-time when needed is simply part of his

life and something he does to be healthy. He

shares aggregated views of his blood glucose

data with his doctor to plan longer term

strategies. He thinks about this relationship

with data not only for his own wellbeing, but

also as a community advocate. For him, and his

community, the design and displays of glucose

monitoring systems is an ongoing pressing

consideration. Hernandez says, “When you see

a number on a display, it can feel very real even

though there are many factors that can throw

it off.” For example some monitors may not be

as accurate as others. Even a high quality device

may present an incorrect number. For instance,

for glucometers, a bit of sugar that might happen

to be stuck on his fingertips when he is doing a

blood test can significantly throw off the results..

“The problem is sometimes people see the number

and just don’t question it.” There’s a kind of

seeming validity and weight imparted by the system

display. Clearly have a high degree of distrust

of a monitor is not an optimal strategy either.

So how can design solve some of the problem it

sometimes causes? One approach is to visualize

normal patters of behavior in certain contexts

and then compare that to a number that might

require a decision. The system can draw upon its

own number crunching abilities to provide more

context to a single reading. That way the design

Where did this come from?

What is this showing me?

Do I have other options forvisualizing this data?

What can I do with it?

100

90

80

70

60

50

40

30

20

10

0

Design Plays a Role in Addressing These Key Questions

of the display can draw upon the strengths of an

analytic tool to minimize the form presenting a

key measure with insufficient context. “It’s the

metadata about the data which makes all the

difference in the world about what actions you

take.” Hernandez believes that imbuing systems

with the ability to passively collect and represent

contextual data such as location, time of day, the

last time someone ate at a particular place, would

be very useful. He adds, “If you can model and

project into the future, that adds even more

value.” This same idea applies to design

approaches for many areas of precision medicine.

Jujitsu and Narratives at Scale

Jon Duke, MD, MS, is Chief Innovation Officer

and Director Drug Safety Informatics at the

Regenstrief Institute. He says, “The emphasis

has really been placed around creating powerful

methodologies for working with big data, but

there’s been much less progress made with how

to represent that information to the end users.”

Duke observes, “I’ve seen conferences where

absolutely brilliant data scientists are presenting

tremendous work and you can almost see all

of this ‘data jujitsu’ falling flat for the mostly

clinical audiences.” An analysis including data

from millions of patients and involving

complex data mining and statistical techniques

that show a particular number for a patient’s

risk of developing diabetes, he says, can still

leave many questions in his own mind. For

example, he might wonder about the source

data and what potential influencing factors are

not being conveyed about the results. Duke

believes there are so many moving parts in

the process of precision medicine, that the

challenges of data presentation may get worse

before they get better. “I think we may get to a

place initially where people become more

confused, not less.” He says, “I would argue

that where the biggest need is right now is

for designs that can communicate the outputs

of big data and precision medicine’s analyses.”

Duke believes that “where people are absolutely

struggling is how do we manifest the findings

of, for example, a genomic study so that the

data is graspable.” He adds that designs that

can clarify are “not just for physicians - there

are many decision makers in the health system -

administration, leadership, government

agencies, clinicians, and patients themselves.”

Duke thinks part of the answer to helping

people connect with analysis is by providing

relatable context - “even big data has to have

a story,” he says in what he terms “narratives

at scale.” “The way I think about narratives at

scale is that big data has to connect to a story

that you can tease out. When people start

digging into the findings of a large-scale

analysis, it often ends up with them asking –

can you tell me about a patient that was part

of this?” Duke adds that

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he’s seen many times, “when you can explain

this at the individual patient level, it all seems

to register. They see what you’ve done and how

you’ve arrived at the results.” He notes that this

kind of context-setting can be aided with tech-

niques such as natural language processing

(NLP) or text mining. He says NLP produces a

great deal of information that has previously

been hard to access about patient symptoms,

the reasoning behind certain decision-making,

and even sentiment analysis. “You can do

that at scale, but it has to be part of a story.”

