This document discusses predicting medication adherence through predictive modeling. It begins by outlining the problem of medication non-adherence and its impacts. It then describes the methodology used, which involved blending data from Medicare, drug information databases, and census data to derive predictors and classify patients based on adherence. Regression and decision tree models with 45 predictors were able to predict medication adherence days and classify patients as adherent or non-adherent. The inferences from the models supported the hypothesis that public health, personal, and medication factors influence non-adherence. The document concludes by discussing interventions like behavioral, financial, and clinical approaches that can be tailored to individuals to improve adherence.

1. Predicting Medication Adherence
Team Lift
Alteryx Data Challenge
Overview of submission
October 24th, 2015

2. Our Team
Data by itself isn’t worth anything unless there’s a problem
to solve and a community to solve it.
- Beth Noveck, Founder, GovLab
Suresh Prasanth Bala Sakthi

3. Medication non-adherence is a major and growing public health concern, as
20% to 30% of medication prescriptions are never filled consistently.
Heart Disease and Strokes
• Cause 1 of every 3 deaths
• Over 2 million heart attacks and strokes
each year
– 800,000 deaths
– Leading cause of preventable death in
people <65
– $444 B in health care costs and lost
productivity
– Treatment costs are ~$1 for every $6
spent
• Greatest contributor to racial disparities in
life expectancy
The less good news is that we are not
reaching 50% medical adherence.
100%
88%
76%
47%
Rx Prescribed
Rx Filled
Rx Taken
Rx Continued
The good news is that we know what
works, and the medications, when
required, are low cost.
Medication Adherence
Source: American Heart Association: Statistics you need to know.
http://www.americanheart.org/presenter.jhtml?identifier=107
Accessed November 21, 2007.
Source: Roger VL, et al. Circulation 2012;125:e2-e220
Heidenriech PA, et al. Circulation 2011;123:933–4

4. Investment in medication adherence can lead to dramatic reductions in
overall cost of care
Why adherence matters?
• Results of failure to adhere to prescribed
medications:
– Increased hospitalization
– Poor health outcomes
– Increased costs
– Decreased quality of life
– Patient death
Diabetes Medication level of Adherence
$8,812
$6,959
$6,237 $5,887
$3,808
$55
$165
$285
$404
$763
1-19% 20-39% 40-59% 60-79% 80-100%
Rx $
Medical $
About 30% to 50% of treatment failures
are due to medication non-adherence.
These treatment failures are estimated
to cause 125,000 deaths annually.
Healthcare expenditure ($/year)
Outcome is significantly higher than outcome for 80-
100% adherence group (P<0.05). Differences were
tested for medical cost and hospitalization risk.
Source: Sokol M et al. Impact of Medication Adherence on Hospitalization
Risk and Healthcare Cost. Medical Care.
Volume 43, Number 6, June 2005
Source: Benner JS, Glynn RJ, Mogun H, Neumann PJ, Weinstein MC, Avorn J.
Long-term persistence in use of statin therapy in elderly patients. JAMA
2002;288:455-461

5. Medical adherence needs to be addressed in Primary Care
Top reasons for nonadherence
• Cost of medications
• Side effects/fear of side effects
• Forget/can’t keep track of
medications/complexity
• Don’t think it works/don’t need it
Most nonadherence is not caused by side effects or drug costs. Rather the
problem is behavioral, simple procrastination and forgetfulness.
Complex human behavior
Source: Sokol M et al. Impact of Medication Adherence on Hospitalization
Risk and Healthcare Cost. Medical Care.
Volume 43, Number 6, June 2005
Source: Nasseh K, et al. Cost of medication nonadherence associated with
diabetes, hypertension, and dyslipidemia.
Am J Pharm. 2012;4.2:e41–e47.
• Socioeconomic factors (age, race,
gender, ecomonic status)
• Patient-related factors (knowledge,
attitudes, beliefs, and skills)
• Condition/treatment related factors
(disease severity, co-morbidity, regimen
complexity, side effects)
• Provider factors (skill, training, resources)
• Setting/policies (access to care, Rx
coverage)

