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Running head: MEDICATION ADHERENCE
An Investigation of Interactive Voice Response and a Care Management Program on
Medication Adherence and Health Utilization in a Senior Population
Diane Cempellin
Doctor of Nursing Practice
Simmons College
School of Nursing and Health Sciences
Boston, Massachusetts
© 2015 Diane Cempellin
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MEDICATION ADHERENCE 4
Abstract
Poor adherence with prescribed medications can lead to poor clinical outcomes, worsening of disease, increased healthcare costs, deteriorated quality of life, and even death. Seniors including those on Medicare, are more likely to have chronic conditions and to be prescribed multiple medications that need to be taken at certain times and/or under certain conditions (for example, before or after a meal). The purpose of this project was to investigate the effectiveness of an Automated Interactive Voice Response System (IVR) outreach intervention on medication adherence rates for older adults with chronic disease using a reminder call to identify members who were non-adherent to their medication regime. Additionally, this intervention investigated the impact of a nurse case management program (MyCarePath), among a high risk group of comorbid older adults on improving medication adherence, as measured by Proportion of Days Covered (PDC) in comparison to providing usual care. The theoretical perspective for this study was based on the basic principles of The Medication Adherence Model. A quantitative quasi-experimental design study using an Interactive Voice Response (IVR) reminder call system (a technology that automates interactions with telephone callers) was used to identify all Medicare Supplement Health Insurance Plan (SHIP) members living in the pilot markets that had pharmacy coverage through UnitedHealthcare (Medicare Part D or other pharmacy coverage) and were non-adherent to a prescribed medication regimen. For both studies the main outcome measures were improved PDC, decrease in utilization, and reduction in prescription costs and total costs with maintenance medications. Analysis was completed through a retrospective review of claims indicating refills of medication and health care utilization. Both studies did not improve PDC nor was a statistically significant difference found in any medication group. The IVR study found there was a decrease in emergency room visits, inpatient visits and nursing home admissions although most results were non-significant. There was no associated decrease in emergency room visits, inpatient visits and nursing home admissions for with the MCP program participants. Finally, there was no associated increase in savings in either prescription drug or total costs for either study.
Keywords: interactive voice response, medication adherence, patient adherence, medication adherence in older adults
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MEDICATION ADHERENCE 5
Acknowledgements
I want to acknowledge the incredible support, leadership and countless hours of work of all the
members of my capstone committee including a special thanks to Susan Duty, Sc.D., ANP-BC
who guided me through all of the statistical analysis.
My deepest thanks are given to Jane, Gandhi, Tim and Mike who answered multiple questions
and pulled hundreds of data sets to make this project a success. A thank you also goes to the
pharmacists at Optum RX who collected data cycle after cycle and packaged it in a digestible
manner for me for evaluation.
Finally, I would be remiss if I did not thank Cynthia Barnowski my fabulous boss who approved
funding for this project and for her assistance and support during the project.
Lastly, but most importantly, I would like to thank my family, collegial support of students,
coworkers, and a myriad of professionals for their encouragement, understanding, and support
though this process. Becoming a DNP would not have been possible without all of you.
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MEDICATION ADHERENCE 6
Dedication
I want to thank my dear friends, Rocky, Joan, Barbara, Pam, Karen, Nancy, Kathy, Marcia and
Marie, for their unwavering support during the last three years. Because of their love, friendship
and help, my doctoral journey was made tremendously easier.
I must thank my husband, Peter, and my children, Laura, and Andrew, and my sister Lynn for
enduring this sometimes frustrating and exhausting journey with me. To my parents who taught
me that education will challenge you to grow and then will reward you in unexpected ways.
Thank you for your love, patience, kindness, and support in allowing me to pursue my degree.
Above all, to God for guiding me each and every day.
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MEDICATION ADHERENCE 7
Table of Contents…………………………………………………………………………………………...7 Capstone Manuscript Oral Presentation Approval ..........................................................................i Capstone Manuscript Approval Form ……………………………………………………………ii Abstract…………………………………………………………………………………………...iii Acknowledgements…………………………………………...................................................... iv Dedication…………………………………………....................................................................... v
Introduction ................................................................................................................................... 12
Description of the Clinical Problem .......................................................................................... 13
Targeted Program Population .................................................................................................... 14
Epidemiology of the Problem ................................................................................................... 14
Purpose Statement ..................................................................................................................... 16
Research Questions ....................................................................................................................... 17
For IVR intervention ................................................................................................................. 17
For the case management intervention: ..................................................................................... 17
Significance................................................................................................................................... 17
Impact on Practice ..................................................................................................................... 18
Impact on Health Policy ............................................................................................................ 18
Review of Literature ..................................................................................................................... 19
Introduction ............................................................................................................................... 19
Current State of Medication Adherence Using Reminders ....................................................... 21
Gaps in the Literature ................................................................................................................ 28
Summary ................................................................................................................................... 29
Definition of Terms....................................................................................................................... 31
Interactive Voice Response Systems (IVR) .............................................................................. 31
Proportion of Days Covered (PDC) .......................................................................................... 32
Adherence .................................................................................................................................. 33
Non-Adherence ......................................................................................................................... 33
Intentional Non-Adherence ....................................................................................................... 33
Unintentional Non-Adherence .................................................................................................. 33
Full Intervention ........................................................................................................................ 33
Authenticated ............................................................................................................................ 34
Fax only Intervention ................................................................................................................ 34
MEDICATION ADHERENCE 8
Control (No Intervention) .......................................................................................................... 34
Compliance ................................................................................................................................ 34
Community Assessment ............................................................................................................ 34
MyCarePath Program ................................................................................................................ 34
Pilot Markets ............................................................................................................................. 35
Medicare Part D ........................................................................................................................ 35
Medication Therapy Management Program (MTMP) .............................................................. 35
United HealthCare ..................................................................................................................... 35
Cycle .......................................................................................................................................... 35
Hierachical Condition Categories (HCC) Risk Score ............................................................... 35
Index Date ................................................................................................................................. 36
Pre-Index Period ........................................................................................................................ 36
Post-Index Period ...................................................................................................................... 36
Methods......................................................................................................................................... 36
Methods-Research Question One .............................................................................................. 36
Design. ................................................................................................................................... 37
Setting. ................................................................................................................................... 37
Intervention. ........................................................................................................................... 38
Data collection/procedure. ..................................................................................................... 39
Data analysis. ......................................................................................................................... 40
Multivariate analysis.............................................................................................................. 42
Methods-Research Question Two ............................................................................................. 43
Setting. ................................................................................................................................... 44
Sample. .................................................................................................................................. 44
Intervention. ........................................................................................................................... 44
Data collection ....................................................................................................................... 45
Data analysis. ......................................................................................................................... 45
Results ........................................................................................................................................... 46
Research question #1 (IVR) ...................................................................................................... 46
Demographics............................................................................................................................ 46
Medication Groups .................................................................................................................... 47
MEDICATION ADHERENCE 9
Intervention Group Size by Drug Class .................................................................................... 48
Change in Total Costs ............................................................................................................... 50
Impact of Interventions on Health Care Utilization .................................................................. 50
Results ........................................................................................................................................... 53
Research question #2 MyCarePath (MCP) participants ............................................................ 54
Demographics............................................................................................................................ 54
Impact of Interventions on Outcomes ....................................................................................... 56
Discussion ..................................................................................................................................... 57
Limitations .................................................................................................................................... 62
Conclusion .................................................................................................................................... 64
