Predictive modeling can help identify beneficiaries with the highest risk and capture those individuals with emerging risks
that otherwise might missed. Coupling predictive modeling with integrated care management and appropriate technology can help
case workers ensure Medicare beneficiaries receive efficient, high quality, cost effective healthcare.
Why use predictive modeling? Programs working with Medicare beneficiaries are being challenged to go beyond traditional utilization,
case and disease management services with limited clinical resources. To meet that challenge, programs need innovative ways
to identify beneficiaries in the population not only with costly chronic conditions but also those at the greatest risk for
adverse outcomes. Predictive modeling tools can analyze data using complex logic and rules for millions of beneficiaries in
a short period of time. These tools offer standardized methodologies that yield consistent, valid results over time.
While traditional case identification methods attempt to assess a beneficiary's current risk status, predictive modeling looks
at future risk and outcomes. Care opportunities identified by predictive modeling can guide proactive evidence-based care
planning for more effective delivery and utilization of services.
Most predictive modelers are heavily dependent on medical and pharmacy claims data. It is well known that not all claims are
created equal; for example incomplete or inaccurate coding, bundling or up-coding, and lags in submission may occur. Relying
on information from claims data alone for validation of risk can lead to inappropriate allocation of services and missed opportunities
for impact. Integration of key non-claims driven information is necessary to adjust the predicted risk. Considerations include:
- Beneficiary-provided information can be invaluable in identifying risks. Behavioral aspects of a beneficiary, such as self-confidence,
perceived barriers and readiness for change, can have significant impact on a person's ability to actively participate in
management of their health.
- Chronically ill and elderly individuals are at a greater risk for isolation and depression. Offering free access to a Health
Risk Assessment (HRA) for beneficiaries, either on a Web portal or via a mailed paper form, can assist in identifying individuals
with potential socialization or depression identified risks.
- The literacy level of an individual can influence their health outcomes and risk. If a beneficiary cannot understand instructions
or read educational materials given to them on managing their chronic medical conditions, the chance for compliance with treatment
regimes is greatly reduced. Non-compliance increases overall risk of disease progression and less than optimal outcomes.
It is important that staff members are able to more precisely identify critical risk factors. Those factors that are most
likely to lead to preventable negative outcomes allows staff to implement system-wide changes and develop more effective interventions
as well as better leverage valuable clinical resources to impact those most in need.
Information regarding a beneficiary's demographics, diagnoses and medications are processed through the predictive model on
a frequent basis, usually monthly. This information is compared to standards of care and evidence-based medicine. Gaps in
care and care opportunities are identified for the care manager, saving time. This information is essential to individualizing
a beneficiary's plan of care and directs the care manager's focus toward quality and improved outcomes across the care continuum.
Timely information from frequent predictive modeling updates allows users to proactively identify members moving towards becoming
"high-risk" before they are high cost. Risks associated with emerging health patterns can be assessed so appropriate interventions
can be implemented early.
Predictive modeling identifies key drivers of risk for a beneficiary. Knowing this information helps to more precisely target
interventions to those risk factors that are likely to lead to a preventable negative event. As success is found in mitigating
these risks with specific interventions, they can be integrated into care coordination strategies to effectively align the
efforts of the care management team.
Technology enhances the connection with beneficiaries. Integrated care management systems offer the opportunity for organizations
to redefine how programs identify, monitor and manage beneficiaries at risk. Technology enables rapid access to beneficiary
data, patterns of behavior, health claim history and pharmacy information, which could hold the keys to improving care and
reigning in healthcare expenditures.
Integration of key information from disparate sources provides a more comprehensive view of a beneficiary across the healthcare
continuum.
Cost-effective allocation of scarce resources can be system driven. The type of resources and intensity of services can be
assigned on predicted future risk of a beneficiary allowing care coordination activities to be matched with changing risk
levels and needs of the beneficiary.
Incorporation of ongoing bio-surveillance information assists in evaluation of ongoing risk and early identification of developing
situations where rapid assessment and timely intervention are needed to prevent an adverse event.
System driven processes and integrated quality of care alerts/reminders facilitate efficient, consistent workflows designed
to meet the complex needs of Medicare care management programs.
Ongoing monitoring of targeted interventions for at-risk and high-risk beneficiaries through analysis and reporting tools
enables efficient, comprehensive identification of program trends, risks and opportunities.
Predictive modeling can help programs working with Medicare beneficiaries focus on identification and stratification of individuals
at highest risk for costs and adverse outcomes. Combining this information with non-claims based beneficiary information can
facilitate successful care management strategies and interventions resulting in positive clinical and financial outcomes.
Jo Anne Hunt ARNP, CCM, is director of Clinical Solutions at Casenet, Inc.
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