risk-segmentation-david-gifford david gifford achrf 2012
TRANSCRIPT
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Risk Segmentation in Recovery8 November 2012
David Gifford, Transport Accident Commission, Victoria
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Contents
1. Overview of TAC2015 and Recovery
2. Increased focus on Risk Identification andSegmentationspecific changes
a. Recovery Phase 1
b. Recovery Phase 2
c. Service Program
3. Results to date and Key Learnings
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Contents
1. Overview of TAC2015 and Recovery
2. Increased focus on Risk Identification andSegmentationspecific changes
a. Recovery Phase 1
b. Recovery Phase 2
c. Service Program
3. Results to date and Key Learnings
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TAC 2015 Strategy In June 2009 The TAC Board approved the
TAC 2015 strategy which centred around a
transformation in Claims Management One of the key aspects of this was the
introduction of a third strategic objective Client Outcomes as another indicator ofsuccess in addition to Client Experienceand Scheme Viability (i.e. financialperformance)
The Transport Accident Act provides abackdrop to the 2015 strategy and theTACs ongoing commitment to improvingits service to clients.
Supporting clients to return to work and health as quickly as possible
In a practical sense, this meansmoving away from merely being a
passive payer of services, towards amore active role in helping clientsachieve better outcomes.
Janet Dore, CEO
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Recovery covers the TACs
minor to moderate injuries
Recovery
-97% of New Claims(~15,000-16,000 p.a.)
-90% of Active Claims(~24,000-25,000)
-65% of AnnualPayments IncludingCommon Law (~$550m)
-30% of OutstandingClaims Liabilities(~$2.5bn - $800m NoFault and $1,700mCommon Law)
TAC
Independence
- 3% of New Claims
-70% of Outstanding ClaimsLiabilities
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Contents
1. Overview of TAC2015 and Recovery
2. Increased focus on Risk Identification andSegmentationspecific changes
a. Recovery Phase 1
b. Recovery Phase 2
c. Service Program
3. Results to date and Key Learnings
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The Journey
BENEFIT DELIVERY RECOVERY
Pre 2010 Oct 10
BenefitDelivery
No automatedriskidentification
Claims movemanually due tocombination oflife of claimevents and
some datatriggers
Dec 11
Jun 12 2012-2014
Recovery Phase 1Autosegmentation
of claims to teamsbased on datacollected at claimacceptance
Other claimmovements basedon outcomes andchange in claim
status rather thanlife of claim
Recovery Phase 2Pre Claim
Intervention
ClientConversational Tool(revised)
Other changes inclaims managementstrategies
ServiceFirst Service
eligibility andincome decisions
StreamlinedDecisions lessmanual review ofdecisions, more riskbased reviews (postpayment)
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The Benefit Delivery Model
M a i n t e n a n c e
o n l y
V o c a t i o n a l ( i n c o m e s u p p o r t r e q u i re d )
N o n - v o c a t i o n a l (n o i n c o m e s u p p o r t r e q u i r e d )N o n - v o c a t i o n a l (n o i n c o m e s u p p o r t r e q u i r e d )
S o f t t i s s u e
O r t h o p a e d i c
L o n g h o s p i ta l
L o n g h o s p i t al
O t h e r ( E a s t )
O t h e r ( W e s t )
S t a n d a r dC a p a c i t y
S t a n d a r dR T W
( 1 0 m t h +C a p a c i t y
S t a n d a r dR T WC a p a c i t y
E a r l y
i n te rven t i onS t a n d a r d L o n g t a i l
E a r l y
i n te rven t i onS t a n d a r d L o n g t a i l
E a r l y
i n te rven t i onS t a n d a r d L o n g t a i l
Q u i c k
r e c o v e r y
team
E a r l y
i n te rven t i on
R T W
( 1 0 m t h + )
E a r l y
i n te rven t i on
I n c o m et e a m
P h a r m a c y t e a m
M i n o r
(Bene f i t de l i ve ry c l i en t suppor t )
M o d e r a t e
(Bene f i t de l i ve ry r i sk teams)
O v e r
5 0 %
O v e r
5 0 %
Previous model had a significant number of claim movements
associated with life of claim (i.e. claim duration) rather than claim
outcomes
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The Recovery Pathway