References ..................................................................................................................................... 65
Figures........................................................................................................................................... 76
Figure 1 Sample Size by Treatment Groups ................................................................................. 76
Figure 2 Sample Size by Drug Class by Treatment Group ........................................................... 77
Figure 3 PDC by Drugs and Treatment Category Pre to Post-period ........................................... 78
Figure 4 Antidepressants-Odds of Improving Adherence using 80% Cut-Point or PDC Difference >=0 Compared to Control Analysis ........................................................................... 79
Figure 5 Beta Blockers-Odds of Improving Adherence using 80% Cut-Point or PDC Difference >=0 Compared to Control Analysis ............................................................................................ 80
Figure 6 RAS Antagonists-Odds of Improving Adherence using 80% Cut-Point or PDC Difference >=0 Compared to Control Analysis…………………………………………………81
Figure 7 Statins-Odds of Improving Adherence using 80% Cut-Point or PDC Difference >=0 Compared to Control Analysis .................................................................................................... 84
Figure 8 Absolute Changes in PDC After Intervention For All Drug Categories ........................ 85
Figure 9 Analysis PDC Percentage Change by Drug Class Compared to Control ....................... 86
Figure 10 Changes in Drug Cost By Treatment Group by Drug Categories ……………………865
Figure 11 Change in Total Cost By Treatment Group by Drug Categories ............................... 886
Figure 12 Odds of ER Admissions for IVR Compared to Control Analysis .............................. 897
Figure 13 Odds of In patient Admissions for IVR Compared to Control ………………………88
Figure 14 Odds of Nursing Home Admissions for IVR Compared to Control………………….89
Tables and Graphics ...................................................................................................................... 90
MEDICATION ADHERENCE 10
Table 1 Intervention Studies That Use IVR-Randomized and Quasi-Experimental .................... 90
Table 2 Drugs included in the Pharmaceutical Adherence Program ............................................ 91
Table 3 Attrition Table .................................................................................................................. 93
Table 4 Antidepressants Socio-demographic Baseline Characteristics ........................................ 94
Table 5 Beta Blockers Socio-demographic Baseline Characteristics ........................................... 94
Table 6 Calcium Channel Blockers Socio-demographic Baseline Characteristics ...................... 96
Table 7 Diabetes Socio-demographic Baseline Characteristics .................................................... 97
Table 8 Osteoporosis Socio-demographic Baseline Characteristics ............................................. 98
Table 9 RAS Antagonist Socio-demographic Baseline Characteristics ....................................... 99
Table 10 Statins Socio-demographic Baseline Characteristics ................................................... 100
Table 11 Analysis Among Antidepressants Users: Effectiveness of IVR on Adherence Compared to Control .................................................................................................................................... 101
Table 13 Analysis Among Calcium Channel Blocker Users: Effectiveness of IVR on Adherence Compared to Control................................................................................................................... 103
Table 14 Analysis Among Diabetes Users: Effectiveness of IVR on Adherence Compared to Control ........................................................................................................................................ 104
Table 15 Analysis Among RAS Antagonist Users: Effectiveness of IVR on Adherence Compared to Control................................................................................................................... 105
Table 16 Analysis Among Osteoporosis Users: Effectiveness of IVR on Adherence Compared to Control ........................................................................................................................................ 106
Table 17 Analysis Among Statins Users: Effectiveness of IVR on Adherence Compared to Control ........................................................................................................................................ 107
Table 18 Analysis Among Antidepressants Users: Healthcare Utilization Outcomes ............... 108
Table 19 Analysis Among Beta Blockers Users: Healthcare Utilization Outcomes .................. 109
Table 20 Analysis Among Calcium Channel Blocker Users: Healthcare Utilization Outcomes 110
Table 21 Analysis Among Diabetes Users: Healthcare Utilization Outcomes ........................... 111
Table 22 Analysis Among RAS Antagonist Users: Healthcare Utilization Outcomes .............. 112
Table 23 Analysis Among Osteoporosis Users: Healthcare Utilization Outcomes .................... 113
Table 24 Analysis Among Statins Users: Healthcare Utilization Outcomes .............................. 114
Table 25 Analysis Among Antidepressants Users: Effectiveness of MCP on Adherence Compared to Control................................................................................................................... 115
Table 26 Analysis Among Beta Blocker Users: Effectiveness of MCP on Adherence Compared to Control .................................................................................................................................... 116
MEDICATION ADHERENCE 11
Table 27 Analysis Among Calcium Channel Blocker Users: Effectiveness of MCP on Adherence Compared to Control................................................................................................................... 117
Table 28 Analysis Among Diabetes Users: Effectiveness of MCP on Adherence Compared to Control ........................................................................................................................................ 118
Table 29 Analysis Among Osteoporosis Users: Effectiveness of MCP on Adherence Compared to Control .................................................................................................................................... 119
Table 30 Analysis Among RAS Antagonists Users: Effectiveness of MCP on Adherence Compared to Control................................................................................................................... 120
Table 31 Analysis Among Statins Users: Effectiveness of MCP on Adherence Compared to Control ........................................................................................................................................ 121
Table 32 Analysis of Drug Costs of MCP on Adherence Compared to Control ........................ 122
Table 33 Analysis of Total Cost of MCP on Adherence Compared to Control ......................... 123
Table 34 Analysis of MCP on ER Visits Utilization .................................................................. 124
Table 35 Analysis of MCP on Inpatient Utilization Outcomes .................................................. 125
Table 36 Analysis of MCP on Nursing Home Utilization Outcomes ......................................... 126
Appendix A-PDC Calculation .................................................................................................... 127
Appendix B-Call Script............................................................................................................... 129
Appendix C- Member Satisfaction Survey ................................................................................. 138
Appendix D- Report and Physician Letter .................................................................................. 140
MEDICATION ADHERENCE 12
Introduction
The Centers for Medicare and Medicaid Services (CMS) estimates that 11% of hospital
readmissions occur due to medication non-adherence, creating an economic impact that is
estimated to cost nearly $100 billion annually (Osterberg & Blaschke,2005). In addition, it has
been estimated that 23% of nursing home admissions are related to the inability of patients to
properly self-administer medications with associated costs of $31.3 billion/380,000 patients
(Strandberg, 1984). Col, Fanale, and Kronholm (1990), demonstrated that 28.2% of 315
consecutive elderly hospital admissions at a single acute-care hospital were related to medication
issues, 16.8% of the hospitalizations were due to adverse drug reactions, and 11.4 % were due to
medication noncompliance. Older adults, including those on Medicare, are more likely to have
chronic conditions, be prescribed multiple medications, have complex medication regimens, and
high medication expenses. Additionally, consumers aged 65 and older fill an average of 31
prescriptions per year (Agency for Healthcare Research & Quality [AHRQ], 2010). The average
adult 55 and older manages six to eight medications and struggles to fit their medication
schedule into their daily lives (AHRQ, 2010). Poly-pharmacy, in combination with the cognitive
and physical changes associated with aging, places older adults at increased risk for poor
medication adherence (Ruppar, Conn, & Russell, 2008). Kocurek (2009) states that low and non-
adherence to prescribed medications can lead to increased morbidity, death, worsening of disease
states, and increased healthcare costs.
Despite advances in technology, there are only a few published peer reviewed studies
demonstrating that electronic reminders, when appropriately used, can improve adherence with
improvements ranging from as low as 24% to as high as 100%. It is clear that more confirmatory
MEDICATION ADHERENCE 13
studies are needed to determine whether improved outcomes, fewer emergency room visits,
nursing home admissions and inpatient admissions can translate to significant cost reductions.
DescriptionoftheClinicalProblem
An estimate of non-adherence in older adults with chronic conditions ranges from 40% to
75% (Doggrell, 2010). The impact of poor adherence, which includes increased admissions to
nursing homes and hospitalizations, disease progression, decreased quality of life, and increased
costs of care, is greater in older adults who have an increased burden of symptoms and disease
(Doggrell, 2010). Forgetting to take or refill medications is one of the leading causes of
medication non-adherence in older adults (Brown & Bussell, 2011).
The site of this practice inquiry is United Healthcare (UHC), one of the nation’s largest
payers. In 2012, UHC had the largest share of Medicare beneficiaries through its Medicare
Supplement Health Insurance Plan, which is branded with AARP. In 2010-2011, United
Healthcare developed a pharmacy adherence program in order to identify the top barriers of
member’s adherence. (C. Barnowski, personal communication, December 16, 2011). Internal
United Healthcare data revealed that the two leading reasons for non-adherence were financial
and cognitive, i.e., forgetfulness. These findings led the team to focus on solutions to address
forgetfulness that would be scalable and cost effective in order to improve outcomes. Our
hypothesis was that the use of IVR technology reminder calls might represent a scalable, cost-
efficient, and effective tool for reminding individuals to take or refill medications.
Long-term use of pharmacotherapy is commonly included in the treatment of chronic
illnesses. Medicare and pharmaceutical adherence programs and Medication Therapy
Management programs to improve adherence were found in nearly all of the published literature
as an element for treatment in their health programs. Although these pharmacotherapy programs
MEDICATION ADHERENCE 14
are effective in combating disease, experts estimate that approximately 50 % of patients do not
take their medicines as prescribed or follow their provider’s recommendations (Brown &
Bussell, 2011). Thus, patients are not receiving the full benefits of the therapy. Pharmacotherapy
is so complex, (more than 200 factors have been identified since the mid- seventies, (Fenerty,
West, Davis, Kaplan, & Feldman, 2012) that no single strategy to improve medication adherence
has been found to be more effective than any other, across a broad range of conditions
(Kripalani, Yao, & Haynes, 2007).
Targeted Program Population
The targeted population consists of Medicare Supplement Health Insurance Plan (SHIP)
members living in the pilot markets of NY, LA, NC, OH, FL, who have pharmacy coverage
through United Healthcare’s AARP plan (Medicare Part D or other pharmacy coverage).
Members were identified as non-adherent, if they met the following criteria: failure to refill a
medication for coronary artery disease, diabetes, heart failure, depression or osteoporosis within
7 days of expected refill and Proportion of Days Covered (PDC) <80% for delinquent medication
class during the prior quarter.
An audit of member records revealed approximately 50% or 13,000 members were
“repeatedly” non-adherent throughout the year and 2,700 members identified as high risk (non-
adherent with four or more medications four or more times during the year). These 2 groups
formed the pools of potential participants for the two clinical interventions of this project.
Epidemiology of the Problem
Medication non-adherence is a prevalent problem among older adults suffering from
chronic illness. Forgetting to take or refill medications is one of the leading causes of medication
non-adherence in older adults. Medication non-adherence contributes to avoidable health care
MEDICATION ADHERENCE 15
costs, can exacerbate disease symptoms, and ultimately may lead to death (Brown & Bussell,
2011). Within United Healthcare, on average, 45% of all older adult (over 65 years of age)
members have identified forgetfulness as a barrier to adherence. Interactive voice response
technology was selected by United Healthcare for an internal project as a scalable, cost-efficient,
and presumably an effective tool for reminding individuals to take or refill medications.