Although precision medicine involves diving

into aspects of patient health data at a very fine

level of detail, Duke says it’s important to keep

sight of the big picture. “We are going to use all

these sharpened tools to look at the individual

but, the reality is, much of the simple stuff still

makes a big difference in terms of translating

into action.” He says, the designs that develop

around precision medicine shouldn’t let people

drill down so far they get lost in the details and

lose sight of larger factors that have a greater

impact. “We need to have context so factors

such as socioeconomic situations are also on

our radar.”

The Perils of Data Alchemy

Thomas Wilckens MD, is CSO/CEO and Founder

of the precision medicine start-up InnVentis.

He worries that data scientists are being

considered “modern alchemists.” Why is that

a potential problem? Wilckens thinks that in

many instances, some of data that will undergo

transformation is not adequate for how it will

be used or the reasons for certain underlying

patterns of data will not be taken into account.

He says, “The quality of the data set - how it’s

collected, its reproducibility, and comparability

can be real problems.” Wilckens adds, “When

we come closer to the hardcore biochemistry,

small differences can have a big impact.” He

notes that a colleague who runs a metabolomics

center said that for some metabolomics assays,

it makes a difference if the patient stands or

sits for the measurements. Wilckens asks,

“How many people would have considered

Design decisions have a big impact on what people can find in the data.

Data Insights: New Ways to Visualize and Make Sense

of Data. Published November 2012 by Morgan

Kaufmann/Elsevier

However, as important as user control can

be, it can also introduce more complexity.

Friedman says, “I think controls are really

useful, but sometimes that can go too far as

well. It’s a balancing act.” Friedman adds,

“We put a lot of care into building tools like

the AnalysisPageServer, that support analytic

workflows rather than just making static reports

and plots which, in a way, can be dead-ends.”

The author of this article had the privilege

of working with Friedman on a project for an

analytic tool and saw him firsthand make the

user experience of the application a top

consideration. Among other things, that

meant direct discussions with users during

the development process and incorporation

of their feedback into the interface. It also

included designing the tool to be as easy to

use as possible and make the interactions

that drive the workflow as fluid as possible.

Friedman says, “Every piece of data and every

visualization has another hundred pieces of

metadata attached to it and you have to find a

way to focus on the ones that are important. I

think that’s what the idea of precision is really

getting at. We want to be able to drill down

without being overwhelmed by the data,”

Friedman says, “We’ve done a lot of gene

expression profiling and want to enable our

research collaborators to be able to dive into

that information, so we organized eight

or ten of the most common questions they

want to ask and they can now collect their

data sets through the web interface, select the

other parameters of these analyses and get the

results to see which genes are differentially

expressed and where the outliers are in a really

user-friendly and easy-to-learn way. By putting

the entire workflow within one application,

it makes it a more useful tool for people.

that impact even three or four years ago?” He

says that is just one of the challenges. It’s a

different issue with genomics in which different

systems doing the sequencing will produce

different patterns of errors. “That’s something

the number crunchers can deal with, but what

they can’t deal with is what I call the ‘batch

factor’.” The way blood is drawn, stored, and

analyzed at various times all has an impact on

the resulting data and metadata. “If you don’t

really know the history of the sample, you have

a significant problem.” Some problems with

flawed data, Wilckens notes, can be mitigated

by pairing effective visualizations with trained

observers who can detect anomalous patterns.

“If you only look at spreadsheets you may

never realize that there is something completely

wrong in the data, unless you have a proper

visualization to reveal it.”

Cutting Through the Static: Tools

For Dynamic Analysis

Brad Friedman, PhD, is a computational biologist

at Genentech who, along with being a researcher,

has also developed a web-based framework

for sharing data and interactive visualizations

called AnalysisPageServer. This tool can be used

for various kinds of analytic work including

the exploration of gene expression data – an

important measure for precision medicine. He

says, “It’s nice to have one foot in each domain

as a tool builder and a tool user - by using

the tools in my analytic work, I become more

aware of what I want from them.” On the other

hand, as a tool builder, Friedman says, “There

are certain kinds of analyses that I may not

be thinking about while I am building the

application or report, but that I still want to

ensure will be possible to do by users.” He

says they want to empower users to bring their

own understanding to the data, even things

he would never initially have considered. In

practical design terms, that means embedding

a lot of interactivity into the interface. For

example, he says there are options that enable

users to bring together and explore different

data sets that haven’t been combined before.

