1. 4/15/13
TrialIO:
A
Empowering
Investigators
and
Patients
with
Better
Information
Executive
Summary
TrialIO
addresses
the
patient
researcher-­‐matching
problem
by
addressing
the
needs
of
the
researchers,
patient
advocates,
and
caregivers
during
the
trial
planning
process.
Trials
that
are
conducted
with
the
“right
investigator,
at
the
right
location,
at
the
right
time”
have
a
better
chance
of
getting
funded,
fulfilling
recruitment
goals
and
improving
confidence
in
the
study
outcome.
Patients
and
researchers
seeking
to
find
each
other
would
be
empowered
with
better
information
to
start
their
process.
The
ClinicalTrials.gov
web
site
and
derivative
search
engines
excel
at
finding
individual
trial
records,
but
provide
little
support
for
a
time-­‐based
or
“trended”
views
of
clinical
trial
activity
for
a
given
disease,
investigator,
sponsor,
or
geographic
location.
TrialIO
re-­‐imagines
the
ClinicalTrials.gov
data
as
a
vast
spreadsheet
in
the
cloud.
Using
a
web
browser
or
mobile
device:
n Patient
advocates
can
quickly
identify
geographies
that
are
under-­‐
represented
by
clinical
trial
activity
for
a
condition.
n Patients
seeking
investigators
can
build
lists
of
candidate
investigators
for
pitching
their
trial
idea.
n Investigators
seeking
funding
can
see
the
entire
portfolio
of
activity
for
a
sponsor
or
possible
collaborator
trending
over
time.
n Trial
planners
can
see
the
recruitment
history
for
a
condition
over
all
locations.
And,
quickly
see
the
likelihood
that
a
planned
trial
will
face
competition
for
patients
at
a
given
location.
n Sponsors
can
identify
the
best
investigators
based
on
prior
trial
activity.
n The
benefits
of
easy
access
to
aggregate
trial
activity
extend
to
world
health
organizations,
governments,
medical
societies,
disease
foundations,
academia
and
industry.
TrialIO
is
envisioned
as
both
a
web
application
and
a
syndicated
web
service
for
developers.
For
end
users,
anyone
with
access
to
an
Internet
connection
can
access
the
site,
generate
reports
and
share
insights
with
colleagues.
Clinical
trial
matching
is
networking
and
better
information
shared
will
promote
communication
and
dissemination
of
information.
Developers
can
syndicate
the
TrialIO
web
service
to
create
new
applications
using
clinical
trial
data.
By
providing
these
data
services
the
cost
of
application
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Incite
Advisors,
Inc.
2013

2. 4/15/13
development
is
lowered
increasing
availability
of
information
services
for
caregivers
operating
in
lower
income
areas.
Background
The
idea
for
TrialIO
grew
out
of
a
consulting
project
with
a
hospital
organization
in
the
Boston
area.
The
client
was
interested
in
expanding
its
collaborative
activity
in
the
field
of
genomics.
This
led
me
to
two
questions:
1)
who
are
the
potential
collaborators
who
would
be
most
interested
in
collaborations
in
genomics?
And,
2)
how
active
are
the
peer
hospitals
in
the
field?
The
ClinicalTrials.gov
web
site
was
a
natural
place
to
look.
I
found
the
data
there
structured
nicely
for
a
computer
programmer
but
too
voluminous
and
not
easily
fitting
into
the
form
I
wanted
it:
a
spreadsheet.
The
project
was
also
inspired
by
the
Clinical
Trials
Transformation
Initiative
Aggregate
Analysis
of
Clinical
Trials
project
sponsored
by
the
Duke
School
of
Medicine.
Notably
Duke
makes
the
data
available
on
the
ClinicalTrials.gov
web
site.
However,
the
IT
required
downloading,
hosting,
and
maintaining
that
data
is
significant.