Statistical points of note include:
Adherence with chronic medical therapy is <50% at 6 months following the initial prescription
(Brown & Bussell, 2011).
Medication non-adherence is responsible for at least 10% of hospitalizations and nearly one
quarter of nursing home admissions (Doggrell, 2010).
As a proportion of all medication-related admissions, 33% to 69% are related to poor medication
adherence (Brown & Bussell, 2011).
Societal cost of poor adherence is $100 billion annually (Brown & Bussell, 2011).
Interactive Voice Response (IVR) programs are somewhat inexpensive, scalable and may
be one effective solution to address medication adherence. These programs have been used
internally at UHC with Medicare members enrolled in the Medicare Advantage program as one
strategy to improve medication adherence. However, researchers have found that while one
intervention may increase adherence for some patients, it may not work to improve adherence for
others. Key factors such as an individual’s emotion health, health literacy, education level,
cultural beliefs, and social support system are unique experiences, which all contribute to
individuals’ adherence (Martin, Williams, Haskard, & DiMatteo, 2005).
In a study performed in Canada, with a very different health system than the U.S.,
Sherrard et al. (2009) used eleven automated interactive voice response calls for patients, whose
MEDICATION ADHERENCE 16
mean age was 65, for the six months following discharge after cardiac surgery. The findings
showed significant differences between the IVR group and control group for the primary
combined outcome of compliance and adverse events and the secondary outcome of medication
compliance. There were no significant impact of IVR on emergency room visits (P=0.897) or
hospitalizations (p=0.519). When patients were given the choice for an IVR follow-up for
medication education compared to no follow-up, most patients (93%) preferred an IVR follow-
up.
Best practice for IVR programs include targeting the appropriate individuals, being user
friendly, and avoiding technological problems (Reidel, Tamblyn, Patel, & Huang, 2008; Sherrard
et al., 2009). More generalized programs are required to reach a greater number of individuals at
a reasonable cost (Planas, Crosby, Mitchell, & Farmer, 2009; Ramalho de Oliveira, Brummel, &
Miller, 2010; Winston & Lin, 2009). Set-up costs to run an IVR call campaign can run as high as
$15,000 per call script campaign (Abu‐Hasaballah, James, & Aseltine, 2007). The more specific
and detailed the campaign is the higher the set-up costs.
PurposeStatement
The purpose of this research project was to study the use of an interactive voice response
system for the purpose of improving medication adherence in multi-comorbid older adults, and
to standardize and optimize the use of interactive voice response for these purposes. This
occurred in conjunction with provider notification. Additionally, this research investigated the
effectiveness of a nurse case management program on reduction of inpatient admission rates,
emergency room utilization, and mortality or nursing home/rehab admissions on this same
population of very high risk co-morbid older adults. The research questions this study aimed to
answer are:
MEDICATION ADHERENCE 17
Research Questions
For IVR intervention 1. How will implementing an interactive voice response (IVR) refill reminder call program
plus provider fax notification (full intervention) of non-adherence status among a very high risk
group of multi-comorbid older adults influence medication adherence in seven drug classes as
measured by the Proportion of Days Covered (PDC), compared to provider fax alone, IVR alone
or no intervention among older adults with chronic disease?
2. How is the treatment group associated with healthcare outcomes, specifically healthcare
cost and utilization of services like inpatient hospitalization and emergency room visits?
For the case management intervention: 1. How will implementing a nurse case management service (MyCarePath) among a very
high risk group of multi-comorbid older adults influence inpatient admission rates, emergency
room utilization, and nursing home admissions?
2. How will implementing a nurse case management service (MyCarePath) among a very
high risk group of multi-comorbid older adults influence medication adherence as measured by
PDC in comparison to providing usual care?
Significance
Importance of Studying this Issue
Adherence to therapy is especially important for management of chronic diseases, such as
diabetes, heart disease and cancer. Chronic disease affects nearly one in two Americans and
treating chronically ill patients’ accounts for $3 out of every $4 spent on medical care (US
Centers for Disease Control and Prevention [CDC], 2008). Poquette (2013), in a recent
commentary, referred to a Harvard University researcher’s remarks that poor adherence among
MEDICATION ADHERENCE 18
patients with chronic conditions persists, “Despite conclusive evidence that medication therapy
can substantially improve life expectancy and quality of life” (Medication Adherence & Mango
Health – An Interview with CEO Jason Oberfest, Para. 2). Adherence to essential medications
can increase value by improving population health, averting costly emergency department visits,
hospitalizations, and improving quality of life (Shrank, Porter, Jain, & Choudhry, 2009).
Impact on Practice
Compliance- that is taking your medication on a daily basis as prescribed and
persistence-maintaining long term use of medications are the key factors that affect clinical
outcomes. The more empowered patients feel, the more likely they are to be motivated to
manage their illness and follow their medication regime. Thus, involving and activating patients
in treatment decisions when possible is another key factor that can improve patient-related
medication adherence. “Patient/provider concordance is another factor affecting adherence—the
extent to which patients and their providers agree on whether, when, and how a medication
should be taken”. Therefore, “adherence requires a patient to believe there is a benefit to the
medicine being prescribed and agree with instructions on how to take it” (Wroth & Pathman,
2006, p. 478-479). IVR can be used to remind those patients that believe and agree to a
prescribed medication to stay on track.
Impact on Health Policy
In 2013, 1,031 prescription drug plans were offered across the 34 prescription drug plan
regions nationwide. The Medicare drug benefit has helped reduce out-of-pocket drug spending
for enrollees, which is especially important to beneficiaries with modest incomes or catastrophic
illness (Lichtenberg & Sun, 2007). Closing the coverage gap by 2020 will bring additional relief
to millions of enrollees (Lichtenberg & Sun, 2007). Today, although several studies exist,
MEDICATION ADHERENCE 19
findings are inconclusive regarding the impact of Part D on emergency department use,
hospitalizations, or preference-based health utility.
Review of Literature
Introduction
The World Health Organization defines adherence as, “The extent to which a person’s
behavior (taking medications, following a recommended diet and/or executing life-style changes)
corresponds with the agreed upon recommendations of a health care provider” (Sabate, 2003, p.
13). Unintentional non-adherence may be the result of forgetfulness due to the complexity of a
medication regimen and the patient’s memory; either, forgetting to take the medication at the
prescribed time, or failure to recall instructions (Wroe, 2002; Lehane & McCarthy, 2007; Lowry,
Dudley, Oddone, & Bosworth, 2005). Interventions addressing forgetfulness may need to focus
on dose simplification, patient reminders, and assisting patient to maintain daily medication
regimes (Hugtenberg, Timmers, Elders, Vervloet, & Van Dijk, 2013). Interactive voice response
programs have been one strategy used for patient reminders that has been found to be relatively
inexpensive, scalable and somewhat effective in the improvement of medication adherence in
older adults (Bickmore & Giorgino, 2005).
Many researchers have tested IVR interventions to improve older adults’ medication
adherence in rigorous randomized controlled trials (Corkrey & Parkinson, 2012). A number of
studies explored the use of IVR to improve adherence as the sole intervention. Others trials used
medication reminders in conjunction with other medication adherence strategies such as drug
education, written instructions, and cell phone reminders – text messaging and face-to-face
consultation with a healthcare provider. Several of these studies reported significantly better
adherence among intervention versus control groups. Those with electronic device reminders
MEDICATION ADHERENCE 20
showed the most improvements with 82.1% adherence in the groups receiving reminders
compared to 71.4% in the control groups (Fenerty et al., 2012). Older adults often report simply
forgetting as a common reason for missed doses. This is true regardless of the presence or
absence of cognitive impairment (Conn, Taylor, & Miller, 1994). Yet, few studies specifically
tested interventions that address the tendency to forget medications (Ruppar, Conn, & Russell,
2008). Interactive Voice Response (IVR) may be one such solution.
The literature review was conducted using CINHAL®, and MEDLINE® as the
primary online search engines. Additional search was completed using PsycINFO,
Academic Research Complete, Health and Psychosocial Instruments and specialty
organization journals. The following key words searches included singularly and in combination
were as follows: interactive voice response, short messaging, cell phone, text messaging,
medication adherence, non-adherence, unintentional non-adherence, intentional non-adherence,
mediations compliance, patient compliance, patient adherence, and medication adherence in
older adults. Searches were restricted to peer reviewed journals; some exception was given to
specialty organizations’ journals that were not peer reviewed as they serve as a valuable source
of clinical information. All the articles reviewed were in the English language. More than 100
articles were reviewed and nearly all articles were included, ranging in publication dates from
1974 to 2013.
After reviewing these various articles, findings were characterized into several
groups. These were (1) reminder calls, which include studies predominantly related to the use of
IVR as a source to “remember” to take medication to improve adherence; (2) disease
management, which includes the use of IVR as an adjunct to case management to improve
clinical outcomes for patients with hypertension, heart failure, diabetes, and asthma; (3)
MEDICATION ADHERENCE 21
preventative care which includes the use of IVR as an educational intervention to promote health
and prevent disease progression in clinics, doctor offices, and health plans to extend the reach
and hours of operation for the clinical teams to disseminate and capture clinical information; and
(4) treatment of co morbid patients as computerized health behavior interventions with a goal of
improving health status through medication management. Following this section are the areas of
further research and some of the barriers and issues of concern for the use of IVR with the
elderly. The final section concludes with the gaps in literature.