19

Hunter Whitney is a consultant, author, and instructor who

brings a user experience (UX) design perspective to data

visualization. He has advised corporations, start-ups,

government agencies, and NGOs to achieve their goals

through a strategic design approach to digital products and

services. Hunter is the author of “Data Insights: New Ways to

Visualize and Make Sense of Data” and contributed a chapter

in “Designing for Emerging Technologies: UX for Genomics,

Robotics, and the Internet of Things.”

will this trend affect my outcome? What’s the

course of action?” He notes that, automotive

engineers might be interested in a somewhat

different set of questions and might want

to look at the past to see where things went

wrong – looking at the same data, but with a

different level of detail. “The screen you see

depends on the function you fulfill, even if

there’s the same data underpinning all the

various outputs,” He says. “We don’t tend to

present a lot of alarms because they can make

the user stressed rather than informed.”

problems, he says, start with multiple variables

and people have several choices to rapidly

judge to determine the best course of action.

“Our models are based on multiple variables

then we start looking at confidence limits and

probabilities around outcomes once changes

are made. McGrath adds, “We still frequently

use Monte Carlo simulations, especially in the

race context.” This includes, appropriately

enough, the Monaco Grand Prix. They will run

thousands of simulated scenarios during the

race in the background, McGrath explains, so

when problems occur, the models suggest a

course of action. “You don’t have to sit around

wondering what to do next” he says and adds,

“That’s our ambition as we go into the medical

space. We are at the early phase of a very long

road, but we’re achieving very promising results.”

Conclusion

Precision medicine is fueled by emerging

forms of collaboration and new feedback loops

between the processing power of machines

and human intellect. The quality of interface

and visualization designs will have a big impact

on the success or failure of these interactions

and outcomes. By thinking deeply about

design, precision medicine can reach the level

of resolution it needs to fulfill its promise.

PROBABILITIES PREDICTION OUTCOME

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Minds, Machines, and Monte Carlo – New

Drivers for Medicine

Some of the best drivers of innovation arise by

taking perspectives from one field and applying

them to another. That’s been a recurring theme

for Geoff McGrath, a vice president of McLaren

Applied Technologies, a high performance

design and engineering company. The company

has branched out from its roots in the world

of Formula One (F1) racecars. For example,

McLaren has teamed up with pharmaceutical

company GlaxoSmithKline on a range of

R&D initiatives, including data analytics and

visualization. “For me,” McGrath says, “going

into healthcare was not a great leap – we don’t

want to limit ourselves to racing technology

per se and embrace a range of other technologies

from other sources and bring them to

market in a fast track sort of way.” He says

they don’t separate drivers from the product

that they drive and takes a person-centered

design approach. “You have to put the humans

first, then design and engineer around them,

and keep the designers in the process to make

changes based on feedback.” The way we feed

the models is by weaving intelligence [e.g.,

sensors] into the product and then feeding

that intelligence back to the design team

which, in turn, feeds the models that can then

be more prescriptive for the users. This is the

philosophy we’ve managed to translate to a

whole range of product types, including medical

devices. It doesn’t matter if the product is an

inhaler or other drug delivery system,” he

says. “It has intelligence built in to provide

information about how customers are using the

product and how they might get more from it.”

