It’s Complicated: Methods to assess medication nonadherence and regimen complexity
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Transcript of It’s Complicated: Methods to assess medication nonadherence and regimen complexity
It’s Complicated: Methods to assess medication
nonadherence and regimen complexity
John Billimek, PhDDepartment of Medicine Grand Rounds | August 12, 2014
Division of General Internal Medicine | Health Policy Research Institute | UC Irvine School of Medicine
Two patients 58 year-old man Type 2 diabetes Middle class,
educated Good overall health
Prescribed
4 medications
58 year-old man Type 2 diabetes Middle class,
educated Good overall health
Prescribed
7 medications
Patient Complexity in Chronic Disease Management
Multiple Chronic Conditions Nationwide (CDC) Among all adults in the US
50% have at least one chronic condition 25% have two or more
Adults over age 65 86% have at least one chronic condition 61% have two or more
Two-thirds of health care spending
Ward 2014 Prev Chronic Dis 2014;11:130389Anderson 2010. Chronic Care: Making the Case for Ongoing Care, RWJ
Complex Patients, Complex Regimens
More Chronic Conditions
More medications
indicated
Over- and under-
prescribing
Worse adherence
More adverse events
Higher costs
Increased hospitalizati
on
Increased readmission
s
Increased mortality
Mansur et al 2012. Am J Geriatr Pharmacother 10;223-229Wilson et al 2014. Ann Pharmacother 48(1);26-32
Medication Nonadherence Over 50% of patients
either Never fill Rx Delay refills Discontinue, and/or Skip doses
Contributes to up to 69% of hospital admissions And $100 billion
Osterweil 2005. NEJM
How much nonadherence is too much?
Varies by condition, treatment and situation
In VA patients with diabetes “Skipping” 20% of doses
+81% mortality risk +58% all-cause admission rate
“Skipping” 50% of doses 12-fold mortality risk
Ho. et al. 2006. Arch Intern Med 166:1836-41 Egede et al. 2011. The Annals of Pharmacotherapy 45: 169 –78
R2D2C2 Study
NIDDK, RWJ, Novo Nordisk funded RCT Disparities in diabetes management Poor, ethnically diverse sample (N=1484) Data collection
Patient questionnaires Chart review Audiorecordings
Study Foci 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 35% have 4+ additional comorbid conditions
87% taking 5 or more different medications 35% are taking 10+ medications
Over 60% report medication nonadherence
Reasons for nonadherence Forgetting Cost, Financial pressures Side effects (currently
experienced) Regimen confusing, complicated Side effects (possible, future
damage) Pharma advertising
Interferes with lifestyle Concerns about alcohol
Concerns about effectiveness, value Experimenting, “N-of-me trials”
DO: (Mixed) Evidence based approaches
Patient
Physician
Pharmacist
NurseProfessio
nal Health
Educators
Community Health Workers
• Multifactorial & Coordinated• Case Management• Education• Patient Engagement
• Tailored & Targeted• One size fits none
DO: The Medical Visit Where treatment
decisions are made
All useful information may not be available
Little time to talk Averages: 15 minutes | 6
topics 5 minutes for main topic 1 minute for each of the rest
Tai-Seale 2007. Health Serv Rsch 42:5 1871-94
Patient
Physician
Pharmacist
NurseProfessio
nal Health
Educators
Community Health Workers
Many patients have problems with adherence
…but few raise problems with the doctor
DO: Coached Care Patient Participation Training
AudioRecord
PatientQuestionnaire
DO: Patient Participation training Coached Care
Raised issue about regimen during visit
Issue was addressed during the visit
0%
10%
20%
30%
40%
50%
60%
70%
39%32%
59%
48%
Standard-ized educa-tion
0%
20%
40%
60%
80%
100%
43%
75%
No issues addressedAt least one issue ad-dressed
% s
how
ing
im
pro
vem
en
t b
y n
ext
vis
it
Raising problems with adherence helps
Patients with A1c>9% at recorded visit
DO: The Medical VisitOrganize
services toCUE UP
topics and info for the medical
visit
Involve the patient to promote FOLLOW-THROUGH
Patient
Physician
Pharmacist
NurseProfessio
nal Health
Educators
Community Health Workers
KNOW: So, who do we help? Two EMR-based approaches to ID
patients
1. Medication Nonadherence Medication Possession Ratio (MPR)
2. Regimen Complexity: Medication Regimen Complexity
Index (MRCI)
Assessing Medication Nonadherence
Don’t we already know who isn’t taking their medications?