Current State of Medication Adherence Using Reminders
Nearly all of the studies lacked a theoretical basis for the intervention. Only one study
used the Social Cognitive Theory (Friedman et al., 1996). The majority of interventions involved
only the individual patient. The interventions were delivered in a variety of outpatient,
community and home settings. Most of these involved medication adherence interventions to
prepare patients to self-administer medications. Medication education was by far the most
common strategy utilized among the reviewed interventions, whether used alone or in
combination with IVR, telephone or other multifaceted adherence intervention methods. Also,
most studies included some form of education about participants’ prescribed medicines,
medication schedules, and side effects that were geared to improving knowledge and skills for
taking medication. Additionally, disease education was also used as an adjunct to medication
education.
The interventions, the diseases being treated, and the methods for measuring medication
adherence differed considerably between studies. Elderly patients with diabetes (Piette et al.,
2000), heart failure (Fulmer et al., 1999), hypertension (Friedman et al., 1996), and mental health
disorders (Montes et al., 2012) and behavior change with newly prescribed statins (Stacey,
MEDICATION ADHERENCE 22
Schwartz, Ershoff, & Shreve, 2009) all showed improvement in adherence, the details of which
will be discussed below. However, Castle et al. (2012) found little impact from IVR using a
quasi-experimental design when used on a younger population, but, a five-fold improvement on
those over 65years of age. For those aged 18 to 24 years, medication adherence ranged from 33%
to 35%, while for those over 65 years or older the adherence rate was 72% in response to an IVR
intervention.
Twenty-one randomized clinical trials were chosen based on the highest quality of
evidence and rigor. Only one study was quasi-experimental (Castle, et al., 2012). All studies
included adult subjects (aged ≥18 years). Sample size ranged from 22 to 2293. All studies
contained interventions with an electronic component. The diseases and medications across the
trial populations varied widely; Five studies contained patients with hypertension (Friedman et
al., 1996; da Costa et al., 2005; Santschi, Wuerzner, Schneider, Bugnon,& Burnier, 2007;
Christensen et al, 2010; Tambyln et al., 2010), five studies included highly active antiretroviral
therapy (HAART), (Safren, Hendriksen, Desousa, Boswell, & Mayer,2003; Andrade et al., 2005;
Simoni et al., 2009; Hardy et al., 2011; Pop-Eleches et al., 2011), two had patients with asthma
(Bender et al., 2010;Strandbygaard, Thomsen, & Backer, 2010), three had patients with
glaucoma (Laster, Martin, & Fleming, 1996; Ho, Camejo, Kahook, & Noecker, 2008; Okeke et
al., 2009), one with statin use (Stacey et al., 2009) one with sunscreen use (Armstrong et al.,
2009), one with vitamin C use (Cococila, Archer, Haynes, & Yuan, 2008), two studies included
cardiac medications (Fulmer et al., 1999) angiotensin-converting enzyme (ACE) inhibitors,
calcium channel blockers, or beta-blockers and one post discharge after coronary artery bypass
graft, (Sherrand et al., 2009) one study with schizophrenia antipsychotics, (Montes et al.,2012)
MEDICATION ADHERENCE 23
and the last study used automated assessments along with educational calls for diabetes (Piette et
al., 2000).
Five trials had a short messaging service (SMS) phone text message reminder
intervention arm, (Cococila et al., 2008; Okeke et al., 2009; Strandbygaard et al., 2010; Hardy et
al., 2011; Pop-Eleches et al., 2011) one used regular phone call reminders (Armstrong et al.,
2009), Five used an interactive voice response (IVR) phone reminder device, (Friedman et al.,
1996; Sherrand et al., 2009; Stacey et al., 2009; Bender et al., 2010; Castle et al., 2012), one used
video-telephone call reminders (Fulmer et al., 1999) (see table 1 for details) and two focused on
the effects of pager reminders (Safren et al., 2003;Simoni et al., 2009). Four used programmed
electronic audiovisual reminder devices (Laster et al., 1996; Ho et al., 2003; Santschi et al.,
2003; Christensen et al., 2010), two used an electronic reminder device with audible reminder
(Andrade et al., 2005; da Costa et al., 2005), one finally one used a computerized drug profile
(Tamblyn et al., 2010).
Almost half of the studies used multiple measures to assess adherence. Electronic
monitoring to record the date and time of medication was used in thirteen studies. Ten studies
exclusively used electronic monitoring, (Fulmer et al., 1999; Ho et al., 2003; Safren et al.,
2003;Santschi et al, 2003; Cococila et al., 2008; Armstrong et al., 2009; Okeke et al., 2009;
Tamblyn et al., 2010; Pop-Eleches et al., 2011). Three combined electronic monitoring with
self-report ( Andrade et al., 2005; Simoni et al., 2009; Christensen et al, 2010) and one
combined electronic monitoring with both pill count and self-report (Hardy et al., 2011). Two
studies used pill count, (Friedman et al., 1996; da Costa et al., 2005). One study assessed bottle
weight (medication in soluble form) with self-report (Laster et al., 1996). Two studies used
claims data: Castle et al (2012) and Stacey et al (2009).
MEDICATION ADHERENCE 24
Twenty of the twenty one studies showed a statistically significant increase in adherence
for at least one of the reminder group arms compared to the control group. Reminder groups
averaged 10.7% higher adherence than the corresponding control groups (Fenerty et al., 2012;
Vervloet et al., 2012; Misono et al., 2014). Adherence averaged 82.1% in the groups receiving
reminders compared to 71.4% in the control groups with adherence ranging from 44.75 percent
to 100 percent in the reminder groups and 18.6 percent to 100 percent in the control groups
(Fenerty et al., 2012; Vervloet et al., 2012; Misono et al., 2014). No significant difference in
adherence rates was seen for patient-reported results compared to electronic monitoring systems.
Among trials using self-reported results or pill counts to calculate adherence rates, overall
adherence was 83.1%, compared to 78.8% among trials using electronic monitoring devices
(Fenerty et al., 2012; Vervloet et al., 2012; Misono et al., 2014). The average reminder group
adherence rate was 80.5% among trials using self-reported adherence and 74.9% for those
relying upon electronic monitoring (P = .01) (Fenerty et al., 2012; Vervloet et al., 2012; Misono
et al., 2014). Trials utilizing phone or pager text message reminder interventions had an average
adherence rate of 74.8% in reminder groups compared to 53.43% in the control group. There was
no statistically significant difference of text message reminders compared to participants
receiving traditional phone calls, video-telephone calls, or interactive voice response system
reminders (74.8% average adherence, P = 0.14). The two trials using electronic monitoring
systems with integrated audio or audiovisual reminder devices resulted in 93.0% average
adherence, showing a statistically significant increase in adherence over control groups of 82.4%
(Vervloet et al., 2012). The average adherence rate among those receiving HAART therapy was
67.9 % in control groups and 75.3% in intervention groups, with one of three trials showing no
statistically significant improvement in adherence (Vervloet et al., 2012). Adherence rates for
MEDICATION ADHERENCE 25
those receiving asthma inhaler treatments was 56.8% among controls and 78.4% for reminder
groups, with both trials showing a significant improvement over controls. Bender et al. (2010)
found adherence to be 32% higher among patients in the IVR group than those in the control
group; 88% compliance was achieved by 85.0% of the treatment group versus 69.7% of the
control group (P = 0.036). Strandbygaard et al. (2010) reported asthma patients in the
intervention group remembered to take an average of 18% more doses. For those studies in
which the participants received blood pressure medications, the average adherence was 84.7%
without intervention and 90.8% with reminders; several trials showed a statistically significant
improvement in adherence. First, Friedman et al. (1999) found patients who were non adherent at
entry had statistically significant improvements in adherence with the intervention (p=. 03),
whereas adherent patients had no change and the effect size (ES) was very small (ES = - 0.13;
95% CI, -0.12-0.37).
Stacey et al. (2009) found with newly diagnosed statin users that the ES was very small
(ES = 0.08; 95% CI, 0.01-0.17) although participants were at least adherent to 80% of their
medications; 47.0% of the intervention group and 38.9% of the control patients, respectively.
Piette et al. (2000) study with diabetics was unique because it included both Spanish-speaking
and English speaking individuals. The automated assessment, education, and counseling phone
calls using an algorithm with messages and interaction via touch-tone keypad. At the end of the
year, 48% of intervention patients had adherence problems as opposed to 69% in the control arm
(p = .003). The ES was small (ES = 0.38; 95% CI, 0.12-0.63). Fulmer et al. (1999) found that
after the 10-week study period, patients in the control arm were found to have adherence of 57%
versus 81% at baseline (p <.04). Patients with phone reminders had adherence of 74% versus
76% at baseline, and patients with video phone reminders had adherence of 84% versus 82% at
MEDICATION ADHERENCE 26
baseline (Fulmer et al., 1999). There was no statistically significant difference between phone
and videophone reminder arms (Fulmer et al., 1999). The ES could not be calculated. Although
there was a slight trend in the experimental group with phone reminders the control group had a
significant fall off in the medication compliance rate during the course of the study, dropping
from 81% to 57% possibly indicating that there needs to be a longer time for intervention to be
effective. For those receiving eye drops, adherence was 48.5% and 67.75% among control and
reminder groups, respectively (Laster et al., 1996; Ho et al., 2003; Okeke et al., 2009).
