Learn how to improve your medication adherence strategy with help from our analytics exports. Identifying the right patients with AI machine-learning and predictive analytics are a solid foundation.

1. 3 Innovative Steps that Increase
Medication Adherence:
HOW TO USE MACHINE LEARNING
AI TO FIND THE NEEDLE IN THE
HAYSTACK
May 14, 2019

2. 50%
of patients with chronic
conditions do not take their
medications as prescribed
WHAT’S THE
IMPACT?
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
Up to
© 2019 Health Dialog

3. Age: 67
“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.”
MEET SUSAN

4. 2008 2009 2010 2011 2012
Mid 2008:
Diagnosed with
diabetes and
hypertension
Prescribed an oral
diabetes medication
and a hypertension
medication
End 2008:
Had to stop working due
to physical limitations
Early 2009:
Misses her A1c
lab work
End of 2009:
Her A1c has risen to
8.6%
Prescribed a second
oral diabetes
medication
Mid 2011:
Her A1c increases to
9.2%
Her doctor adds another
oral diabetes medication
Mid 2012:
She doesn’t feel any
different while taking
hypertension medication,
so she stops taking it
Early 2010:
Misses 2 consecutive
refills of her hypertension
medication
Mid 2010:
Prescribed a statin,
but doesn’t fill it
© 2019 Health Dialog

5. 2013 2014 2015 2016 2018
Mid 2013:
Stops driving and starts
relying on family and
friends for
transportation
Mid 2014
Misses her yearly
physical
Mid 2016:
Her hypertension gets
worse due to
continuous missed
doses of hypertension
medication
Mid 2017:
Suffers a myocardial
infraction which leads to
a diagnosis of ASCVD
While in the hospital
recovering, she is
prescribed more
medication to help
control the ASCVD
2017
Mid 2018:
She is consistently
late filling her
prescriptions
Early 2015
Feels down; schedules
a mental health visit
Mid 2015
Is diagnosed with
depression and is
prescribed an
antidepressant
medication
© 2019 Health Dialog

6. 6
• A woman and over 65
• On several medications
• Has high copays and has a lower
income for her area
Predictive models indicate that
Susan will likely be receptive to
telephonic coaching because she is:
WHY SUSAN?

7. 7
• She has shown some evidence of
medication non-adherence
• She has been taking
hypertension medication for an
extended period of time
• She is likely to respond to
specific types of offers
Predictive models indicate she is
likely to improve her medication
adherence patterns with support:
WHY SUSAN?

8. Disease
Stage
Data
Social
Determinants
of Health
Medical
Claims
Rx
ClaimsEngagement
Data
Other
Available
Data
HOW TO FIND THE RIGHT PATIENTS
© 2019 Health Dialog

9. This is typically where a patient like Susan is found – after a major health event that requires a hospital stay
With our analytics we are able to find
Susan and intervene at an earlier stage.
TYPICAL PROGRAMS FIND SUSAN WHEN IT’S TOO LATE
2008 20182009 2010 2011 2012 2013 2014 2015 2016 2017
Susan suffered a myocardial infarction
which leads to a diagnosis of ASCVD.
While in the hospital recovering, she is
prescribed more medication to help
control the ASCVD.
© 2019 Health Dialog

10. FINDING SUSAN AT THE START OF HER JOURNEY
First Fill Interventions
• Patients filling a script for the first time are less likely
to be adherent
• Patients coached upon their first fill see refill rates 7-
10 percentage points higher than those not coached
2008 20182009 2010 2011 2012 2013 2014 2015 2016 2017
Misses two consecutive
refills of her hypertension
medication
Prescribed
medication when
diagnosed with
hypertension
© 2019 Health Dialog

11. FINDING SUSAN AT THE START OF HER JOURNEY
Demographics & Social Determinants of Health
• Has physical limitations
• Lives alone
• Lives in a rural location
• Has little discretionary income
• Not a good financial planner
2008 20182009 2010 2011 2012 2013 2014 2015 2016 2017
Had to stop
working due to
physical
limitations
© 2019 Health Dialog

12. FINDING SUSAN AT THE START OF HER JOURNEY
Multiple Chronic Conditions
These patients are more likely to be non-adherent
than patients with only one chronic condition.
2008 20182009 2010 2011 2012 2013 2014 2015 2016 2017
Her A1c increases to 9.2%.
Her doctor adds another oral
diabetes medication
© 2019 Health Dialog

13. FINDING SUSAN AT THE MIDDLE OF HER JOURNEY
Mental Health
We could have found Susan when she incurred a
mental health claim. Patients with depression are
more likely to be non-adherent.
2008 20182009 2010 2011 2012 2013 2014 2015 2016 2017
Susan scheduled a mental health visit.
Later diagnosed with depression and
prescribed an antidepressant
medication
© 2019 Health Dialog

14. FINDING SUSAN AT THE MIDDLE OF HER JOURNEY
Early Non-Adherence
We could have found Susan when she showed early signs
of non-adherence upon not taking the hypertension and
oral diabetes medications she was prescribed.
2008 20182009 2010 2011 2012 2013 2014 2015 2016 2017
Suffered a myocardial infarction
which led to a diagnosis of ASCVD.
While in the hospital recovering, she
is prescribed more medication to help
control the ASCVD.
© 2019 Health Dialog

15. RESULTS FROM 1:1 PATIENT ENGAGEMENT
A Health Coach was able to uncover
Susan’s barriers and help her develop
a plan to overcome them.
Addressed knowledge gaps
Assisted her to switch prescriptions to a 90-day supply
Helped her sign up for a home delivery service
Designed a plan to organize medications
Connected Susan to a local pharmacist
Facilitated provider communications
Scheduled monthly follow-up calls
© 2019 Health Dialog

16. Coached patients had a
refill rate 9 percentage
points higher than
patients not coached
© 2019 Health Dialog
TELEPHONIC INTERVENTION: MEDICARE PART D PLANS

17. Coached patients had
adherence levels 7.3%-
7.9% higher than non-
coached patients1
7.5% 7.3%
7.9%
0.0%
2.0%
4.0%
6.0%
8.0%
Diabetes RASA Statins
Additional Percent Adherence
Coached vs Not Coached2
1 Patients coached and those not coached were propensity-matched on age and gender to minimize inherent differences between the two groups
2 Patients may be counted for each measure. The same patient may be counted more than once.
TELEPHONIC INTERVENTION: MEDICARE PART D PLANS
© 2019 Health Dialog