6. Effective interventions - Automated call helped increase the number of
prescriptions that were filled, indicating improved adherence.
Source: Derose SF, Green K, Marrett E. Automated outreach to increase
primary adherence to cholesterol-lowering medications
[published online November 26, 2012]. Arch Intern Med. 2013.
Automated call system in patients
with primary non-adherence to
statin medication

7. Blending data from different sources was key to derive predictors &
classifying patients based on adherence
Source: www.cms.gov
www.nlm.nih.gov/research/umls/rxnorm/docs/rxnormfiles.html
www.census.gov
Medicare - Primary Data Source
Drug Information
Disease Information
rxNorm Data
Economic Information
Social Information
Household Information
Census Data
Patient Information
Medicare Part D Information

8. When we looked at this data, we kept asking this question often “What
diseases that this pill treat?”
• Five websites with 3 different datasets to
find this information
• Often information coding conventions are
not common or consistent
• We used Alteryx to help solve this problem
Simple question, but complex data
We were using this so often that we built
an Analytic App!
Published this on http://gallery.alteryx.com as well

9. Prediction Methodology - What factors influence nonadherence?
• Decision tree model to predict ‘Yes’/’No’
• Regression to fit adherence days. Can we
predict for how many days does this
patient take his medication
• Models with 45 predictors
Modeling medication nonadherence
Socioeconomic Factors
Changing the Context
To make individuals’ behavior
Long-lasting
Protective Interventions
Clinical
Interventions
Counseling
& Education
Largest
Impact
Smallest
Impact
Poverty, education, housing, inequality
Brief intervention for alcohol,
cessation treatment
0g trans fat, salt, smoke-free
laws, tobacco tax
Rx for high blood pressure, high
cholesterol
Eat healthy, be physically active
Examples for
cardiovascular health
Our Hypothesis

10. Inferences from our predictive models are largely inline with our hypothesis
• Public Health factors – State, County, Area
• Personal factors – Age, Income, Race,
Sex, Education, Insurance
• Medication factors – Number of pills,
Patient payments, Cost of drugs, Other
chronic diseases
Factors that influence nonadherence
for cardio vascular diseases
Model Summary
Variables actually used in tree construction:
[1] Area.name Avg_Annual.Average.Pay BENE_BIRTH_DT
[4] BENE_RACE_CD BENE_SEX_IDENT_CD BENRES_CAR
[7] BENRES_OP MEDREIMB_CAR MEDREIMB_OP
[10] Num_Pills PPPYMT_CAR PTNT_Pays
[13] RX_Cost SomeColPct SP_ALZHDMTA
[16] State
Root node error: 1821/10308 = 0.17666
n= 10308
We can add more personal and
behavioral factors to improve the
accuracy of our model further

11. Each intervention must be tailored to individual patients. Incentives of all
stakeholders needs to be aligned to improve medication adherence.
Source: Derose SF, Green K, Marrett E. Automated outreach to increase
primary adherence to cholesterol-lowering medications
[published online November 26, 2012]. Arch Intern Med. 2013.
Improve medication adherence
• Behavioral – audible reminders, smart pill
boxes and auto-refills counteract
procrastination and forgetfulness.
• Financial – Lower cost pharmacies,
generics, and payment assistance make
medication more affordable.
• Clinical – Pharmacist consultations and
therapeutic resources help address
medical concerns.
Improve Medicare systems
• Medication Adherence rates are far from
optimal but CAN be improved through
collaboration and alignment of incentives
for plans, physicians and pharmacists
• Ongoing, consistent, measurement of
medication adherence rates is important to
gauge improvement and to identify “best
practices” across plans, physicians and
pharmacists
• What gets measured…. Gets improved!
Source: Nau D The importance of measuring adherence Pharmacy Quality
Alliance [published online November 7, 2011].

12. Notes on models & data
• Lift_ISCHM_NDC_workflow p1 – this
workflow does the lookup of all drugs to
diseases lookup and builds relations
database for stage 2
• Lift_ISCHM_NDC_workflow p2 – this
worlfow does the panel data construction
with merging census datat, medicare data
and prescriptions data
• Lift_NDC_Disease_App – This is our
Analytical App for ‘What diseases does this
pill treat’
• Lift_Predictive_Model – Data
preprocessing and predictive models
(decision tree/regression) for medication
adherence
Workflows in our submission