Three of the six studies used SMS reminders showed significant positive effects on
adherence. Patients who received the SMS reminders took more medication (50% vs. 39%)
within the prescribed timeframe and missed less doses (15% vs. 19%, p = 0.065) than those who
did not receive reminders, over the six month study (Vervloet et al., 2012). These studies used
either customized text messages that required a response from the patients when taking their
medication, (Hardy et al., 2011) or a standardized text message with no response required
(Strandbygaard et al., 2010; Pop-Eleches et al., 2011). The study revealed no effect using
standardized messages. However, using the customized messages a significant difference in
adherence was found at weeks 3 and 6 p= 0.012. Six of the fourteen studies evaluating
audio/visual reminders from electronic reminder devices (ERD) significantly improved patients’
adherence. Five of them used electronic devices that produced both an audible and visual
reminder, (Laster et al., 1996; Ho et al., 2003; Santschi et al., 2007; Da Costa et al., 2005;
Christensen et al., 2010;) the sixth used a device that only emitted an audible reminder (Andrade
et al., 2005). Andrade (2005) conducted the only study that found a beneficial effect of a device
with an audible reminder when using a Disease Management Assistance System (DMAS)
device, combined with monthly adherence counseling. The DMAS prompting device improved
MEDICATION ADHERENCE 27
adherence for memory-impaired subjects but not for memory-intact subjects. Two interventions
used pagers, one of which revealed a significant affect Safren et al. (2003). Use of the pager
system revealed greater improvements in adherence at weeks 2 and 12 than patients who were
only monitored by electronic reminder devices but both pager study groups adherence was less
than optimal (<70%). The Simoni et al. (2009) study provided standardized text messages to
patients’ pagers at predetermined times. Findings from the study indicate a decrease in adherence
overtime. Adherence rates at 2 weeks, 3, 6, and 9 months was 63%, 46%, 36%, and 34%,
compared with 62%, 43%, 39%, and 31% for those not receiving the intervention.
In one study among elderly patients taking at least four medications for chronic diseases,
a pharmacy care program significantly improved medication adherence from 61.2% to 96.9%
(5.2%; P<.001) (Lee, Grace, and Taylor, 2006). Intervention approaches using case management
(Graham et al., 2012), collaborative care (Graham et al., 2012), decision aids (Veroff, Ochoa-
Arvelo, & Venator, 2013), and educational curriculum that focused on activation and self-
management skills administered by care managers (Klickman et al., 2010), pharmacists (Magid,
et al., 2011), were tested and found to improve adherence over the control groups.
The use of IVR and other electronic devices in the treatment, diagnosis, and management
of medication adherence for chronic disease such as congestive heart failure, diabetes,
hypertension, coronary artery disease, asthma has been found to be somewhat effective as seen
by the results documented earlier in this paper. The electronic reminder devices and systems
were more effective than the other interventions, showing the largest effect sizes in the literature
review. Education systems and counseling interventions were less efficacious, as was the
addition of real time adherence feedback. Interactive systems demonstrated very small effect
MEDICATION ADHERENCE 28
sizes and the studies directed to improve provider-patient interaction also had very small effect
sizes.
The number of studies had wide variability in study design, including patient population,
interventions, and outcomes. The duration of the studies, including the optimal length of calls,
follow-up time and limited sample size of studies leave opportunity to further explore and study
the use of interactive voice response technology use for the elderly particularly as the size of this
population is expected to double in the next 25 years.
Gaps in the Literature
Given the state of the evidence, further research is needed to study the feasibility of using
IVR on a large scale to address medication adherence with older adults. Additional foci address
cost, quality, and efficiency in meeting the triple aim of improving the patient experience of care,
improving the health of populations; and reducing the per capita cost of health care. Lee et al.
(1999) suggests employing accredited standards for IVR as more wide spread use becomes
standard.
There is a plethora of information about the use of IVR in healthcare. A gap remains in
the research literature surrounding segmentation and individual customized messaging which
addresses patient factors; e.g., an individual’s emotional health, health literacy, education level,
cultural beliefs, and social support system. It is critical that health messages are designed with an
understanding of how people process health information and consequently make medical
decisions. Identification of appropriate actions and educational materials for each individual
patient which is delivered to them over the communication channel that the patient finds most
convenient needs to be studied further. The IVR message can be sent through any channel of
MEDICATION ADHERENCE 29
communication which today may be a plain old telephone (POTs) or cell phone, iPod, tablet, or
any other electronic channel.
Summary
IVR is somewhat effective in improving medication adherence in a chronic illness, such
as, heart failure (Fulmer et al., 1999), diabetes (Piette et al., 2000), hypertension (Friedman et al.,
1999), and mental health disorders (Montes et al., 2012). However, Castle et al. (2012) found
little impact from IVR using a quasi-experimental design when used on a younger population but
a five-fold improvement on those over 65years of age. In addition, IVR was found to be effective
in a variety of settings, and age ranges, in small and large scale studies. Several interventions
used in studies have proven to be effective for forgetfulness (Conn et al., 1994) and patient
education (Goldman et al., 2008).
Older adults with chronic conditions serve as an important population to test IVR.
Another opportunity exists in improving the technology issues that have affected past studies in
which technical problems have impacted rates of participation and overall ease and satisfaction
with telephone reminders (Stacy et al., 2009). Additionally, testing the use of this technology,
over an extended time, for at least one year, would determine if it also leads to improved health
outcomes over the long range, since, a majority of the studies were very short in length (less than
six months).
Although communication technology cannot replace the provider patient interface as the
primary source of information, it offers an important opportunity to extend provider -patient
communication. Behavior change achieved during any controlled study may or may not easily
translate into wide-ranging clinical practice implementation. Change in adherence behavior
achieved during various studies may or may not persist after the program ends. Future research
MEDICATION ADHERENCE 30
applications will require testing of IVR’s effectiveness in the new models of care such as
accountable care organizations, medical homes and rural settings. Accordingly, IVR should be
useful for patients who are non-adherent mainly because they forget to take their medication
secondary to a lack of acquired routines or cognitive deficits.
Conceptual Framework
The Medication Adherence Model (MAM) (Johnson, 2002) is the model undergirding
this study. This model was developed to describe the process of medication adherence and to
guide health care providers in assessing medication-taking in individuals with hypertension
(Johnson, 2002). The MAM was structured with the idea that two types of non-adherence
contribute to inconsistent medication taking, the intentional decision to miss medications, and the
unintentional interruptions that cause medications not to be taken. The three essential concepts of
the MAM model are: (1) Purposeful Action, (2) Patterned Behavior, and (3) Feedback.
Purposeful action is the degree to which individuals cognitively or intentionally decide to
take medication based on perceived need, effectiveness, and safety. Purposeful action specifies
the individuals’ perception of need, effectiveness, and safety which determines whether he/she
will intentionally take, alter or stop medication. If individuals perceive that medication may
promote health and well-being and prevent complications they are more likely to take
medication. Individuals who perceive themselves as low risk for health problems are less likely
to take medication (Brooks, 1986; Johnson, Williams, & Marshall, 1999).
Patterned behavior is the degree to which individuals initiate and establish a ritual, habit,
or pattern of taking medications through access, routine and remembering (Johnson, 2002). If
individuals are committed to take their medication they may still become unintentionally non-
adherent due to the inability to access medications and interruption of routine, or a lack of
MEDICATION ADHERENCE 31
reminders (Hamilton et al., 1993; Johnson et al., 1999). Patients need to be able to access
medications physically and be able to pay for them in order to initiate treatment and maintain
medication taking. Patients also need to remember to take their medications, which is facilitated
through establishing a routine or having reminders that trigger their memory. Interruptions of an
individual’s routines may lead to unintentionally missed doses of medication.
Feedback is the third major concept of the MAM. Feedback is defined in the model as the
degree to which information, facts, prompts or events reinforce the need to maintain or modify
medication taking (Johnson, 2002). Feedback can be viewed in terms of the benefits, needs,
effectiveness, and safety of the medication treatment the member receives (Johnson et al., 1999;
Johnson, 2002).
The MAM was developed for chronic disease, which poses a low threat to the patients a
majority of the time; e.g., hypertension, osteoporosis or hyperlipidemia. For example,
hypertension is a disease considered to be a silent killer. There are no daily outward signs or
symptoms of the disease process for a patient to experience. Similarly, osteoporosis has no
outward signs to signal an individual that bone density is diminished. The model not only
identifies specific cognitive factors of medication taking in chronic illness; but, also
acknowledges that the cognitive component is only one of three domains associated with taking
medication.