The Recovery Model
places the client
with the team bestequipped to help
them with their
individual needs
900 claims/portfolio
40 claims /
portfolio
Complex RTW50
Less-ComplexRTW 110
Complex RTH95
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Recovery Segmentation ModelNo Fault Model
Logistic regression model
Target variable is probability of claimreceiving services 6 months post injury
Most significant factors are injury,
employment status and hospitalisationTotal of seventeen variables used in the
model
Common Law Model
Logistic regression model
Target variable is probability of claimultimately lodging a Common Law application
Most significant factors are fault status,injury (including presence of psych injury),
employment status and hospitalisationTotal of nine variables used in the model
Combined Model
Weighted average of No Fault and Common Law models
Thresholds can be adjusted to control claims moving into active management teams
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Algorithm ProcessC l a i m a c c e p t e d b y E l ig i b il it y t e a m
C l a im a u t o m a t i c a ll y m o v e s t o T o B e
S e g m e n t e d P o r t f o li o
D a t a e x t r a c t e d f ro m t h e c la i m s s y s t e m
i n t o S A S
S c h e d u l e d p r o c e s s r u n s in S A S
L i st p r o d u c e d o f c l a im s a n d t h e i r
a p p r o p r i a t e t e a m
L i s t f e d b a c k i n to c l a im s m a n a g e m e n t
s y s t e m
C l a im s m o v e d t o n e w t e a m
S e n i o r o f f ic e r s in t e a m s m o v e c l a i m s t o
a p o r t f o l io i n t h e i r t e a m
D a y o f a c c e p ta n c e
O v e r n i g h t fo l l o w i n g
a c c e p t a n c e
D a y f o ll o w i n g
a c c e p t a n c e
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Allocation of Claims(Day 1 and Post Algorithm)
Common Law Active orPending
Psychology orPsychiatry services in
last 3 months
Pharmacy service in
last 3 months andTreatment in last 3
months Common Law Potential
Not at faultdriver with
solicitor
Payments excludingincome/impairment in
past 6 months >$8,500
1 Y Y Y2 Y Y
3 Y Y
4 Y5 Y Y Y
6 Y Y7 Y Y
8 Y Y9 Y
10 Y
11 Y
12 Y13 Y14
Ranking
Flag
Active Management Complex
Active Management Non-Complex
Client Assist
Slightly less than 50% of claims remained with their previous
claims manager following implementation
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Contents
1. Overview of TAC2015 and Recovery
2. Increased focus on Risk Identification andSegmentationspecific changes
a. Recovery Phase 1
b. Recovery Phase 2
c. Service Program
3. Results to date and Key Learnings
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Phase 2Phase 1 realigned our staff and claims in to the right place to
achieve both the TAC and our clients goals
Phase 2 of Recovery aimed to provide our staff with thetools and pathways to assist complex clients to return to
work as quickly as possible
Pre-claim Contact
Client Conversational Tool - Revised
Recovery Action Plan
Motivational Interviews
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Pre-claim ContactFor claims at risk of poor return to work outcomes, between the
initial telephone interview and actual claim acceptance
Contact the Client to:
Set expectations regarding the role of TAC in assisting theirrecovery
Outline the benefits of early return to workEncourage early return of the claim form
Identify potential barriers to their return to work
Contact the employer to:
Explain their role in the return to work process
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Pre-claim Contact
How are the claims at risk identified?
Predictive model developed by ISCRR
Regression modelling use to identify significant factors forclaims
Receiving any income
Receiving income 3, 6, and 12 months post accident
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Client Conversational Tool -Revised
Aims to identify clients with high needs early in the life of aclaim
Enables claims management to be more appropriatelyaligned to client needs
Initially introduced in October 2010 and enhanced in April2012 (again with help from ISCRR)
How are claims at risk identified?