A
number
of
commercial
firms
exist,
mainly
to
supply
clinical
trial
business
intelligence
and
analytics
to
pharmaceutical
and
biotech
executives.
IMS
Health
provides
Site
Optimizer.
Citeline
provides
TrialTrove
and
SiteTrove
products.
A
number
of
market
research
providers
offer
reports
on
clinical
trial
pipeline
activity
for
upwards
of
$2,500
per
condition.
The
presence
of
these
commercial
offerings
validates
the
value
proposition
of
TrialIO.
However,
their
business
models
are
prohibitive
for
many
academic
and
non-­‐profit
entities.
Thus,
TrialIO
has
the
potential
to
serve
a
real
market
need
and
is
potentially
disruptive
to
these
businesses.
PCORI
Considerations
Technical
Feasibility,
Usability,
and
Scalability
The
TrialIO
architecture
is
a
proof-­‐point
for
the
application
of
“big
data”
programming
and
database
technologies
in
healthcare.
The
system
uses
the
Apache
open
source
database
CouchDB
and
the
data
is
indexed
using
the
“map-­‐reduce”
paradigm.
The
presentation
of
this
proof-­‐of-­‐concept
implementation
validates
these
technical
choices.
Cloudant,
a
data-­‐as-­‐a-­‐service
company
provides
the
servers
and
storage
hosting
the
project.
Without
these
tools
the
functionality
would
have
been
challenging
to
achieve
and
the
programming
cost
and
IT
infrastructure
needed
would
have
made
the
project
prohibitive.
A
majority
of
the
effort
focused
on
the
development
of
the
indices
and
algorithms
for
managing
complex
queries
and
the
“pivot”
function.
The
map-­‐reduce
computing
paradigm
assures
that
most
of
the
heavy
computation
of
indices
occurs
on
the
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Incite
Advisors,
Inc.
2013

3. 4/15/13
server
during
off-­‐peak
times,
so
there
are
no
scalability
issues
there.
Currently
the
“pivot”
algorithm
runs
in
the
client.
This
method
can
be
computational
so
we
plan
to
move
this
processing
to
the
server
on
the
next
revision
of
the
software.
The
client
maintains
a
cache
of
trial
records
when
bulk
loading
data
from
the
server
to
keep
the
screen
active
without
having
data
from
the
server
over
run
the
client.
Scalability
is
further
insured
by
enforcing
a
‘date-­‐range’
on
all
queries.
By
placing
limits
on
the
time
range,
we
limit
the
number
of
trial
records
the
system
has
to
process
at
once.
Currently
these
limits
are
1,
2,
and
5-­‐year
windows.
We
anticipate
the
need
for
mobile
access
the
web
user
interface
is
created
using
the
responsive
web
design
techniques.
We
are
not
skilled
designers;
we
are
data
architects
so
the
application
will
need
a
user
interface
design
makeover
before
going
into
production.
We
tried
to
minimize
options
and
extra
features
to
keep
users
focused
on
the
spirit
of
the
application.
To
get
the
trial
documents
into
the
system
require
significant
data
cleansing
operations.
One
example
is
a
system
of
classifying
trial
conditions
into
one
of
24
NLM
Mesh
Terms
was
devised
so
that
trial
activities
can
be
grouped
into
“categories”.
Differences
in
the
ways
patients,
caregivers,
and
researchers
interact
The
research
community
will
find
the
spreadsheet
paradigm
the
most
relevant
and
comfortable.
Though,
the
application
requires
no
knowledge
of
Excel,
pivot
tables,
and
the
like.
The
application
can
be
made
more
approachable
to
patients
by
for
example
changing
references
to
conditions
from
“neoplasms”
to
“cancer”
wherever
possible.
A
key
future
requirement
of
TrialIO
is
to
help
caregivers
directly
match
patients
to
trials.
Physicians
treating
patients
who
are
candidates
for
clinical
trials
are
unable
to
spend
time
parsing
updates
to
clinical
trials
to
recommend
to
their
patients.
As
a
result,
many
physicians
don’t
refer
their
patients
to
trials
because
they
don’t
know
about
them1.
With
an
interface
to
the
EHR,
this
process
can
be
automated
and
recommendation
alerts
forwarded
to
physicians
in
a
convenient
manner.
Maximizing
Patient-­‐Centeredness
and
Scientific
Rigor
The
application
anticipates
that
users
will
make
interesting
discoveries
in
the
data
and
want
to
share
their
findings.
To
support
this,
users
will
be
able
to
cut-­‐paste
simple
URL
into
their
email
or
social
media
(Facebook,
Twitter)
accounts.
The
volume
of
discussion
about
clinical
trial
activities
should
increase.
Our
scientific
rigor
is
computer
science.
Before
going
into
production,
the
system
will
need
extensive
testing
and
validation
of
results
against
some
hand
calculations
1
The
Project
IMPACT
Experience
To
Date:
Increasing
Minority
Participation
and
Awareness
of
Clinical
Trials
3
Copyright
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Incite
Advisors,
Inc.
2013

4. 4/15/13
to
verify
the
results.
We
version
our
software
and
develop
tests
to
confirm
that
software
quality
is
maintained
as
new
features
are
added
to
the
system.
Serving
“hard
to
reach”
audiences
Our
model
for
extending
our
reach
is
to
syndicate
our
feeds.
The
proliferation
of
clinical
trial
searching
sites
on
the
Web
is
evidence
of
the
demand
for
this
type
of
information.
By
syndicating
our
data
feeds
we
can
lower
the
cost
of
software
development
so
that
the
barriers
to
better
information
are
lowered
for
organizations
serving
hard
to
reach
audiences.
Usage
The
prototype
version
of
TrialIO
launches
to
a
dashboard
of
aggregated
clinical
trial
counts
grouped
by
Disease.
Dashboard
Figure
1
-­‐
Sample
Dashboard
The
three
main
navigation
options
are
Dashboard,
Explore,
and
Share.
Users
can
navigate
from
the
Dashboard
to
begin
their
exploration
of
the
data,
or
move
to
the
Explore
menu.
Explore
Most
users
will
move
straight
to
EXPLORE
where
they
can
select
from
a
menu
of
pre-­‐computed
indexes
such
as
Disease,
Sponsor,
or
Location.
It
will
be
possible
to
expand
this
list
to
include
combinations
of
indexes
such
as
Sponsors-­‐Collaborators
to
allow
comparative
analysis.
Also,
it
will
be
possible
to
index
complex
data
types
in
the
clinical
trials
archive
such
as
Study
Design.
The
functionality
will
benefit
from
a
deeper
understanding
of
the
research
investigators
use
case.
See
figure
2
below.
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Incite
Advisors,
Inc.
2013

5. 4/15/13
Share
(not
yet
implemented)
The
sharing
paradigm
returns
a
URL
for
each
report
or
graph
generated.
Users
can
book
mark
and
share
these.
Figure
2
-­‐
EXPLORE
output
for
"Conditions".
Users
can
choose
a n
interesting
condition
and
see
an
aggregation
of
"locations"
for
that
disease.
About
Incite
Advisors,
Inc.
Incite
Advisors,
Inc.
is
a
consulting
business
focused
on
data
driven
web
applications
for
healthcare
and
life
sciences.
We
offer
strategy
consulting
and
web
data
services.
We
serve
life
science
vendors,
pharmaceutical
and
biotech
enterprises,
and
healthcare
provider
institutions
worldwide.
Our
offices
are
in
Worcester,
Massachusetts.
Contact
Information:
Incite
Advisors,
Inc.
19
Goddard
Drive
Auburn,
MA
01501
www.inciteadvisors.com
Ph.:
(508)
254-­‐8349
5
Copyright
-­‐
Incite
Advisors,
Inc.
2013