Transcript Methods to assess medication nonadherence and regimen complexity

It’s Complicated:
Methods to assess medication nonadherence
and regimen complexity
John Billimek, PhD
Department of Medicine Grand Rounds | August 12, 2014
Division of General Internal Medicine | Health Policy Research Institute | UC Irvine School of Medicine
Two patients
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58 year-old man
Type 2 diabetes
Middle class, educated
Good overall health
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58 year-old man
Type 2 diabetes
Middle class, educated
Good overall health
Prescribed
Prescribed
4
7
medications
medications
Patient Complexity in Chronic
Disease Management
Multiple Chronic Conditions
Nationwide (CDC)
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Among all adults in the US
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Adults over age 65
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50% have at least one chronic condition
25% have two or more
86% have at least one chronic condition
61% have two or more
Two-thirds of health care
spending
Ward 2014 Prev Chronic Dis 2014;11:130389
Anderson 2010. Chronic Care: Making the Case for Ongoing Care, RWJ
Complex Patients, Complex Regimens
Worse
adherence
More adverse
events
More Chronic
Conditions
More
medications
indicated
Over- and
underprescribing
Higher costs
Increased
hospitalization
Increased
readmissions
Mansur et al 2012. Am J Geriatr Pharmacother 10;223-229
Wilson et al 2014. Ann Pharmacother 48(1);26-32
Increased
mortality
Medication Nonadherence
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Over 50% of patients either
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Never fill Rx
Delay refills
Discontinue, and/or
Skip doses
Contributes to
up to 69% of hospital
admissions
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And $100 billion
Osterweil 2005. NEJM
How much nonadherence is too much?
Ho. et al. 2006. Arch Intern Med 166:1836-41
Egede et al. 2011. The Annals of Pharmacotherapy 45: 169 –78
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Varies by condition, treatment
and situation
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In VA patients with diabetes
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“Skipping” 20% of doses
 +81% mortality risk
 +58% all-cause admission rate
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“Skipping” 50% of doses
 12-fold mortality risk
R2D2C2 Study
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NIDDK, RWJ, Novo Nordisk funded RCT
Disparities in diabetes management
Poor, ethnically diverse sample (N=1484)
Data collection
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Patient questionnaires
Chart review
Audiorecordings
Study Foci
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Patient Participation Training
Patient Complexity
Medication Adherence
Kaplan 2013. J Gen Int Med 28(10): 1340-9
Complex Patients at UCI: Diabetes
 75% of R2D2C2 study patients have
2+ additional comorbid conditions
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35% have 4+ additional comorbid conditions
 87% taking 5 or more different medications
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35% are taking 10+ medications
Over 60% report
medication nonadherence
Reasons for nonadherence
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Forgetting
Cost, Financial pressures
Side effects (currently experienced)
Regimen confusing, complicated
Side effects (possible, future damage)
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Interferes with lifestyle
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Pharma advertising
Concerns about alcohol
Concerns about effectiveness, value
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Experimenting, “N-of-me trials”
DO: (Mixed) Evidence based approaches
Nurse
Professional
Health
Educators
Pharmacist
Physician
Patient
Community
Health
Workers
• Multifactorial & Coordinated
• Case Management
• Education
• Patient Engagement
• Tailored & Targeted
• One size fits none
DO: The Medical Visit
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Where treatment
decisions are made
Nurse
Professional
Health
Educators
Pharmacist
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All useful information
may not be available
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Little time to talk
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Physician
Averages: 15 minutes | 6 topics
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5 minutes for main topic
1 minute for each of the rest
Tai-Seale 2007. Health Serv Rsch 42:5 1871-94
Patient
Community
Health
Workers
Many patients have problems with
adherence
…but few raise problems
with the doctor
DO: Coached Care
Patient Participation Training
Audio
Record
Patient
Questionnaire
DO: Patient Participation training
Coached Care
70%
59%
60%
48%
50%
40%
39%
32%
30%
Standardized
education
20%
Coached Care
10%
0%
Raised issue about regimen Issue was addressed during
during visit
the visit
Raising problems with adherence helps
Patients with A1c>9% at recorded visit
% showing improvement by next visit
100%
75%
80%
60%
43%
40%
20%
0%
No issues
addressed
At least one issue
addressed
DO: The Medical Visit
Organize services to
CUE UP
topics and info for
the medical visit
Involve the patient
to promote
FOLLOWTHROUGH
Nurse
Professional
Health
Educators
Pharmacist
Physician
Patient
Community
Health
Workers
KNOW: So, who do we help?
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Two EMR-based approaches to ID patients
Medication Nonadherence
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Medication Possession Ratio (MPR)
Regimen Complexity:
2.
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Medication Regimen Complexity Index
(MRCI)
Assessing Medication
Nonadherence
Don’t we already know who isn’t taking
their medications?
The way we ask matters
Do you take your medications as prescribed? (less
than always)
61%
60%
40%
25%
20%
0%
Indicate which (of 9) barriers to adherence you have
faced recently (reporting at least one)
% difference between
nonadherent vs. adherent
% reporting nonadherence
80%
20%
*
14%
15%
*
11%
10%
5%
0%
5%
A1c
6%
LDL
Look in the EMR:
the Medication Possession Ratio (MPR)
How much nonadherence is too much?
Ho. et al. 2006. Arch Intern Med 166:1836-41
Egede et al. 2011. The Annals of Pharmacotherapy 45: 169 –78

