Population Health Management

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Population Health ManagementReal Time State of
Health Analysis
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YESTERDAY CLAIMS-BASED PREDICTIVE MODELS

• 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.

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• 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
  /kimberlyswartz/projected-costs-ofchronic-
  diseases/
• http//www.ahrq.gov/research/ria19/expendria.htm

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• These models lack a correlation to clinical
  information.
• 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.

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FURTHER CONSIDERATIONS

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

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VITAL PROGRESS

• 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.

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CLOSED-LOOP CMP

• 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.

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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
  good,
• because health is improving.

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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.

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Figure 2 Population SOH (Risk) Stratification by
Physician
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Chronic Disease Management

• Patients who comply with prescribed care programs
  are typically more successful in managing chronic
  conditions.
• This is where care coordinators play an important
  role.
• 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.

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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
  5.

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Figure 3 - Effectiveness of two physician CHF
populations.
Use best practices within the risk group for
evidence-based care coordination medicines,
treatment levels, frequency of visits by risk
group.
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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.
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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
  costs.

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• 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.
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• Patient SOH scores can be rolled up to population
  averages.
• 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)

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• 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.
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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

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Inpatient Admissions

• Figure 7 shows total hospitalized patients as a
  ratio of the total diabetic patients for that SOH
  band.
• 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

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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.
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WORK SMARTER USING SOH MODELS

• 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.

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• 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).

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• 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.

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SUMMARY

• 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.
• To become masters in their population management
  programs, they need decision support systems such
  as population SOH (risk) stratification and
  predictive models.