Quality Improvement Solutions Boost quality ratings across HEDIS, HOS and other measure sets

 

Plans must coax members to engage and help them gain confidence in self-management. When members understand the importance of their screenings and ways to control their condition, they are more likely to take action on them. Decision Point has helped Medicare Advantage, Medicaid and Commercial plans experience sustained improvement in quality and outcomes by delivering personalized communications to members at a level and intensity commensurate with their predicted behavior.

Decision Point’s approach is based on predictive analytics, member segmentation and personalized communication.

While prediction is key to identifying members that are of greatest compliance (i.e. HEDIS) risk, identifying the drivers of behavior or barriers to engagement further helps plans segment the population by imputing the reasons why members are predicted to behave a certain way. Most importantly, identifying behavioral drivers enable plans to craft outreach interventions that directly address those drivers, thereby personalizing the outreach to maximize the probability of behavioral change.

Decision Point has developed a system that identifies actionable driver segments that are not too individualized so as to preclude large-scale deployment, and not too large so as to eliminate personalization.

The combination of risk prediction and behavioral drivers help create homogeneous member cohorts that can be prioritized based on risk, and personalized based on driver.

HEDIS Screenings

Predicting HEDIS Screenings enables plans to deploy a multi-dimensional strategy, focusing on members that require more help and attention to get their screenings at the start of the measurement year, and then shifting the focus to members that simply need a reminder and gentle push to get their screenings towards the end of the year.   This way plans can yield high HEDIS screening rates, while also efficiently deploying the right resources at the right time.

Readmissions

Readmissions are a function of both clinical and engagement risk.  Members that are at high risk for readmissions typically have an undesirable disease trajectory as well as a history of engagement challenges, such as poor preventive behavior, PCP switching, etc. Proactively addressing these clinical and engagement issues in advance of the initial, index admission enables plans to focus on both the clinical and engagement challenges of “at risk” members in order to potentially avoid the initial, index admissions and/ or the subsequent readmission.

HOS

Survey data on HRA (or mock-HOS) responses can be used to profile member on their functional status – not only for members that responded to the survey, but for all members in the plan. The Decision Point solution includes models to predict HOS behavior that can be applied to the entire population, enabling outreach to members that are at risk for negative HOS behavior.  Clients have used this data in combination with Decision Point’s admission and readmission predictive models to fuel effective field case management activities.

While prediction is key to identifying members that are of greatest for quality of care issues, identifying the drivers of behavior and channel preference further helps plans segment the population by imputing the reasons why members may be at risk for non-compliance or negative outcomes. Most importantly, identifying behavioral drivers and channel preference enable plans to craft outreach interventions that directly address those drivers, thereby personalizing the outreach to maximize the probability of behavioral change.

 


 

Results & Outcomes

Our approach has produced sustainable results:

Improved member compliance with screening and care recommendations

  • Compared to control groups…
  • 10%-15% higher breast cancer screening rates in a Medicare population
  • 8% – 20% better member performance on comprehensive diabetic care measures in a Medicare population
  • 12% higher screening rates (across multiple HEDIS measures) for a Medicaid population
  • 15% higher screening rates (across multiple HEDIS measures) for an Exchange population

Lower avoidable utilization

  • Our models identified more than 70% of readmissions by targeting less than 5% of members, and reduced readmissions by 25% annually in a Medicare population;
  • Lowered hospital admission rates by 5%-15% annually, depending on the condition for a Medicaid population
  • Lowered emergency room visits by 12% annually in a Medicaid population

 

Contact Decision Point today to learn more.

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