Structured conversation
Focus on persistent pain, mental health, return to work
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Contents
1. Overview of TAC2015 and Recovery
2. Increased focus on Risk Identification and
Segmentationspecific changesa. Recovery Phase 1
b. Recovery Phase 2
c. Service Program
3. Results to date and Key Learnings
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Service Program - First Service
Aims to improve how quickly and easily we accept a claimand get the initial services and benefits to clients
Identify claims that could be fast tracked througheligibility
Using similar regression models to those used inRecovery Segmentation and Pre-claim Contact
Opportunities to improve Segmentation
Different eligibility process means different data sourcesfor initial Segmentation decision
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Service Program - Streamlined Decisions
Aims to improve how quickly and easily we make decisions aboutservices and benefits
Building on Recovery changes which saw the introduction ofstraight through processing for the Client Assist team
Enables the evolution of the Client Focus Team from supportingClient Assist to make decisions or reviewing more difficult decisionsto reviewing analytically selected files
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Service Program - Streamlined Decisions
Recovery Client AssistExceptions
0
5,000
10,000
15,000
20,000
25,000
Jul-09
Sep-09
Nov-09
Jan-10
Mar-10
May-10
Jul-10
Sep-10
Nov-10
Jan-11
Mar-11
May-11
Jul-11
Sep-11
Nov-11
Service Limits
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Contents
1. Overview of TAC2015 and Recovery
2. Increased focus on Risk Identification and
Segmentationspecific changesa. Recovery Phase 1
b. Recovery Phase 2
c. Service Program
3. Results to date and Key Learnings
R lt
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ResultsIndicator Timing
Algorithm effectiveness
Claims in correct teams
Less manual claim movements
Immediate
(allowing for some bedding
down of the model)
Lead Indicators of Liability Reductions
Less claims on Income (weekly benefits)
Reduced durations for treatment benefits
More active management of claims with Common
Law potential
Fewer Common Law claims/lower settlement
sizes
Within 6-12 months
Within 6-12 months
Within 6-12 months
Within 2-3 yearsImproved client satisfaction Within 6-12 months
Actual liability reductions (actuarial release) Within 2-3 years
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80-90% of claims correctly assigned to light touch Client Assist
team, about 45-50% of claims correctly assigned to Active
Management team, in line with expectations
ResultsAlgorithm Effectiveness:
AccuracyAlgorithm Accuracy
High cost, common law lodged
0%
10%
20%30%
40%
50%
60%
70%80%
90%
100%
Oct-10
Nov-1
0
Dec-1
0
Jan-11
Feb-11
Mar-11
Ap
r-11
Ma
y-11
Jun-11
Jul-11
Au
g-11
Se
p-11
Oct-11
Nov-11
Dec-11
Jan-12
Feb-12
Active Managem ent
% 'Right' Team - Actual
Client Assist
% 'Right' Team - Actual
Active Management
% 'Right' Team - Expected
Client Ass ist
% 'Right' Team - Expected
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About 600-700 claim movements per month (excluding one-off
bulk movements). Reduction of about 30% compared to Benefit
Delivery
ResultsAlgorithm Effectiveness:
Claim Movements
R e c o v e r y
C la im s m o v in g b e tw e e n t e a m s
0
5 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
3 5 0 0
O c t-1 0 D e c -1 0 F e b -1 1 Ap r-1 1 J u n -1 1 Au g -1 1
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Probability of High Cost
g
Reflections and Learnings
Target high risk or target ability to impact?Most likely to be highcost claims
Many of these haveserious injuries
unlikely to be able tobe impacted
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Probability of High Cost
g
Reflections and Learnings
Target high risk or target ability to impact?
The next tier of
claims may representgreater opportunityfor impact
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Reflections and Learnings
A model that does not fit particularly well may be no better than nomodel!
No fault model predicts costs 6 months post accidentreasonablysuccessful
Common Law model tries to predict Common Law claim lodgement (often3 or more years post accident)
This Common Law model used as the trigger for review of claims forCommon Law potential
Review a lot of claims who dont lodgeDont review a lot of claims who do lodge
Fair amount of wasted effort.
A lot more information available at 6 or 12 months post accident. Canpredict much better.
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Reflections and Learnings
It is one thing to identify risk and segment claims to the right team.It is another thing to manage them well once the risk is identified!
Anecdotally at least, greater improvements likely to come from Phase2 (primarily about changes in strategies) than Phase 1 (RiskIdentification)
However Phase 2 would have been a lot less successful withoutPhase 1
The greatest success is likely to come from a combination ofsuccessful Risk Identification and Segmentation and effectiveclaims management strategies and interventions
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Conclusion
TAC is on a journey of using data to identify risk and better manageclaims
The process of identifying risk and using it to manage claims is an
iterative oneThe best outcomes will result from an optimal mix of data (to identifyrisk and segment claims) and judgement.