0%
20%
40%
60%
80%
25%
61%
Do you take your medications as prescribed? (less than always)
Indicate which (of 9) barriers to adherence you have faced recently (reporting at least one)
% r
ep
ort
ing
non
ad
-h
ere
nce
The way we ask matters
0%
5%
10%
15%
20%
5%6%
11%
14%
% d
iffere
nce
betw
een
n
on
ad
here
nt
vs.
ad-
here
nt
A1c LDL
*
*
Look in the EMR: the Medication Possession Ratio (MPR)
How much nonadherence is too much?
Varies by condition and situation
In VA patients with diabetes “Skipping” 20% of doses
+81% mortality risk +58% all-cause admission rate
“Skipping” 50% of doses 12-fold mortality riskHo. et al. 2006. Arch Intern Med 166:1836-41
Egede et al. 2011. The Annals of Pharmacotherapy 45: 169 –78
Assessing Regimen Complexity
Take two patients taking 7 medications
15 doses 4+ times/day2 modalities
9 doses 2 times/day1 modality
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 27 meds
S M T W TH F S
Morning
7 7 7 7 7 7 7
Midday
Evening
2 2 2 2 2 2 2
Night
Look in the EMR: Medication Regimen Complexity Index (MRCI)
One score for each patient
Objective
Actionable
Patient A’s MRCI
score
24
Patient A’s Med List
-------- --- -- ---------- --- -- ---------- --- -- ---------- --- --
-------- --- -- ---------- --- -- ---------- --- --
Flag high-risk patients in a
registry
Available at point of care
MRCI = Total A + Total B + Total Cfor all current prescription medications
Dosage Form Dosing FrequencySpecial Instructions+ +
Medication Regimen Complexity Index (MRCI)A weighted count of currently prescribed medications
A B C
A B C
All polypharmacy is not created equal
Putting it together: Population management of medication issues
MRCI
Patient Reported
Nonadherence
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 1: R2D2C2 DatasetHypothesis testing
MRCI
MPR
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 2: UCI Diabetes RegistryPredictive modeling
2012 2013
Stage 3: Stakeholder EngagementFrom KNOW to DO
Stage 1 R2D2C2 Dataset: Preliminary Findings
Hospital admission
ER Visit
LDL > 100 mg/dl
A1c > 8%
Medication nonadherence
0.0 0.5 1.0 1.5 2.0 2.5
1.8
1.9
1.1
1.4
1.5
Stage 1 R2D2C2 Dataset: Linking MRCI to outcomes
Higher rates with high MRCI
Odds ratios comparing MRCI above vs. below 17Adult UCI patients with type 2 diabetes (N=998)adjusted for: Age, Sex, Race/ethnicity, Education, Insurance type, Nativity, duration of diabetes and comorbidity (TIBI)*
MRCI
Patient Reported
Nonadherence
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 1: R2D2C2 DatasetHypothesis testing
MRCI
MPR
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 2: UCI Diabetes RegistryPredictive modeling
2012 2013
Stage 3: Stakeholder EngagementFrom KNOW to DO
MRCI
Patient Reported
Nonadherence
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 1: R2D2C2 DatasetHypothesis testing
MRCI
MPR
Outcomes
A1c
LDL
ER Visits
Hospital Admissio
ns
Adjust for
Comorbidity
Patient Char
Stage 2: UCI Diabetes RegistryPredictive modeling
2012 2013
Stage 3: Stakeholder EngagementFrom KNOW to DO
Acknowledgments
Funders DOM Chair’s Award ICTS Pilot Awards program NIDDK
Collaborators 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) Marco Angulo Anabel Arroyo
MRCI/MPR Development team Travis Nesbit Daniel Orlovich
Audiocoding Team 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 Nonadherence and Complex regimens are
common Problems with regimens are rarely discussed
Regimen complexity Outcomes Independent of comorbid disease burden
EMR-based approaches can identify patients struggling with medication regimen Help direct interventions and resources
Questions?
John Billimek, PhD | [email protected]