Are You Running the Population Management Marathon on One Leg? - PowerPoint PPT Presentation

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Are You Running the Population Management Marathon on One Leg?

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How it feels when you are working very hard and investing millions on population care management programs and the results don’t meet your expectations! Some population care management programs are successful while some are not delivering the expected results. The case study results we are going to share will show you why there are “winners” and “losers” in effective population management programs. We hope that the results we share are not only going to be an “eye-opener” but a “game-changer” as the healthcare providers take on risk for population health. – PowerPoint PPT presentation

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Title: Are You Running the Population Management Marathon on One Leg?


1
Are You Running the Population Management
Marathon on One Leg
  • By Jay Reddy

2
  • When you are working very hard and investing
    millions on population care management programs
    and the results dont meet your expectations!
  • Some population care management programs are
    successful while some are not delivering the
    expected results.

3
  • The case study results we are going to share will
    show you why there are winners and losers in
    effective population management programs.
  • We hope that the results we share are not only
    going to be an eye-opener but a game-changer
    as the healthcare providers take on risk for
    population health.

4
  • VitreosHealth (formerly PSCI) has completed
    multiple population State-of-Health (SOH) risk
    analyses for Medicare ACOs and Medicare Advantage
    programs harvesting their EMR, demographics and
    claims data.
  • Thanks to Centers for Medicare Medicaid
    Services (CMS) for they are the first payer in
    the industry who are sharing the claims data with
    the providers for their patient population.

5
  • Our vision is that the case study below will
    compel all private payers to do so if they want
    to be on the fore-front of healthcare
    transformation.
  • The results we are sharing are a representative
    sample of what we are seeing as a pattern across
    multiple Medicare ACO customers.

6
  • A leading Physician-led ACO used VitreosHealth
    SaaS to perform the population State-of-Health
    (SOH) analyses by running the predictive risk
    analytics which leveraged both EMR and claims
    data.
  • Our predictive models helped identify the risky
    patients, the underlying risk factors and help
    design tailor-made care management programs for
    the high risk cohort of population.

7
VitreosHealth uses a closed-loop provider-driven
population process as shown in Figure 1.
8
  • VitreosHealth received historical claims and EMR
    data for 3-years (2011 2013) from the ACO for
    the Medicare population cohort. VitreosHealth
    cleansed the data and ran the predictive risk
    analytics algorithms to identify the clinical
    risk scores for each patient.
  • The SOH clinical risk score is a composite of the
    individual disease risk scores and is calculated
    from EMR (clinical) data that includes vitals and
    lab results.

9
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10
  • The top right quadrant (Critical) is the cohort
    of high cost, high clinical risk score patients.
    These patients are clinically risky based on the
    current state-of-health and are also high
    utilizers today and account for about 42 of the
    total population spend.
  • The lower right quadrant represents the cohort
    (High Utilizers) that are high utilizers today
    even though they are relatively at lower clinical
    risk based on their state-of-health analysis
    using EMR data.

11
  • Both these segments are typically identified
    through claims analysis in most population and
    disease management programs and become high risk
    candidates for care management programs.

12
  • However, there is a far more important category
    of patients which is the upper left (Hidden
    Opportunity).
  • This cohort comprises of members that are
    clinically at higher risk today based on EMR data
    analysis, but have historically not been high
    utilizers, hence are not identified by claims
    based risk scores that are biased towards
    historical utilization costs.
  • In most cases, they account for only 10 of the
    total spend and have very low PMPM costs, so most
    of these members are ignored by CM programs.

13
  • VitreosHealth performed similar analysis for
    Year 2011 1Q and Year 2012 1Q to understand the
    movement of population over the 12 month period.
  • Figure 3 shows the movement of population from
    Hidden Category to Critical category and The
    Unknowns/Relatively Healthy to High Utilizers
    during this period.

14
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15
  • These similar findings which we are seeing across
    the ACO populations are transformational for
    the care management strategy development and
    execution. Most of the current care management
    programs (See Figure 4) are focused on the
    Critical and High Utilizers categories which
    make up 70 of last year costs.
  • Our analysis points out, 44 of the costs in the
    following year were coming from 17 of Hidden
    and Unknown cohort populations migration into
    the Critical and High Utilizer categories.

16
  • This means that nearly 40 of the costs by the
    end of the year were contributed by members who
    were not being identified by the current care
    management programs at the beginning of the year
    and hence not being cared for proactively.

17
  • So your clinical teams are working very hard,
    investing millions on the care management
    programs that are not focused on the right
    members.!
  • If you dont put the right passengers on the
    right bus with no Future Visibility, the
    population management journey will have a
    destination with undesired outcomes.

18
  • Do you know who these 17 of population in the
    Hidden category and Unknown category in 2013
    that will be migrating to the right by 2014 and
    making up 44 of new costs?
  • What are their risk drivers and what care
    management programs to design for them?

19
  • This is why ACOs cannot drive their population
    management programs using claims-based predictive
    risk analytics and get the desired results.
  • For superior results, ACOs need to use Next
    Generation Population Predictive Models that
    leverage multiple data sources EMR, claims and
    demographics to help identify the high risk
    patients and design tailor-made care management
    programs.
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