Definition of Terms
Interactive Voice Response Systems (IVR)
IVR also known as Interactive Voice Response Systems is a technology that automates
interactions with telephone callers and communication systems. It is often called a telephone
linked to a “talking computer.” It can deliver recorded telephone messages, instructions,
MEDICATION ADHERENCE 32
reminder, or informational lectures, and can provide patients a means to interact with the system
in order to obtain health information or to record on-going health management efforts (Lee,
Friedman Cukor, & Ahern, 2003). It allows for an efficient exchange of information to or from a
database. IVR improves access to health care by extending care beyond the walls of a provider’s
office or the hospital setting, with health programs and messages available twenty-four hours per
day, seven days per week. IVR provides immediate feedback to the patient thus freeing up
clinical resources (Lee, et al., 2003). IVR can be defined in two ways. (1) The type of contact
(inbound and outbound calls to contact patients, and (2) the amount of interactivity, one way
transmission to patients or responses to data collection (Lee, et al., 2003). One-way
telecommunication transmission can be used to provide reminders or information. This can be
seen as a useful way to reduce forgetfulness by reminding subjects to refill or take medication.
The proposed project will use one-way telecommunication transmission through Interactive
Voice Response technology to remind patients who have unintentionally forgotten to refill their
medications by the due date. The call script is designed to educate and engage members on the
importance of medication adherence.
Proportion of Days Covered (PDC)
PDC is a calculation based on the fill dates and days supply for each fill of a
prescription. The PDC is not a simple summation of the days supply. The denominator for the
PDC is the number of days between the first fill of the medication during the measurement
period and the end of the measurement period. This means that a patient who discontinues the
medication during the measurement period will still be tracked through the end of the year, and
thus the non-persistence is accounted for in the PDC. The patient-level numerator for the PDC is
MEDICATION ADHERENCE 33
the number of days covered by the prescription fills during the denominator period (Benner, et
al., 2002) (see Appendix A for PDC calculation).
Adherence
The World Health Organization defines adherence as, “The extent to which a person’s
behavior (taking medications, following a recommended diet and/or executing life-style changes)
corresponds with the agreed recommendations of a health care provider” (Sabate, 2003, p. 13).
Non-Adherence
Cognitive (intentional) processes or behavioral components (unintentional) of a patient
to discontinue their medications as prescribed (i.e., twice daily), as well as whether they continue
to take a prescribed medication.
Intentional Non-Adherence
Patients undertake an active, reasoned decision-making process in relation to following
or levels of purposeful actions to follow or disregarding professional advice. (Playle & Keeley,
1998; Lahdenpera, 2000; Lowry et al., 2005).
Unintentional Non-Adherence
Patients’ passivity, toward three key factor groupings including patient factors, treatment
factors and patient professional factors. (Wroe, 2002; Lowry et al., 2005).
Full Intervention
The intervention consisting of the authenticated IVR call and a provider fax
IVR (Authenticated) Only Intervention
The intervention consisting of an authenticated phone call by a member.
MEDICATION ADHERENCE 34
Authenticated
This is defined as a member who picks up the phone and verifies full date of birth (for
example, January 1, 1936) and zip code.
Fax only Intervention
The intervention consisting of a fax to the provider informing them that a member is
non-adherent to a specific drug.
Control (No Intervention)
A member receives no IVR call and a provider receives no fax.
Compliance
Taking medication each day as prescribed 80% of the time.
Community Assessment
A United Healthcare proprietary holistic comprehensive assessment completed in
CareOne, either face to face or telephonic, by a nurse care manager, which includes socio-
demographic, environmental, support system, health status, and functional status of every
member enrolled in MyCarePath care management program.
MyCarePath Program
The holistic care management program for high risk members with complex medical
conditions. Each member has a primary nurse who leads a multidisciplinary team to assist
members in attaining their goals. Members interact with their primary nurse and other team
members via phone calls and face to face visits. Technology (biometric monitoring) is offered
for those members with heart failure. This program is offered to Medicare Supplement Health
MEDICATION ADHERENCE 35
Insurance Plan (SHIP) members living in the pilot markets who have pharmacy coverage
through UnitedHealthcare (Medicare Part D or other pharmacy coverage).
Pilot Markets
The states of New York, Florida and California-Los Angeles, North Carolina-
Greensboro Ohio-Cleveland.
Medicare Part D
Is a federal prescription drug benefits program to subsidize the costs of prescription
drugs for Medicare beneficiaries in the United States.
Medication Therapy Management Program (MTMP)
Is a Part D drug coverage benefit provided by pharmacists and providers to optimize
therapeutic outcomes through improved medication adherence.
United HealthCare
Is a national insurance company which offers several health plans for all ages across the
United States.
Cycle
A cycle is defined as each time a bi weekly IVR reminder call campaign runs.
Hierachical Condition Categories (HCC) Risk Score
“The CMS-HCC risk adjustment models are used to calculate risk scores, which predict
individual beneficiaries’ health care expenditures, relative to the average beneficiary. Risk scores
are used to adjust payments and bids based on the health status (diagnostic data) and
demographic characteristics (such as age and gender) of an enrollee. Both the Medicare
MEDICATION ADHERENCE 36
Advantage and Prescription Drug programs include risk adjustment as a component of the
bidding and payment processes” (Medicare Managed Care Manual, 2013). Individuals with a
lower HCC score would have lower risk (acuity) than those with a higher HCC score.
Index Date
Is the date of the first intervention for example, for member IVR only: the date first IVR
call was administered.
Pre-Index Period
Is a variable length up to a twelve month period prior to the index date. This will be
defined by index date minus 365 days or beginning of membership coverage, whichever is
shorter
Post-Index Period
Is of variable length: up to 365 days after index or March 31, 2014 (or the latest date
after 3 months claims run-out period), or until the individual is no longer covered by AARP
Medigap insurance, whichever is sooner.
Methods
Methods-Research Question One
The first research objective of the study was to explore the association between
interactive voice response (IVR) refill reminder calls with patient authentication and provider fax
reminder (full intervention) on medication adherence as measured by the Proportion of Days
Covered (PDC), compared to fax alone, IVR alone or no intervention among older adults with
chronic disease. Additional research was done to evaluate how each level of the intervention was
MEDICATION ADHERENCE 37
associated with the healthcare outcomes, that is, healthcare cost and utilization of services like
inpatient hospitalization and emergency room visits.
Design. This was a secondary data analysis of data previously collected for clinical
reasons which used an Interactive Voice Response reminder calls system, a technology that
automates interactions with telephone callers, and communication systems for non-research
purposes and links outcomes to a pharmaceutical claims analysis for a measure of medication
adherence. In the comparison groups analysis was done to compare PDC/adherence in the 7 drug
classes among the four intervention groups from the pre to the post period; (1) the number of
members that received the full intervention; (2) the number of members who received the call
intervention only-the member (authenticated) the refill, but the provider did not receive the fax;
(3) the number of members received the received the fax only provider intervention, the member
did not authenticate the refill reminder call and 4) the cases where both the members and their
providers cannot be reached, due to the fact that occasionally, an incorrect member telephone
number and provider fax are not in the data base.
Setting. The research for this study took place within United Health Group’s United
Healthcare Medicare and Retirement division which services more than three million Medicare
Supplement Health Insurance Plan members from March 2013 to December 2013. The study
participants were a group of non-adherent community dwelling members who received bi-
monthly, automated, HIPAA-compliant refill reminder phone calls with a contracted messaging
and communications vendor to their home telephone. United Health Group is the largest and
most diversified health care insurance company in the United States who serves more than 85
million individuals worldwide with $130 billion in revenues and over 165,000 employees.
MEDICATION ADHERENCE 38
Sample. The study was limited to members who lived in the five pilot markets of NY,
CA, NC, OH and FL. Approximately one hundred and forty thousand United Healthcare
members were eligible for the IVR intervention if they had Medigap coverage via AARP
Medicare Supplement Health Insurance Plan, and failed to refill a target medication within seven
days of the expected fill date and had a proportion of days covered of <80% for the delinquent
medication during the quarter.
Intervention. The focus of this project was to use IVR to test if further improvement in
medication adherence could be obtained over UHC’s historical baseline levels of adherence.
Reminder call content for this intervention was developed by the IVR vendor Silverlink, a cross
functional clinical team within United Healthcare including registered nurses, social workers,
and pharmacists. Calls used a structured algorithm to present the member specific information
described below. Calls lasted between 3-5 minutes depending on the number of medications the
patient had not refilled.
This research project focused on the IVR calls intervention that ran from March 2013 to
Dec 2013 for a total of 20 cycles. A cycle was defined as each time the bi-weekly IVR reminder
call campaign ran. Pharmacy claims data was first extracted from an electronic data base bi-
weekly by OptumRX. Then the eligible member demographic data, along with the class/name of
drug they were non-adherence to, was transmitted securely to the IVR vendor who automated the
reminder calls. The IVR system was programmed to automatically attempt to contact members a
maximum of three different times during each of the cycles. To protect member privacy, at the
beginning of each IVR call, members’ identities were verified by confirmation of full date of
birth (for example, January 1, 1936) and zip code. The IVR call entailed four components. There
was a reminder call script (see Appendix B for Call Script) directed to the patient seven days
MEDICATION ADHERENCE 39
after they had not filled the prescribed medication. The message specified the name of up to
three medications that required refills. If a member responded (authenticated), they had the
opportunity to listen to the call and complete a satisfaction survey, (see Appendix C satisfaction
survey) whose purpose was to try to understand if the reminder call as well as the reminder
information was helpful to members, and to be transferred to Nurse Health line if member
needed to discuss adherence or other issues. Additionally, if a member was unreachable, a
message was left for the member asking them to call back to hear the important health
information. Finally a faxed letter and report (see Appendix D) for satisfaction survey was sent
to the provider for every patient who was identified as non-adherent. The provider report
delivered the details of their patient’s year-to-date PDC and recent prescription refill history for
the identified medication.
