This document discusses using analytics to track, monitor, and reduce costs in the healthcare industry. It begins with learning objectives about identifying misuse and abuse, using analytics to identify trends, how pharmacy benefit managers can use analytics, and predictive analytics in workers' compensation. The document then discusses challenges claims managers face with long-acting opioid prescriptions costing much more. It identifies 5 key problems claims managers encounter and how Rx Intelligence analytics can help address these problems by flagging potentially problematic claims early. The impact of successful peer-to-peer conversations on reduced prescription fills is also demonstrated.
Using analytics to_track_monitor_and_reduce_costs_final
1. Using
Analy+cs
to
Track,
Monitor,
and
Reduce
Costs
Anne
Kirby
Chief
Compliance
Officer
and
Vice
President,
Medical
Review
Services,
Rising
Medical
Solu+ons
James
Masingill
Vice
President,
Claims
Opera+ons,
Market
First
Comp
Insurance
Company
Joe
Anderson
Director
of
Analy+cal
Services,
Progressive
Medical
Dr.
Robert
Hall
Medical
Director,
Progressive
Medical
2. Learning
Objec<ves
• Iden+fy
warning
signs
of
misuse
and
abuse
and
how
claim
managers
can
take
ac+on.
• Tell
how
payers
can
use
effec+ve
analy+cs
to
iden+fy
relevant
trends.
• Explain
how
Pharmacy
Benefit
Managers
can
use
analy+cs
with
strong
clinical
programs.
• Describe
the
role
and
benefits
of
predic+ve
analy+cs
in
the
workers’
compensa+on
industry.
3. Disclosure
Statement
• Anne
Kirby
has
no
financial
rela+onships
with
proprietary
en++es
that
produce
health
care
goods
and
services.
• James
Masingill
has
no
financial
rela+onships
with
proprietary
en++es
that
produce
health
care
goods
and
services.
• Joe
Anderson
has
no
financial
rela+onships
with
proprietary
en++es
that
produce
health
care
goods
and
services.
• Robert
Hall
has
no
financial
rela+onships
with
proprietary
en++es
that
produce
health
care
goods
and
services.
3
4. Using
Analy<cs
to
Track,
Monitor,
and
Reduce
Costs
Anne
Kirby,
RN
Chief
Compliance
Officer/VP
of
Medical
Review
Services
Rising
Medical
Solu+ons
5. Accepted
Learning
Objec+ves
1. Iden+fy
warning
signs
of
misuse
and
abuse
and
how
claim
managers
can
take
ac+on.
2. Tell
how
payers
can
use
effec+ve
analy+cs
to
iden+fy
relevant
trends.
3. Explain
how
Pharmacy
Benefit
Managers
can
use
analy+cs
with
strong
clinical
programs.
4. Describe
the
role
and
benefits
of
predic+ve
analy+cs
in
the
workers’
compensa+on
industry.
7. Challenge
for
Claims
Claims
with
long-‐ac+ng
opioid
Rx
cost
9.3
+mes
more
than
claims
without
(Journal
of
Occupa+onal
&
Environmental
Medicine)
• Very
manual
process
• Case
selec+on
not
always
on
target
• Trea+ng
physicians
and
pain
mgmt
peer
reviewers
used
drug
names
inconsistently
• If
a
person
was
taking
1
or
2
opioids,
it
was
likely
they
were
taking
upwards
of
7
or
8
other
drugs
8. 5
Key
Problems
1. Difficult
to
iden+fy
claims
with
ques+onable
drug
use
before
cases
turn
into
large
losses
2. Too
+me
consuming
for
adjuster
to
find
at-‐risk
cases
3. Not
enough
to
have
a
pharmacist
contact
a
trea+ng
physician
4. Data not
comprehensive
enough –
need integrated approach
5. Viewing
opioids
in
a
vacuum
–
need
to
look
at
other
constella+on
of
drugs
9. Addressing
the
Problems
Rx
Intelligence
Analy+cs
1. Expedites
file
iden+fica+on
2. Flags
poten+ally
problema+c
claims
early
3. Adds
another
level
of
interven+on
4. Looks
beyond
just
opioids
5. Uses
data
to
intervene
11. Demonstrated
Impact
Effect
of
successful
peer-‐to-‐peer
conversa+on
(between
pain
management
physician
and
prescribing
physician)
Fills
before
interven<on
Fills
aFer
interven<on
12. Demonstrated
Impact
• Decreased
Rx
Refills
within
6-‐8
months
of
Peer-‐to-‐
Peer
Review
65%
Claims
• Decreased
Opioid
Rx
Refills
71%
Claims
• Decrease
of
All
Injury
Related
Drugs
• Opioids,
Muscle
Relaxants,
Hypno<cs
&
57%
An<-‐Anxiety
meds
Claims
13. Connec+ng
the
Dots
Where
do
we
go
from
here?
