Proactive and Personalized Preventive Health

Closing gaps in care just got a whole lot easier.

Welcome to Your Preventive Health Toolkit

Cancer and Routine Screenings

Prenatal and Postpartum Care

Well Child Visits

Vaccinations

Comprehensive Diabetes Care

Annual Wellness Visits

Consumer Provider Search

Powerful, Proven Prevention

0 %

of targeted members closed gaps across 20+ HEDIS® measures

0 %

improvement in CDC A1c Controlled

0 %

improvement in AAP visit rate for targeted members

0 %

of unengaged members completed their AWV

SOLUTION SHEET

HEDIS® & Prevention

Improve gap closure and HEDIS® performance by leveraging predictive insights to deliver personalized interventions.

Interventions for Prevention

Bridging the Gap

Leveraging data and predicative models, mPulse segments your population into who is most to least likely to close needed care gaps, providing you with a clear path to who to target and when.

The Right Conversation

Identify and overcome individual barriers to care by providing the right education and resources through each individual’s preferred digital channel at key moments.

Education to Action

Encourage informed health decisions and member actions at key touchpoints. With portal-driven reminders and tools to complete screenings and wellness visits, your members can find a provider, stay up-to-date on vaccinations, and manage their ongoing care.

CASE STUDIES

Close Gaps with Powerful Technology

  • CASE STUDY 1
  • CASE STUDY 2

HEDIS® Quality Improvement

Goal:

Empower Medicaid members to close care gaps and assess the efficacy and impact of SMS messaging outreach on related HEDIS® measures.

Solution:

SMS messages were deployed in both English and Spanish translation, depending on each member’s preferred language. Messages included barrier assessments, CTAs to needed resources, and motivational messaging to improve the likelihood of health action. A control group was compared to an intervention group to assess impact using claims data.

Results:

8.5

pp improvement in comprehensive diabetes care A1c controlled (CDC A1c)

14.3

pp improvement in postpartum care visits (PPC)

11.7

pp improvement in well child visits (WCV)

Improving HEDIS® with Machine Learning-Driven Engagement

Goal:

Improve HEDIS® measures, specifically targeting breast cancer screening rates and comprehensive diabetic care, while enhancing overall access to care.

Solution:

Machine learning models were deployed to assign a risk score for every member in the plan and for each relevant measure. Outside data sources were used to enrich the dataset and identify the environmental factors that were contributing to access issues and leading to low compliance rates. High-risk members were targeted with barrier-specific engagements early and often throughout the year, while the members most likely to close on their own were given a light “nudge” at the end-of-the year.

Results:

12%

annual improvement in Breast Cancer Screening Rates

8%

annual improvement in Comprehensive Diabetic Care Rates

14%

annual increase in CAHPS Access to Care Measures

Testimonials

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