Data collection/procedure. Data was requested from United Health Group’s database of
insured’s who had Medigap coverage via AARP Medicare Supplement insurance. Individual de-
identified member data was delivered in excel files containing the following variables; primary
and secondary diagnosis, demographic data such as age, gender, race, income, urban or rural
location, Medigap Plan Coverage type, (grouped into 2-3 categories), qualified/engaged in other
UHC pilot programs or activities. The race, income and urban indicators were inferred from the
census of the U.S. population data based on member’s zip code of residence. The UHC pilot
programs included care management, disease management or (MyCarePath), Nurse Healthline
utilization, End Stage Renal disease care, depression management, and a Medication
Management Therapy program. Presence of a current Advanced Directive, a completed PHQ-9
or Health Risk Assessment survey, and indictor for admission to a long-term care nursing facility
was also gathered. Other variables included major comorbidity status, pre-index period
MEDICATION ADHERENCE 40
healthcare expenditures, and HCC score. Further, indicators of having taken the following drugs
of interest were provided: antidepressants, SSRIs and SNRIs only, beta-blockers, calcium
channel blockers, anti-diabetic medications, bisphosphonates, renin angiotensin system
antagonists and statins. Table 2 lists the drugs included in the Pharmaceutical Adherence
program. Data also included a review of the follow-up number of months in the post-index
period. Additionally, the following elements were extracted from the claims data bases: hospital
admissions and readmission within 30 day; emergency room visits, and total cost of care.
Additionally, Silverlinks, the vendor, returned the outcomes of the authentication/call
termination status that helped to categorize the members into different cohorts for data analysis.
Data analysis. Statistical Package for Social Sciences (SPSS) v19 was used to analyze
data. The analysis took place on subjects that met the inclusion criteria. For example, some
members that could not be matched again with the United Healthcare data base were dropped
from the analysis. Member who did not have minimum 60 days in the pre-index period and 90
days in the post-index period were excluded. Similarly members with pre or post period total
monthly costs <=0.1, missing an HCC score or Medigap plan type or with special diseases HIV,
Hepatitis C, Sickle Cell Anemia, and Multiple Sclerosis were all dropped from the dataset.
Finally, members with lacking information in ZIP code level, race and income, as well as
hospital beds per 100 thousand, were dropped (Table 3).
Descriptive statistics including frequencies for categorical variables and mean (standard
deviation) for continuous variables were presented in tables for the overall sample and for each
of the four intervention groups. Aggregate PDC rates were calculated for all selected drugs and
displayed by treatment group. The health care cost was displayed descriptively by inpatient,
outpatient, ER visits, ancillary and pharmaceutical and total cost in pre and post periods and
MEDICATION ADHERENCE 41
were compared across the four intervention groups. Frequencies of ER visits, inpatient
admissions and long–term care admissions in pre and post periods were compared across four
intervention groups. Chi-Square p-values were used to measure the statistical differences in
categorical variables between the four groups of interest while ANOVA was used for the
continuous variables.
Analysis was conducted using seven separate data sets, one for each drug class, with a
separate regression model for each drug class. A member could be in more than one dataset
based on whether they were non-adherent for more than one drug classes. The first time a
member became non-adherent for a given drug class was selected as the index date for that drug
class. Thus, PDC was calculated for each drug class due to the fact that an individual’s index
date to determine adherence was variable. A member’s adherence index date would vary based
on the number of drug classes and their start date of non-adherence in a specific drug category.
Therefore, an overall PDC could not be calculated for all medications combined for each
individual.
Adherence. Although PDC >=80% was the original definition of success, that outcome
was not obtained, therefore a second definition was used “improvement in adherence after
intervention”. The change in adherence variable was defined as the absolute difference between
post PDC minus baseline PDC. In addition, in order to determine how sensitive the change in
PDC results were based upon the definition (> 80%), additional analyses were performed using
three PDC measures. First, improved adherence was defined as a change in PDC from <80% pre
intervention to > 80% post intervention (a dichotomous variable). Secondly, improved
adherence was defined as any positive change in PDC (>0%) from pre-period to post-period (a
MEDICATION ADHERENCE 42
dichotomous variable). Finally, improved adherence was defined as the absolute positive change
in PDC (a continuous variable).
Multivariate analysis. Analyses were conducted using a four level treatment variable
representing the level of treatment they actually received. A multivariate regression model was
used to predict the change in PDC across treatment while controlling for differences between the
non-randomized intervention groups at baseline. The independent variable was the IVR
intervention group with 4 levels of intervention with those receiving no intervention and living
outside the pilot market serving as the reference group. Potential confounders were any variable
that was statistically different between the intervention groups at baseline and was included in
the multivariable model if their inclusion changes the beta coefficient by more than 10% or was
itself an independent predictor of the outcome. Depending on the model used, the beta
coefficients indicated the magnitude and direction of the variable’s impact on cost, utilization
and adherence relative to the control group.
In addition, logistic regression models for each of the three types of utilization, inpatient
hospitalization, nursing home admissions and emergency room visits in the outcome period was
used to determine the odds of having a visit or healthcare utilization among those with IVR
interventions compared to the comparison group. The independent variable was the intervention
group. The dependent variable for the model was binary indicator for (a) whether the member
had hospital admission in the post period within 30 days or, (b) whether the member had an
admission to a long-term facility in the post period within 30 days or, (c) whether the member
had ER visits in the post period respectively within 30 days. The same process was followed for
identifying and adjusting for possible confounding factors, i.e. controlled for race and income.
MEDICATION ADHERENCE 43
Methods-Research Question Two
The second objective of the study was to evaluate the performance of Nurse Case
Management Service (MyCarePath). Specifically to evaluate whether implementing MyCarePath
program among a very high risk group of multi-comorbid older adults would:
(a)Decrease emergency room emergency room utilization, inpatient utilization, and mortality
or nursing home/rehab admissions?
(b)Improve medication adherence as measured by PDC in comparison to providing usual
care?
Design. To answer these research questions a retrospective nested case-control study was
conducted. Members for the study were initially identified from a group of 2700 high risk
pharmacy members identified from the UHC 2012 IVR campaign yet never enrolled in the
MyCarePath program. To be considered for inclusion a member had to be non-adherent to three
or more maintenance medications four or more times during the year and required a HHC risk
score higher than 2.75. This reduced the sample down to 1600 members. Finally, each of the
1600 members were run through a United Healthcare proprietary risk algorithm (propensity
model). A propensity score of 1 or higher was chosen as a cut point for inclusion. These analyses
reduced the eligible sample size to 881 members. The 881 members received a call from the
telephonic care managers to voluntarily enroll them into the MyCarePath program for closer
observation and education. Indicators of having taken the following drugs of interest:
antidepressants, SSRIs and SNRIs only, beta-blockers, calcium channel blockers, anti-diabetic
medications, bisphosphonates, renin angiotensin system antagonists and statins. Additionally,
the follow-up number of months in the post-index period and other elements were extracted from
the claims data bases; hospital admission; emergency room visits; utilization/total cost of care.
MEDICATION ADHERENCE 44
Setting. The study took place within United Healthcare’s Medicare and Retirement
division which services more than three million Medicare Supplement Health Insurance Plan
members. United Healthcare is a national insurance company which offers several health plans
for all ages across the United States. The study participants were Medicare Supplement Health
Insurance Plan (SHIP) members living in the pilot markets of NY, CA, NC, OH, and FL, who
voluntarily enrolled in MyCarePath program and who received monthly telephonic calls to their
home from case managers.
Sample. A sub population of 48 highest risk members who were enrolled in the
MyCarePath program were compared to a 3:1 matched sample of 211 propensity weighted
controls not enrolled in MyCarePath. Six percent (59) of the members accepted enrollment into
the program. Controls were chosen from a sub population of the IVR controls member pool
using age, gender and HCC scores and a 3:1 ratio.
Intervention. MyCarePath members were identified between August of 2013 and
August of 2014. They received monthly telephonic calls from case managers for assessment,
planning, facilitation, care coordination, evaluation and advocacy for options and services to
meet an individual’s and family’s comprehensive health needs through communication and
available resources to promote quality cost effective outcomes (CMSA, 2010). In addition, a
pharmacist on staff was available for consultation for medication issues and education for both
the members and the case managers. Controls met similar inclusion criteria and received usual
services. The usual care members met all of the eligibility criteria for enrollment into the
MyCarePath program but declined enrollment and therefore did not receive telephonic outreach
by a case manager.
MEDICATION ADHERENCE 45
Data collection. The researcher requested data from United Health Group’s database of
insureds who had Medigap coverage via AARP Medicare Supplement insurance. Individual de-
identified member data was delivered in Excel files containing the following variables; (1) age;
(2) gender; (3) primary and secondary diagnosis. Additionally, elements were extracted from the
claims data bases (4) hospital admission; (5) emergency room visits; (6) utilization/total cost of
care; (7) nursing home and or SNF admissions; (8) the number and % of members with
resolution of gaps in adherence (Adherence >80% for prescribed medication; and (9) adherence
pre-and post-intervention: measured by Proportion of Days Covered (PDC).