Varies by condition and situation

In VA patients with diabetes

“Skipping” 20% of doses
 +81% mortality risk
 +58% all-cause admission rate
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“Skipping” 50% of doses
 12-fold mortality risk
Assessing Regimen Complexity
Take two patients
taking 7 medications
7 meds
S
M
T
W
TH
F
S
Morning
7 (P)
7 (P)
7 (P)
7 (P)
7 (P)
7 (P)
7 (P)
Midday
2
2
2
2
2
2
2
Evening
4 (P)
4 (P)
4 (P)
4 (P)
4 (P)
4 (P)
4 (P)
Night
2
2
2
2
2
2
2
7 meds
S
M
T
W
TH
F
S
Morning
7
7
7
7
7
7
7
2
2
2
2
2
2
2
Midday
Evening
Night
15 doses
4+ times/day
2 modalities
9 doses
2 times/day
1 modality
Look in the EMR:
Medication Regimen Complexity Index (MRCI)
 One
score for
each patient
 Objective
 Actionable
Patient A’s
Med List
Patient A’s
MRCI score
-------- --- -- --------- --- -- --------- --- -- --------- --- --------- --- -- --------- --- -- --------- --- --
Flag high-risk
patients in a
registry
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Available at
point of care
Medication Regimen Complexity Index (MRCI)
A weighted count of currently prescribed medications
A
Dosage Form
B
+
C
Dosing Frequency
+
Special Instructions
C
MRCI = Total AA + Total BB + Total C
for all current prescription medications
All polypharmacy is not created equal
Putting it together: Population
management of medication issues
Stage 1: R2D2C2 Dataset
Hypothesis testing
Adjust
for
Comorbidity
Patient Char
MRCI
Patient
Reported
Nonadherence
Stage 2: UCI Diabetes Registry
Predictive modeling
Outcomes
Adjust
Outcomes
A1c
for
Comorbidity
Patient Char
A1c
LDL
MRCI
LDL
ER Visits
ER Visits
Hospital
Admissions
Hospital
Admissions
MPR
2012
Stage 3: Stakeholder Engagement
From KNOW to DO
2013
Stage 1 R2D2C2 Dataset:
Preliminary Findings
Stage 1 R2D2C2 Dataset:
Linking MRCI to outcomes
1.5
Medication nonadherence
A1c > 8%
1.4
LDL > 100 mg/dl
1.1
ER Visit
1.9
Hospital admission
1.8
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
Higher rates with high MRCI
Odds ratios comparing MRCI above vs. below 17
Adult UCI patients with type 2 diabetes (N=998)
adjusted for: Age, Sex, Race/ethnicity, Education, Insurance type,
Nativity, duration of diabetes and comorbidity (TIBI)*
Stage 1: R2D2C2 Dataset
Hypothesis testing
Adjust
for
Comorbidity
Patient Char
MRCI
Patient
Reported
Nonadherence
Stage 2: UCI Diabetes Registry
Predictive modeling
Outcomes
Adjust
Outcomes
A1c
for
Comorbidity
Patient Char
A1c
LDL
MRCI
LDL
ER Visits
ER Visits
Hospital
Admissions
Hospital
Admissions
MPR
2012
Stage 3: Stakeholder Engagement
From KNOW to DO
2013
Stage 1: R2D2C2 Dataset
Hypothesis testing
Adjust
for
Comorbidity
Patient Char
MRCI
Patient
Reported
Nonadherence
Stage 2: UCI Diabetes Registry
Predictive modeling
Outcomes
Adjust
Outcomes
A1c
for
Comorbidity
Patient Char
A1c
LDL
MRCI
LDL
ER Visits
ER Visits
Hospital
Admissions
Hospital
Admissions
MPR
2012
Stage 3: Stakeholder Engagement
From KNOW to DO
2013
Acknowledgments
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Funders
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DOM Chair’s Award
ICTS Pilot Awards program
NIDDK
Collaborators
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Sheldon Greenfield
Sherrie Kaplan
Dara Sorkin
Quyen Ngo-Metzger
Shaista Malik
Dana Mukamel
Lisa Dahm
Andrea Hwang
UC Irvine Health Informatics &
Research Computing
Patient Advisory Group (La Voz de la Esperanza)
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Marco Angulo
Anabel Arroyo
MRCI/MPR Development team
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Travis Nesbit
Daniel Orlovich
Audiocoding Team
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Herlinda Guzman
Linh Vu
Katherine Vu
Sophia Nguyen
Kimberly Gardner
Taylor Gardner
Mylon Remley
Mei Chang
Sana Moosaji
Stephanie Torrez
Maria Paula Gonzalez
Alejandro Avina
Jessica Colin Escobar
Linda Nguyen
Summary
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Nonadherence and Complex regimens are common
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Regimen complexity  Outcomes
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Problems with regimens are rarely discussed
Independent of comorbid disease burden
EMR-based approaches can identify patients struggling
with medication regimen
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Help direct interventions and resources
Questions?
John Billimek, PhD | [email protected]