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Predicting Medication Adherence Team Lift Alteryx Data Challenge Overview of submission October 29 th , 2015

Team Lift: Predicting Medication Adherence

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Page 1: Team Lift: Predicting Medication Adherence

Predicting Medication Adherence

Team Lift

Alteryx Data Challenge Overview of submission

October 29th, 2015

Page 2: Team Lift: Predicting Medication Adherence

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

Page 3: Team Lift: Predicting Medication Adherence

Medication non-adherence is a major and growing public health concern, as 20% to 30% of medication prescriptions are never filled consistently.

Bad news is that we aren’t reaching 50% medication adherence

100%88%

76%

47%

Rx PrescribedRx FilledRx TakenRx Continued

Medication Adherence

Source: American Heart Association: Statistics you need to know. http://www.americanheart.org/presenter.jhtml?identifier=107Accessed November 21, 2007.

Page 4: Team Lift: Predicting Medication Adherence

Investment in medication adherence can lead to dramatic improvements in effectiveness of treatments

Why adherence matters?• Results of failure to adhere to prescribed medications:

– Increased hospitalization– Poor health outcomes– Increased costs– Decreased quality of life– Patient death

About 30% to 50% of treatment failures are due to medication non-adherence. These treatment failures are estimated to cause 125,000 deaths annually.

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

Page 5: Team Lift: Predicting Medication Adherence

Investment in medication adherence can lead to dramatic reductions in overall cost of care

Diabetes Medication level of Adherence

1-19% 20-39% 40-59% 60-79% 80-100%

$8,812$6,959 $6,237 $5,887

$3,808

$55

$165$285 $404

$763

Rx $Medical $

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

Page 6: Team Lift: Predicting Medication Adherence

Medication 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

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, economic 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)

Page 7: Team Lift: Predicting Medication Adherence

Blending data from different sources was key to derive predictors & classifying patients based on adherence

Source: www.cms.govwww.nlm.nih.gov/research/umls/rxnorm/docs/rxnormfiles.htmlwww.census.gov

Medicare - Primary Data Source

Drug InformationDisease Information

rxNorm Data

Economic InformationSocial InformationHousehold Information

Census Data

Patient InformationMedicare Part D Information

Page 8: Team Lift: Predicting Medication Adherence

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

Page 9: Team Lift: Predicting Medication Adherence

Heart diseases contribute up to 50% of healthcare spends in US. We wanted to look at medication adherence in the context of heart ailments.

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 good news is that we know what works, and the medications, when required, are low cost.

Source: Roger VL, et al. Circulation 2012;125:e2-e220Heidenriech PA, et al. Circulation 2011;123:933–4

Chronic Diseases

Page 10: Team Lift: Predicting Medication Adherence

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 ContextTo make individuals’ behavior

Long-lasting Protective Interventions

ClinicalInterventions

Counseling & Education

LargestImpact

SmallestImpact

Poverty, education, housing, inequality

Hypothesis

Page 11: Team Lift: Predicting Medication Adherence

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 SummaryVariables 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

Most nonadherence is not caused by side effects or drug costs. Rather the problem is behavioral, simple procrastination and forgetfulness.

Page 12: Team Lift: Predicting Medication Adherence

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 tostatin medication

Page 13: Team Lift: Predicting Medication Adherence

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].

Page 14: Team Lift: Predicting Medication Adherence

Demo

Page 15: Team Lift: Predicting Medication Adherence

Questions