Data analysis. Data analysis mainly included descriptive statistics due to the small
sample size. Analysis was performed comparing the drug adherence, cost and utilization of
services between cases and controls. T-test and ANOVA tests were used for continuous variables
and Chi-squared test was used for categorical variable to explore statistical differences between
cases and controls.
Adherence. Although PDC >=80% was the original definition of success, that outcome
was not obtained, therefore a new definition was used ‘improvement in adherence after
intervention”. The change in adherence variable was defined as the absolute difference between
post PDC minus baseline PDC (a continuous variable).
Bivariate analysis. A paired t-test was done for matched variables and McNemar's test
was performed to investigate the impact of the MCP program on emergency room visits,
inpatient utilization, and nursing home admissions from the pre to post period.
Multivariate regression. For the change in PDC model, a linear regression model was
used. For the binary flags of PDC improvement over time, logistic regression model was used to
predict the likelihood of improvement in adherence. The independent variables were the
MEDICATION ADHERENCE 46
participants receiving nurse case management services in MyCarePath compared to those
receiving usual care but who qualified for enrollment into MyCarePath. Potential confounders
were any variable that was statistically different between the intervention group at baseline and
was included in the multivariable model if their inclusion changed the beta coefficient by more
than 10% or was itself an independent predictor of the outcome. Depending on individual model,
the beta coefficient indicated the magnitude and direction of the influence of participation in
MyCarePath for additional care on cost, utilization and adherence relative to the non-participant
group. In addition to the descriptive analysis of health services utilizations, the factors associated
with inpatient hospitalization, nursing home admissions and emergency room visits in the
outcome period were explored using separate logistic regression models. The independent
variable was the intervention group. The dependent variable for the model was binary indicator
for (a) whether the member had hospital admission in the post period within 30 days or, (b)
whether the member had an admission to a long-term facility in the post period within 30 days
or, (c) whether the member had ER visits within 30 days in the post period respectively. The
same process was followed for identifying and adjusting for possible confounding factors.
Results
Research question #1 (IVR)
Demographics
Women made up 18,878 (63%) of the study population. The average age of identified
members was 77 years old with individual participant ages ranging from 51-104 of age. Seventy
eight percent 23,398 members were between 64 and 84 years of age. Nineteen percent were over
85 year of age. Forty percent 11,844 of the participants lived in racially non-diverse areas (less
than 15% non-white) and 62% of the study population had income in the top 15% based on the
MEDICATION ADHERENCE 47
national median. Finally, forty-five percent of the participants lived in New York (13,608) while
the lowest (%) lived in Florida 3303. There were statistically significant differences in baseline
characteristics between the four groups in all drug classes except for gender, as shown in the
descriptive Tables 4-10. Variables such as age, race, income and geographic location showed a
clinically insignificant yet statistically significant difference.
Twenty-nine thousand seven hundred and ninety-two unique members were included in
the study. There were 7,421 people who received an IVR phone call and their medical provider
received a fax notification (full intervention); 10,937 people who didn’t authenticate but their
medical provider received a fax notification (faxed only); 878 people who received IVR phone
call only (authenticated only) and there were 18,179 people in the comparison (reference) group
who could not be authenticated nor were their provider’s sent a fax. See Figure 1.
Only about 6% could not be authenticated because a correct number could not be
obtained from the claims data base or their provider faxed due to the same reason. The group that
only authenticated was always the smallest group. To create more stable control group additional
members were drawn from those who were not enrolled in the IVR program living outside the
pilot markets, but who met the same non-adherence criteria. The control group was the largest
group in the study sample.
Medication Groups
The highest numbers of members included in the study were on statin medications
(10,942) while the lowest numbers of members were taking osteoporosis medications (805).
There were (4,823) member include in the study taking antidepressants and (3,357) taking
calcium channel blockers. Finally there were (7,359) on beta blockers (2,587) on diabetes
medication and (6,644) on RAS antagonists. Some individuals could be in more than one drug
MEDICATION ADHERENCE 48
class based on the number of drugs they were prescribed. For example a member’s non-
adherence to any of the seven drug categories would trigger inclusion into the study.
Intervention Group Size by Drug Class
In general about 20 % of participants in most drug groups received the full intervention
while between 17-32 % received just the fax only and a much smaller portion 1-3% received just
the IVR phone call. The proportion of participants in each intervention group varied by drug
category, see Figure 2 for more details.
Pre-period adherence by drug category. Baseline adherence averaged 65% in the total
study population with a standard deviation of (+/-1). The average adherence in all three
intervention groups receiving reminders was 64% compared to 65% in the control group. There
was no significant difference in adherence between users of beta blockers, diabetes and
osteoporosis medications at baseline. However, statistically significant differences in adherence
were found at baseline among users of antidepressants, calcium channel blockers, RAS
Antagonist and statins all with a (p-value < 0.0001).
Post-period adherence by intervention. There was a statistically significant difference
found between the intervention groups in the post period for users of beta blockers (p-value <
0.0195), calcium channel blockers (p-value < 0.0184), RAS Antagonist (p-value < 0.0007) and
statins (p-value < 0.0030). See Figure 3 for details of PDC by drugs and treatment category pre
to post-period.
Change in PDC cut-points. Changing the cut-point for the PDC definition only slightly
changed the results and did not change the study findings. First, using a PDC cut-point of PDC
>=0.8 resulted in users of antidepressants (p-value < 0.0001), beta blockers (p-value < 0.0003),
MEDICATION ADHERENCE 49
calcium channel blockers (p-value < 0.0017), RAS Antagonist (p-value < 0.0001), and statins (p-
value < 0.0001), in the faxed only group being statistically significant. Then, using a PDC cut-
point of PDC difference> =0 resulted in users of RAS Antagonist (p-value < 0.0058) and statins
(p-value < 0.0421), in the faxed only group being statistically significant. See Figures 4-7 for
odds ratio details.
Change in adherence after interventions. Absolute change in adherence for
intervention groups, ranged from -0.08 percent to 0.02 percent (std dev 0.03) compared to -0.08
percent to -0.02 percent in the control group. The PDC average drop was found to be smaller in
the intervention groups compared to the control group. See Figure 8 for detailed differences
across interventions.
Multivariate Models for Adherence
After adjusting for race and income there were small, although statistically significant,
the reduction in PDC was less for the treatment groups compared to control. The PDC for three
of the seven drug classes’ antidepressant, beta blockers and osteoporosis medications were found
to be about 2% statistically significantly higher with either the full intervention or the fax only
intervention compared to control. The PDC for users of calcium channel blockers was
significantly higher only for the full treatment group compared to the control group. The
authenticated only intervention showed no difference compared to control in any drug category.
See Figure 9 and tables 11-17.
Change in Prescription Drug Costs.
There were no significant differences in prescription drug costs associated with program
participation. See Figure 10.
MEDICATION ADHERENCE 50
Change in Total Costs
There were two significant differences in total costs associated with program
participation. The individuals with statins drugs who were in the full intervention group had
savings of $241 (P<0.008) compared to control. Individuals using calcium channel blockers who
were in the faxed only group had a savings of $553 (P<0.01) See Figure 11.
Impact of Interventions on Health Care Utilization
Emergency room utilization/analysis. There was a statistically significant reduction in
ER admissions for the users of antidepressants who received the full treatment intervention (RR
= .834; 95% CI: .693–1.00; (p-value < .055) compared to control in reducing ER visits. The odds
of an ER visit among users of antidepressants was 17% less than those in the control group.
There was a statistically significant reduction in ER admissions for the users of calcium
channel blockers who received the fax only intervention (RR = .748; 95% CI: .613–.913; (p-
value < .004) compared to control in reducing ER visits. The odds of an ER visit among users of
calcium channel blockers was 26% less than those in the control group. There was no difference
in ER visits in any of the other drug classes compared to control. See Figure 12 and Table 20A.
Among user of statins a statistically significant reductions in ER visits was found with
both the full treatment intervention (RR = .773; 95% CI: .676–.988; (p-value < .000) and the fax
only intervention (RR = .839; 95% CI: .745–.945; (p-value < .004). The odds of an ER visit
among statin users was between 17-33 % less than the control. The authenticated reminder call
only intervention did not result in a difference in ER visits compared to the control group
(Table24A).
MEDICATION ADHERENCE 51
Among users of RAS Antagonist a statistically significant reductions in ER visits was
found with fax only intervention (RR = .857; 95% CI: .745–.991; (p-value < .037) (Table 22A).
The odds of an ER visit among users of RAS Antagonist was 15% less than those in the control
group. Finally, beta blockers (Table19A), diabetes (Table21A), and osteoporosis medications
(Table23A), revealed no statistically significant decrease in ER visits compared to control.
Inpatient utilization. Among beta blocker users, a statistically significant reduction in
inpatient utilization with the full intervention was found (RR = .787; 95% CI: .645–.960; (p-
value < .007), and with the fax only intervention (RR = .826; 95% CI: .826–.978; (p-value <
.026) compared to control. