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Population Health Management


To lower health costs, physician networks and medical homes must employ a closed loop population management program that focus on patient SOH stratification, chronic disease management, care coordination and incentive management. This approach will enable them to consistently reduce ER and inpatient admissions, which are the greatest expenditures in health care today. – PowerPoint PPT presentation

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Title: Population Health Management

Population Health ManagementReal Time State of
Health Analysis
  • For years, healthcare insurance companies
    (payers) have mined claims data for chronic
    patients and have built predictive models to
    identify high-risk patients.
  • While this approach has seen some success,
    limitations far outweigh merits.
  • Data used by payers to flag high risk patients is
    historical claims data primarily costs,
    admissions, and diagnoses. Furthermore,
    regression and time series risk models are
    typically updated only annually.

  • Most physicians are highly skeptical of claims
    based predictive models because they have no
    clinical basis, and give no consideration to an
    individual's current state of health.
  • Moreover, there is a complete lack of causation,
    "Why is a patient considered high-risk? What are
    the clinical reasons for the score? How do we
    lower the patient's risk score? How does the
    score measure the effectiveness of my care
    management program?
  • http//healthcarecostmonitor.thehastingscenter.org
  • http//www.ahrq.gov/research/ria19/expendria.htm

  • These models lack a correlation to clinical
  • Claims-based risk scores are created with
    regression analysis at a population level to
    predict scores at the patient level.
  • Not only are todays calculations unsuitable for
    determining a patients true risk, they provide
    no insight on how an individuals score improves
    or deteriorates after each clinical visit.

  • Current thinking and efforts create a
    disproportionate focus on existing chronic
  • A better approach is to monitor all patients,
    healthy and chronic, for risk of
  • Unfortunately, current claims-based predictive
    risk models allow no room for this approach.

  • Today, most large physician groups and medical
    homes already use at least a basic EHR system.
  • CMS predicts that by 2014, more than fifty
    percent of all eligible medical professionals in
    the U.S. will use EHR.
  • This is a transformational shift, because for the
    first time in history, clinical information is
    digitally available in real time, with reasonable
    availability of laboratory results and patient
    vital data.

  • Using real-time clinical data from EHR records,
    health care providers now have the capacity to
    design a closed-loop population care management
    program (Figure 1). A well-designed program
    delivers primary care to drive higher quality,
    reduce costs, and deliver greater
  • value in health care.

Population SOH Stratification
  • State of health stratification provides
    actionable and measurable information about
    actual health status at the population and
    patient levels, with visibility of controllable
    and non-controllable factors.
  • SOH is a risk predictor. However, it is also an
    indicator of the quality of care delivered.
  • If the score trends down, the quality of care is
  • because health is improving.

Origins of SOH Models
  • Nationally accepted clinical models are the basis
    for state of health models.
  • SOH scores are calculated at the patient level
    and rolled up to a population level (Figure 2).
  • In this example, each row corresponds to a
    physician's patient population. It shows the
    patient count, the number of office visits
    (encounter) and the average population SOH score
    for each chronic disease.

Figure 2 Population SOH (Risk) Stratification by
Chronic Disease Management
  • Patients who comply with prescribed care programs
    are typically more successful in managing chronic
  • This is where care coordinators play an important
  • Monitoring gaps in care established by
    evidence-based care, patients SOH trends, and
    underlying clinical drivers over time, care
    coordinators can identify patients that need
    their attention.

Care Coordination
  • Physicians who improved the state of health for
    their population (i.e. lower the score) over a
    one to three year period established and used
    better clinical protocols (i.e. best practice
    care management programs).
  • In one instance, one physicians CHF population
    risk increased to 55, while anothers dropped to

Figure 3 - Effectiveness of two physician CHF
Use best practices within the risk group for
evidence-based care coordination medicines,
treatment levels, frequency of visits by risk
Population performance Map patients on quality
and total cost across the continuum-of care
(ambulatory and acute). Identify optimal
preventive care levels to minimize lifecycle cost
over a time period by chronic condition.
Incentive management
  • If financial incentives for health care
    professionals are not aligned with performance,
    success may be temporary and hard to sustain.
  • Effective incentive programs distinctly drive
    higher quality and reduce costs for greater value
    in health care.
  • Incentive programs reward care teams for reducing
    population risk scores, improving patient
    satisfaction scores, and reducing overall patient

  • Continuum of care dashboards (ambulatory and
    acute) are useful in designing incentive programs
    and illustrate risk-cost-quality details for each
    patient (Figure 5).
  • Figure 5 - Continuum of Care Analysis by Patient,
    Preventive Care Impact on Acute Care Costs

Monitor how much total inpatient and outpatient
care (cost and quality) is being provided to the
risk panel identify patient outliers.
  • Patient SOH scores can be rolled up to population
  • For example, one incentive program dashboard maps
    physician/care coordinator teams on a
    cost-quality grid.
  • Each bubble corresponds to a specific physician-
    care coordinator team, and the size of the bubble
    illustrates the size of the population they
    manage. The distance of each bubble from the
    crosshair indicates the positive or negative
    variance from the target and is proportional to
    each teams bonus or penalty.( Refer Fig.6)

  • Figure 6 Physician value index used for
    incentive management for care teams.

Report shared savings by plan by physician on a
periodic basis and show the impact of actions on
their pocketbook.
Validating the SOH Model APPROACH
  • To validate the models, researchers compared the
    new SOH model against that of a leading
    claims-based risk model (the payer model).
  • For the SOH model, researchers used real-time
    clinical data. The SOH model did not include past
    ER or IP admissions data.
  • Next, researchers calculated a SOH score for each
    patient using historical data over two years

Inpatient Admissions
  • Figure 7 shows total hospitalized patients as a
    ratio of the total diabetic patients for that SOH
  • At very high scores, all patients were
    hospitalized. Thus, Figure 7 validates the
    accuracy and predictive power of the SOH score.
  • Figure 7- Ratio of Hospitalized Patients to Total
    Diabetic Patients

Creating a SOH Composite
  • Figure 8 shows the
  • relationship between
  • the payer risk scores and IP admissions.
  • Similarly, at higher risk scores, the predictive
    power of the payers model
  • is only 50 whereas the researchers SOH model is
    closer to 100 accurate

Figure 8 - Relationship between the payer risk
scores and IP admissions.
  • State of health models are highly accurate and
    predictive, and ideally suited for chronic care
    population management by chronic condition.
  • Using SOH scores, care coordinators can correctly
    identify and focus on high risk patients with a
    great risk of hospitalization in the short term.
  • Given the rapid adoption of EHRs among primary
    care physicians and groups, the data required to
    build SOH models is readily available now, and
    will continue to expand over the next two years.

  • Healthcare providers can enable continuous
    improvement using SOH models together with care
    management programs. This approach has already
    been institutionalized in a number of leading
    medical homes like Medical Clinic of North Texas

  • MCNT has pioneered the SOH-based population
    management approach.
  • MCNT experienced a stellar FY 2010 performance
    with Total Medical Cost trend.
  • Overall performance index improved in Facility
    Outpatient (-5), Other Medical Services (-6),
    and Professional (-1) categories, relative to
    the market. An enviable performance considering
    the challenges healthcare provider markets are
    facing with the influx of market changes.

  • To lower health costs, physician networks and
    medical homes must employ a closed loop
    population management program that focus on
    patient SOH stratification, chronic disease
    management, care coordination and incentive
  • To become masters in their population management
    programs, they need decision support systems such
    as population SOH (risk) stratification and
    predictive models.
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