3. 50%
of patients with chronic
conditions do not take their
medications as prescribed THE REAL
COST OF
POOR
ADHERENCE
75%
of patients who are
prescribed statins stop
taking them after 2 years
Of all medication-related
hospitalizations that occur
in the US,
1/3 to 2/3
are the result of poor
medication adherence
Poor medication adherence
takes the lives of
125,000 people
a year and costs the
US healthcare system
$300 Billion
3
Up to
4. 4
Health Plans and Medicare Advantage Plans:
• Fewer hospital admissions
• Better health outcomes
• Utilization improvement
• Star ratings & reimbursements
Pharmaceuticals:
• Increase sales
• Promote patient outcomes
WHY MEDICATION ADHERENCE IS IMPORTANT:
Medicare Prescription Drug Plans:
• Star ratings & reimbursements
• Patient outcomes & satisfaction
Pharmacy Benefit Managers:
• Increase Star ratings
• Promote patient outcomes
5. 3 Critical Elements of a Successful Program
1. Identify the right patients to engage
2. Target engagement tactics to unique patient needs
3. Measure and track results
5
7. How to Find The Right Patients
7
Disease
Stage
Data
Social
Determinants
of Health
Medical
Claims
Rx
ClaimsEngagement
Data
Other
Available
Data
8. How to Find The Right Patients
Stratify Large and Complex Populations
Based on Key Predictive Analytics
• Likelihood to be non-adherent
• Likelihood to be receptive to health
coaching and engagement
• Likelihood to change behavior
• Receptivity to outreach modality
(e.g., live coaching, IVR, email, digital)
8
9. 9
Traditional predictive modeling is a time-consuming, labor-intensive process.
Newer machine learning-based predictive modeling allows rapid model
development, verification, deployment, monitoring, and adjustment.
Machine-Learning Predictive Models
11. TOP BARRIERS TO MEDICATION ADHERENCE
47%
10% 8% 7% 5% 3% 3%
0
10
20
30
40
50
Time
Management
Lack of
Knowledge
Provider
Issues
Lack of
Motivation
Side Effects Physical
Limitations
Cost/Financial
Every patient and every population is unique.
This chart shows the top barriers to medication
adherence impacting one of our clients’
populations.
12. Meet Mark, our first-fill patient case study
Overview
• Age: 65
• Conditions: Hypertension
• Medication: Lisinopril
• Overall good health and leads an active lifestyle
Because Mark works hard to stay healthy and lives an active
lifestyle, he is not convinced that he really needs to take his
medication despite multiple elevated blood pressure readings
that have led to a formal diagnosis of hypertension.
“I feel fine and exercise almost every
day. If I was sick, I would take a
medication, but I’m not.”
PATIENT CASE STUDY
Meet Mark
13. Engagement strategies targeted to Mark addressed a variety of topics
and issues:
• Disclosed possible side effects
• Reviewed his expectations and preferences surrounding
Lisinopril use and explored knowledge gaps
• Assessed barriers to obtaining Lisinopril
• Sent him educational information about Lisinopril
and high blood pressure
By the end of the coaching session, Mark felt more comfortable taking his new
prescription and understood the important role that the medication plays in
maintaining his good health.
CASE STUDY RESULTS
14. Meet Susan, our poly-chronic patient case study
Overview
• Age: 67
• Conditions: Diabetes, Hypertension, ASCVD, & Depression
Susan takes 12 different medications every day to help control
these conditions, but often runs into roadblocks or barriers,
including:
• Lack of transportation
• Poor health
• Complex medication regimen
Meet Susan
“I take so many different pills that I can
never keep track of what is going on.
Before I know it, a few days go by
without taking some of my pills or I
forget to refill something altogether.”
PATIENT CASE STUDY
15. Engagement strategies targeted to Susan addressed a variety of topics
and issues:
• Assessed barriers to obtaining prescription medications
• Designed a plan to organize a schedule for taking medications
• Scheduled ongoing monthly follow-up calls to monitor progress
towards adherence
• Connected Susan to a local pharmacist
Personalized health coaching put Susan on the path to adherence with
simplified medication routines and continued support.
CASE STUDY RESULTS
18. 18
REFILL RATES
Patients coached on
their first fill had refill
rates 8-10 percentage
points higher than
patients not coached
49%
48%
49%
59%
56%
58%
40%
45%
50%
55%
60%
Diabetes RASAs Statins
Not Coached Coached
Like-Population Refill Rates
Refill rate = % of patients who refilled their prescription one or more times
To minimize inherent differences between the ‘Coached’ and ‘Not Coached’ groups, a subset of ‘Not Coached’
members who looked similar to coached members were selected for the comparison (propensity-matched)
19. 19
REFILL RATES
SUPD is a
triple-weighted Stars
measure beginning with
Measurement Year 2019
10.1%
9.1%
0%
3%
6%
9%
12%
Patient-Directed
Calls
Prescriber-Directed
Letters
Increase in Patients with Diabetes
Taking a Statin, Intervention
Group
21. 21
• Increase Star ratings
• Improve patient outcomes,
reduce hospitalizations, and
improve utilization rates
• Enhance the quality of medication
management offerings
• Cost-effective outreach program for large
populations
SUMMARY
SS-MA-0919