Design Rules of the Road

McGrath and his colleagues use a number of

design heuristics in their work. For example,

in the racing context, he says, participants don’t

have much time to think so the displays need

to “crisply suggest the best course of action or

intervention.” He adds, “The design challenge

is capturing and then presenting quickly the

answers to questions like - Where am I? How

McGrath continues, “That’s why I like the idea

of predictive or even prescriptive analytics -

predictive meaning if you carry on this way,

this outcome is likely.” There’s a program that

they’re working on that addresses this issue

in the context of a neonatal intensive care for

premature infants. “These patients are very

difficult to monitor, so you need to establish a

baseline for the individual child and then

try and spot the anomalies,” he says. Following

from that, anomaly alerts should be designed

so that they optimize the nurse’s time. Most

Dudley believes new generations of tools are

needed to visualize and explore, for example,

the data about patterns of relationships in

patient populations. These tools would enable

users to ask clinical questions about patients

with shared characteristics. “A lot of precision

medicine will involve representing an individual

patient in the context of other patients.”

Dudley continues, “We just completed a project

in which we took the patient population at

Mount Sinai and took all of the lab tests and

created a similarity score with other patients

based on that clinical data.” Dudley says they

had several hundred variables and developed

a patient-patient similarity network and then

highlighted where all the type 2 diabetics were

positioned. It turned out that there were

several different hot spots and they had different

clinical characteristics. For example, even

though all type 2 diabetics had a heart attack

risk increase, one subgroup of diabetics had a

higher risk than the others. Another hot spot,

he adds, was cancer risk. He wonders, “How

do you efficiently traverse the network space

to move closer to an important cluster of

characteristics in a patient population?” On the

care delivery side, he believes the emphasis

shifts from investigating networks to looking

at health trajectories, inflection points, and

outcomes. In other words, what are the potential

health outcomes for a person following one

course of action versus another? For example,

looking at where care paths of similar patients

diverge, such as the introduction of a medication,

and determining what impact that has. These

care pathway tools, Dudley notes, are more

driven by data rather than only relying on

guidelines.

What’s in a Number? Data, Metadata, and Design

What does the number 280 mean? When it

appears on Manny Hernandez’s continuous

glucose monitor (CGM), it signifies that his

blood sugar level is too high and he’ll need to

take corrective action very soon. The number

is given additional context by an accompanying

Smoking

Exercise

Diet

Prostatecancer

Hypertension Depression

Type 2diabetes

Obesity

Osteoarthritis

Coronaryartery disease

Asthma

Abdonimalaortic aneurysm

StressAlcohol

Antihypertensives

Air pollution

Sodium

Interferon-a

Pesticides

Cocaine

NSAID

Anticoagulants

Statins

Myocardialinfarction

MAO inhibitorsParkinson’s

Disease

Allergens

Antipsychotics

Injury

arrow in either upward, level, or down trend,

with double up arrows and double down arrows

showing, respectively, a more pronounced

trend. As a person with diabetes, the conse-

quences of a mistaken assessment of blood

sugar level, and it’s likely trajectory, can be

severe. Even though he may be taking all the

right actions, the number may still in a

problematic range or it may be inaccurate for

a host of reasons that are not readily apparent.

The more good context the system can provide,

the better. A CGM is only one component of

a system that Hernandez uses to keep track

of his blood sugar levels. The mainstay device

is called a glucose meter or glucometer that

measure the level from a single sample of

blood, but does not show trends in the data.

His CGM needs to be calibrated with a

glucometer on a regular basis but, the glucometer

cannot show multiple readings. (The full

process and nuances of the devices that

Hernandez employs monitor his data takes

some effort and won’t be described in full

A map of health risk. Reprinted from The Lancet,

Vol. 375 (9725), 2010. Ashley E.A., Butte A. J., Wheeler

M. T., Chen R., et al. Clinical assessment incorporating

a personal genome, pp. 1525-1535, with permission

from Elsevier.

15

here.) However, viewing data and taking action

in real-time when needed is simply part of his

life and something he does to be healthy. He

shares aggregated views of his blood glucose

data with his doctor to plan longer term

strategies. He thinks about this relationship

with data not only for his own wellbeing, but

also as a community advocate. For him, and his

community, the design and displays of glucose

monitoring systems is an ongoing pressing

consideration. Hernandez says, “When you see

a number on a display, it can feel very real even

though there are many factors that can throw

it off.” For example some monitors may not be

as accurate as others. Even a high quality device

may present an incorrect number. For instance,

for glucometers, a bit of sugar that might happen

to be stuck on his fingertips when he is doing a

blood test can significantly throw off the results..