Treati
ng Pain Mgmt
Physi Peer
Clai cian Reviewer
ms UR Nurse
Pers
on
PATI
Pharmac TCM
ENT
y Benefit Nurse
Mgr Clinical
Pharmaci
st
14. Using
Analy<cs
to
Track,
Monitor,
and
Reduce
Costs
Jamey
Masingill
Vice
President
of
Claims
Markel-‐FirstComp
Insurance
15. Accepted
Learning
Objec+ves
1. Iden+fy
warning
signs
of
misuse
and
abuse
and
how
claim
managers
can
take
ac+on.
2. Tell
how
payers
can
use
effec+ve
analy+cs
to
iden+fy
relevant
trends.
3. Explain
how
Pharmacy
Benefit
Managers
can
use
analy+cs
with
strong
clinical
programs.
4. Describe
the
role
and
benefits
of
predic+ve
analy+cs
in
the
workers’
compensa+on
industry.
18. Priming
the
Pump
by
Extrac+ng
“Old
School”
Thinking
from
the
Claims
Environment
• There
is
no
right
or
wrong…only
grey
• Reduce
ac+vity
checks
and
surveillance
• Targeted
and
directed
case
management
• Own
your
data
– Driven
down
to
unit
and
individual
levels
• Adherence
to
established
best
prac+ces
• Valida+on
process
22. Notes
Only
Presenta+on
Outline:
• Preparing
the
claims
environment
before
implemen+ng
your
program.
Analy+cs
and
program
will
only
be
effec+ve
if:
– Extract
“old
school”
thinking
from
claims
processing
– Reduce
ac+vity
checks
and
inves+ga+ons
– Redeploy
those
resources
into
added
medical
exper+se
/
interven+on
tools
• Using
claims
triangles
to
track
and
improve
performance
• Importance
of
integrated
approach
from
mul+ple
angles
to
effec+vely
tackle
prescrip+on
drug
problem
• Impact
on
overall
costs
23. Using
Analy<cs
to
Track,
Monitor,
and
Reduce
Costs
Joe
Anderson,
Director
of
Analy<cs
Robert
Hall,
MD,
Medical
Director
Progressive
Medical,
Inc.
24. Learning
Objec<ves
• Iden+fy
warning
signs
of
misuse
and
abuse
and
how
claim
managers
can
take
ac+on.
• Tell
how
payers
can
use
effec+ve
analy+cs
to
iden+fy
relevant
trends.
• Explain
how
Pharmacy
Benefit
Managers
can
use
analy+cs
with
strong
clinical
programs.
• Describe
the
role
and
benefits
of
predic+ve
analy+cs
in
the
workers’
compensa+on
industry.
26. What
Is
Predic<ve
Analy<cs?
Predictive Analytics is making decisions with statistics and data.
Company
Goal
of
predic<ve
analy<cs
Result
Target
Iden+fy
new
mothers
as
quickly
as
Delivered
coupons
to
young
possible
to
get
them
in
the
habit
of
mothers
before
their
family
even
shopping
at
Target.
knew
they
were
expec+ng.
Nemlix
Determine
which
movies
customers
Improved
their
predic+ons
by
10%;
will
like
based
on
what
they
have
a
$1
million
prize
was
awarded.
already
rated.
Oakland
Choose
the
best
baseball
players
20
consecu+ve
wins;
the
book
and
Athle+cs
available
for
the
next
season,
with
a
film
Moneyball
are
based
on
this.
limited
budget.
Sources:
Duhigg,
C.,
How
Companies
Learn
Your
Secrets,
The
New
York
Times
Magazine.
2012
February
16
Lohr,
S.,
A
$1
Million
Research
Bargain
for
NeElix,
and
Maybe
a
Model
for
Others,
The
New
York
Times,
2009
September
21
Mahler,
J.,
Smaller
Markets
and
Smarter
Thinking,
The
New
York
Times,
2011
October
14
27. How
Can
We
Use
It?
• As
a
PBM,
we
see
some
of
the
data
going
through
the
system,
but
not
all
of
it.
• Each
company
in
the
industry
can
use
analy+cs
with
their
own
data:
– Imagine
if
Nemlix
wants
to
know
whether
you’ll
enjoy
the
movie
Moneyball
– Nemlix
doesn’t
know
if
you
have
read
the
book
Moneyball,
if
you
studied
sta+s+cs
or
if
you’re
an
Oakland
Athle+cs
fan
– They
do
know
if
you
like
other
baseball
movies,
other
Brad
Pir
movies
and
other
movies
based
on
nonfic+on
books
Image source: http://www.managedcaremag.com/archives/1208/1208.pbm-functions.html
28. The
Problem
A
solu<on
is
needed
that
reduces
prescrip<ons
most
efficiently.
Prescrip<on
Drug
Deaths
and
Time
Constraints
on
Nurses,
Increasing
Costs
Adjustors,
Clinicians
• More
people
are
dying
from
• Cannot
examine
or
intervene
on
prescrip+on
drug
use.
every
claim
• Prescrip+on
drug
prices
are
rising.
• Cannot
determine
which
claims
will
• Workers’
compensa+on
in
par+cular
have
high
long-‐term
costs
has
seen
increases
in
use
of
• Too
many
“false
posi+ves”
from
prescrip+on
pain
killers.
individual
clinical
triggers
(i.e.
only
10%
of
claims
with
morphine
equivalence
of
90mg
result
in
high
long-‐term
costs)
29. The
Solu<on:
Mul<variate
Sta<s<cal
Model
to
Predict
High-‐Cost
Claims
Our
original
model,
since
refined:
Correlate
early
data
…
with
resul<ng
long-‐
about
an
injured
term
spend
of
that
worker…
injured
worker.
Workers
injured
in
2007
Resul+ng
pharmacy
costs
in
2009-‐2010
30. Data
Used
in
Sta<s<cal
Models
100%
90%
80%
70%
Pharmacy
Behavior:
Medica+ons,
Percent
of
Number
of
Prescribers,
Number
of
Significance
60%
Pharmacies
(Aggregated
across
mul<ple
Injury:
Body
part,
nature
of
injury
variables)
50%
Prescriber:
Demographics
of
trea+ng
40%
prescriber
30%
Geographic
and
Other
Demographics
20%
10%
0%
1
4
6
9
12
18
24
Months
Since
Date
of
Injury
31. The
Risk
Score
Claim
Risk
Score
Reason
Allison
6.5
Mul+ple
Neck
Injury,
High
Total
Medica+on
Use
(Including
Narco+cs)
Bob
5.4
Con+nued
Medica+on
Use,
High
Risk
Prescriber:
Allergy
and
Immunology
Specialist
Cindy
5.0
Mul+ple
Prescribers
in
Early
Months,
High
Days
Supply
of
Various
Medica+ons
Dwayne
4.5
High
Risk
State
and
Moderate
Injury
Risk:
Dislocated
Disc
Elaine
3.9
Prescriber
Risk:
Pain
Management
Specialist,
High
Narco+cs
Use
To-‐Date
Frank
3.1
Moderate
Injury
Risk,
Demographic
Risk,
and
Prescriber
Risk:
Pain
Management
Specialist
32. Predic<ons
Become
Interven<ons
• Types
of
clinical
interven+ons:
• Claims
Professional
Outreach
• Physician
Outreach
• Drug
U+liza+on
Evalua+on
• Peer-‐to-‐Peer
Review
• Interven+ons
should
be
completed
as
soon
as
possible
to
avoid
any
developing
complica+ons.
33. Measuring
Effec<veness
Statistical Confidence that
Intervention Changes this Outcome
100%
90%
96%
80%
70%
70%
60%
50%
55%
40%
30%
20%
10%
0%
Cost
per
Claim
Morphine
Equivalence
per
Claim
Prescrip+ons
per
Claim
34. Analy<cs
From
a
Provider’s
Perspec<ve
• Finding
common
ground
with
analy+cs
and
providers
• Embracing
challenges
that
can
arise
with
analy+cs
35. Common
Ground
–
Data
Collec<on
• Personal
medical
history
• Family
history
• Social
history
• Physical
examina+on
• Diagnos+c
studies
36. Common
Ground
–
Risk
Assessment
Stroke
Modifiable
risk
factors
Non-‐modifiable
risk
factors
• High
blood
pressure
• Age
• Atrial
fibrilla+on
• Gender
• High
cholesterol
• Race
• Diabetes
• Family
history
• Atherosclerosis
• Previous
stroke
• Circula+on
problems
• Fibromuscular
dysplasia
• Tobacco
• Alcohol
• Patent
foramen
ovale
• Physical
inac+vity
• Obesity
Source: National Stroke Association, Am I at Risk for a Stroke? Stroke Risk Factors. 2013 March 18
37. Common
Ground
–
Outcome
Predictors
Stroke
• Poor
strength
recovery
predictors
– Severe
arm
weakness
at
onset
of
stroke
– No
hand
strength
4
weeks
aLer
stroke
• 30-‐day
mortality
– EKG
abnormali+es
– Brainstem
stroke
– Elevated
blood
glucose
in
non-‐diabe+c
pa+ents
Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation Board
Review. New York. Demos Medical Publishing. 2004
38. Common
Ground
–
Outcome
Predictors
Stroke
• Nega+ve
predictors
for
return
to
work
– Low
Barthel
Index
score
• Ac+vi+es
of
daily
living
– Prolonged
length
of
stay
in
rehabilita+on
– Aphasia
(language/communica+on
deficits)
– Prior
alcohol
abuse
Source: Zorowitz, R., Baerga, E., Cuccurullo, S., Stroke Rehabilitation, Physical Medicine and Rehabilitation Board
Review. New York. Demos Medical Publishing. 2004
39. Common
Ground
–
Language
• Data
collec+on
• Risk
assessment
• Risk
factors
• Outcome
predictors
• Interven+ons
• Behavior
• Effec+veness
40. Embracing
Challenges
Avoid
Blame
• Comprehensive
claim
evalua+on
• Interven+ons
may
need
to
be
mulNfaceted
41. Embracing
Challenges
Validate
Success
• Hill
Physicians
Medical
Group
– 2,200
physicians
– 332,000
pa+ents
– Predic+ve
modeling
• Management
of
chronic
diseases
– Prospec+ve
Risk
Score
• Likelihood
of
pa+ent
using
physician
resources
in
future
• RNs
are
assigned
to
call
pa+ents
with
high
risk
scores
Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality
and Reduce Costs, The Commonwealth Fund. 2009 March
42. Embracing
Challenges
Validate
Success
0.5
x
In-‐pa+ent
days
over
last
365
days
In-‐pa+ent
days
over
last
90
days
+
2
x
ER
days
over
last
365
days
ER
days
over
last
90
days
2
x
(Prospec+ve
Risk
Score
+
adjustment
factor)
= Priority
Score
Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality
and Reduce Costs, The Commonwealth Fund. 2009 March
43. Embracing
Challenges
Validate
Success
• Diabe+c
pa+ents
– High
Priority
Score
– Contacted
by
nurse
case
managers
– Reminders
for
screenings
• Eyes
• Kidneys
• Cholesterol
– Counseling
with
diabetes
educator
Source: Emswiler, T. and Nichols, L., Hill Physicians Medical Group: Independent Physicians Working to Improve Quality
and Reduce Costs, The Commonwealth Fund. 2009 March
44. Embracing
Challenges
Be
Responsive
• A
provider’s
ques+ons
– Is
my
prac+ce
style
being
ques+oned?
– Will
the
care
of
my
pa+ents
be
affected?
– Where
is
the
evidence?
– Why
now?
45. Embracing
Challenges
Reward
Posi<ve
Outcomes
• Should
providers
be
rewarded?
– Pay
for
performance
• Physician
payments
at
the
group
level
(not
individual)
• Mee+ng
absolute
benchmarks
• Soon
auer
performance
period
– Preferred
provider
status
• Recogni+on
• Increased
referrals
Source: Gamble, M., GAO: 3 Ways CMS Can Incentivize Physicians Like Private Payors, Becker's Hospital
Review, ASC COMMUNICATIONS. 2012 January 7; 2013 March 11
46. Takeaways
• Common
ground
– Data
collec+on
– Risk
assessment
– Outcome
predictors
– Language
• Embracing
challenges
– Avoid
blame
– Validate
success
– Be
responsive
– Reward
posi+ve
outcomes