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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.