“The problem is sometimes people see the number

and just don’t question it.” There’s a kind of

seeming validity and weight imparted by the system

display. Clearly have a high degree of distrust

of a monitor is not an optimal strategy either.

So how can design solve some of the problem it

sometimes causes? One approach is to visualize

normal patters of behavior in certain contexts

and then compare that to a number that might

require a decision. The system can draw upon its

own number crunching abilities to provide more

context to a single reading. That way the design

Where did this come from?

What is this showing me?

Do I have other options forvisualizing this data?

What can I do with it?

100

90

80

70

60

50

40

30

20

10

0

Design Plays a Role in Addressing These Key Questions

of the display can draw upon the strengths of an

analytic tool to minimize the form presenting a

key measure with insufficient context. “It’s the

metadata about the data which makes all the

difference in the world about what actions you

take.” Hernandez believes that imbuing systems

with the ability to passively collect and represent

contextual data such as location, time of day, the

last time someone ate at a particular place, would

be very useful. He adds, “If you can model and

project into the future, that adds even more

value.” This same idea applies to design

approaches for many areas of precision medicine.

Jujitsu and Narratives at Scale

Jon Duke, MD, MS, is Chief Innovation Officer

and Director Drug Safety Informatics at the

Regenstrief Institute. He says, “The emphasis

has really been placed around creating powerful

methodologies for working with big data, but

there’s been much less progress made with how

to represent that information to the end users.”

Duke observes, “I’ve seen conferences where

absolutely brilliant data scientists are presenting

tremendous work and you can almost see all

of this ‘data jujitsu’ falling flat for the mostly

clinical audiences.” An analysis including data

from millions of patients and involving

complex data mining and statistical techniques

that show a particular number for a patient’s

risk of developing diabetes, he says, can still

leave many questions in his own mind. For

example, he might wonder about the source

data and what potential influencing factors are

not being conveyed about the results. Duke

believes there are so many moving parts in

the process of precision medicine, that the

challenges of data presentation may get worse

before they get better. “I think we may get to a

place initially where people become more

confused, not less.” He says, “I would argue

that where the biggest need is right now is

for designs that can communicate the outputs

of big data and precision medicine’s analyses.”

Duke believes that “where people are absolutely

struggling is how do we manifest the findings

of, for example, a genomic study so that the

data is graspable.” He adds that designs that

can clarify are “not just for physicians - there

are many decision makers in the health system -

administration, leadership, government

agencies, clinicians, and patients themselves.”

Duke thinks part of the answer to helping

people connect with analysis is by providing

relatable context - “even big data has to have

a story,” he says in what he terms “narratives

at scale.” “The way I think about narratives at

scale is that big data has to connect to a story

that you can tease out. When people start

digging into the findings of a large-scale

analysis, it often ends up with them asking –

can you tell me about a patient that was part

of this?” Duke adds that

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he’s seen many times, “when you can explain

this at the individual patient level, it all seems

to register. They see what you’ve done and how

you’ve arrived at the results.” He notes that this

kind of context-setting can be aided with tech-

niques such as natural language processing

(NLP) or text mining. He says NLP produces a

great deal of information that has previously

been hard to access about patient symptoms,

the reasoning behind certain decision-making,

and even sentiment analysis. “You can do

that at scale, but it has to be part of a story.”

Although precision medicine involves diving

into aspects of patient health data at a very fine

level of detail, Duke says it’s important to keep

sight of the big picture. “We are going to use all

these sharpened tools to look at the individual

but, the reality is, much of the simple stuff still

makes a big difference in terms of translating

into action.” He says, the designs that develop

around precision medicine shouldn’t let people

drill down so far they get lost in the details and

lose sight of larger factors that have a greater

impact. “We need to have context so factors

such as socioeconomic situations are also on

our radar.”

The Perils of Data Alchemy

Thomas Wilckens MD, is CSO/CEO and Founder

of the precision medicine start-up InnVentis.

He worries that data scientists are being

considered “modern alchemists.” Why is that

a potential problem? Wilckens thinks that in

many instances, some of data that will undergo

transformation is not adequate for how it will

be used or the reasons for certain underlying

patterns of data will not be taken into account.

He says, “The quality of the data set - how it’s

collected, its reproducibility, and comparability

can be real problems.” Wilckens adds, “When

we come closer to the hardcore biochemistry,

small differences can have a big impact.” He

notes that a colleague who runs a metabolomics

center said that for some metabolomics assays,

it makes a difference if the patient stands or

sits for the measurements. Wilckens asks,

“How many people would have considered

Design decisions have a big impact on what people can find in the data.

Data Insights: New Ways to Visualize and Make Sense

of Data. Published November 2012 by Morgan

Kaufmann/Elsevier

However, as important as user control can

be, it can also introduce more complexity.

Friedman says, “I think controls are really

useful, but sometimes that can go too far as

well. It’s a balancing act.” Friedman adds,

“We put a lot of care into building tools like

the AnalysisPageServer, that support analytic

workflows rather than just making static reports

and plots which, in a way, can be dead-ends.”

The author of this article had the privilege

of working with Friedman on a project for an

analytic tool and saw him firsthand make the

user experience of the application a top

consideration. Among other things, that

meant direct discussions with users during

the development process and incorporation

of their feedback into the interface. It also

included designing the tool to be as easy to

use as possible and make the interactions

that drive the workflow as fluid as possible.

Friedman says, “Every piece of data and every

visualization has another hundred pieces of

metadata attached to it and you have to find a

way to focus on the ones that are important. I

think that’s what the idea of precision is really

getting at. We want to be able to drill down

without being overwhelmed by the data,”

Friedman says, “We’ve done a lot of gene

expression profiling and want to enable our

research collaborators to be able to dive into

that information, so we organized eight

or ten of the most common questions they

want to ask and they can now collect their

data sets through the web interface, select the

other parameters of these analyses and get the

results to see which genes are differentially

expressed and where the outliers are in a really

user-friendly and easy-to-learn way. By putting

the entire workflow within one application,

it makes it a more useful tool for people.

that impact even three or four years ago?” He

says that is just one of the challenges. It’s a

different issue with genomics in which different

systems doing the sequencing will produce

different patterns of errors. “That’s something

the number crunchers can deal with, but what

they can’t deal with is what I call the ‘batch

factor’.” The way blood is drawn, stored, and

analyzed at various times all has an impact on

the resulting data and metadata. “If you don’t

really know the history of the sample, you have

a significant problem.” Some problems with

flawed data, Wilckens notes, can be mitigated

by pairing effective visualizations with trained

observers who can detect anomalous patterns.

“If you only look at spreadsheets you may

never realize that there is something completely

wrong in the data, unless you have a proper

visualization to reveal it.”

Cutting Through the Static: Tools

For Dynamic Analysis

Brad Friedman, PhD, is a computational biologist

at Genentech who, along with being a researcher,

has also developed a web-based framework

for sharing data and interactive visualizations

called AnalysisPageServer. This tool can be used

for various kinds of analytic work including

the exploration of gene expression data – an

important measure for precision medicine. He

says, “It’s nice to have one foot in each domain

as a tool builder and a tool user - by using

the tools in my analytic work, I become more

aware of what I want from them.” On the other

hand, as a tool builder, Friedman says, “There

are certain kinds of analyses that I may not

be thinking about while I am building the

application or report, but that I still want to

ensure will be possible to do by users.” He

says they want to empower users to bring their

own understanding to the data, even things

he would never initially have considered. In

practical design terms, that means embedding

a lot of interactivity into the interface. For

example, he says there are options that enable

users to bring together and explore different

data sets that haven’t been combined before.

19

Hunter Whitney is a consultant, author, and instructor who

brings a user experience (UX) design perspective to data

visualization. He has advised corporations, start-ups,

government agencies, and NGOs to achieve their goals

through a strategic design approach to digital products and

services. Hunter is the author of “Data Insights: New Ways to

Visualize and Make Sense of Data” and contributed a chapter

in “Designing for Emerging Technologies: UX for Genomics,

Robotics, and the Internet of Things.”

will this trend affect my outcome? What’s the

course of action?” He notes that, automotive

engineers might be interested in a somewhat

different set of questions and might want

to look at the past to see where things went

wrong – looking at the same data, but with a

different level of detail. “The screen you see

depends on the function you fulfill, even if

there’s the same data underpinning all the

various outputs,” He says. “We don’t tend to

present a lot of alarms because they can make

the user stressed rather than informed.”

problems, he says, start with multiple variables

and people have several choices to rapidly

judge to determine the best course of action.

“Our models are based on multiple variables

then we start looking at confidence limits and

probabilities around outcomes once changes

are made. McGrath adds, “We still frequently

use Monte Carlo simulations, especially in the

race context.” This includes, appropriately

enough, the Monaco Grand Prix. They will run

thousands of simulated scenarios during the

race in the background, McGrath explains, so

when problems occur, the models suggest a

course of action. “You don’t have to sit around

wondering what to do next” he says and adds,

“That’s our ambition as we go into the medical

space. We are at the early phase of a very long

road, but we’re achieving very promising results.”

Conclusion

Precision medicine is fueled by emerging

forms of collaboration and new feedback loops

between the processing power of machines

and human intellect. The quality of interface

and visualization designs will have a big impact

on the success or failure of these interactions

and outcomes. By thinking deeply about

design, precision medicine can reach the level

of resolution it needs to fulfill its promise.

PROBABILITIES PREDICTION OUTCOME

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Minds, Machines, and Monte Carlo – New

Drivers for Medicine

Some of the best drivers of innovation arise by

taking perspectives from one field and applying

them to another. That’s been a recurring theme

for Geoff McGrath, a vice president of McLaren

Applied Technologies, a high performance

design and engineering company. The company

has branched out from its roots in the world

of Formula One (F1) racecars. For example,

McLaren has teamed up with pharmaceutical

company GlaxoSmithKline on a range of

R&D initiatives, including data analytics and

visualization. “For me,” McGrath says, “going

into healthcare was not a great leap – we don’t

want to limit ourselves to racing technology

per se and embrace a range of other technologies

from other sources and bring them to

market in a fast track sort of way.” He says

they don’t separate drivers from the product

that they drive and takes a person-centered

design approach. “You have to put the humans

first, then design and engineer around them,

and keep the designers in the process to make

changes based on feedback.” The way we feed

the models is by weaving intelligence [e.g.,

sensors] into the product and then feeding

that intelligence back to the design team

which, in turn, feeds the models that can then

be more prescriptive for the users. This is the

philosophy we’ve managed to translate to a

whole range of product types, including medical

devices. It doesn’t matter if the product is an

inhaler or other drug delivery system,” he

says. “It has intelligence built in to provide

information about how customers are using the

product and how they might get more from it.”

Design Rules of the Road

McGrath and his colleagues use a number of

design heuristics in their work. For example,

in the racing context, he says, participants don’t

have much time to think so the displays need

to “crisply suggest the best course of action or

intervention.” He adds, “The design challenge

is capturing and then presenting quickly the

answers to questions like - Where am I? How

McGrath continues, “That’s why I like the idea

of predictive or even prescriptive analytics -

predictive meaning if you carry on this way,

this outcome is likely.” There’s a program that

they’re working on that addresses this issue

in the context of a neonatal intensive care for

premature infants. “These patients are very

difficult to monitor, so you need to establish a

baseline for the individual child and then

try and spot the anomalies,” he says. Following

from that, anomaly alerts should be designed

so that they optimize the nurse’s time. Most

hunter Whitney The Journal of Precision Medicine

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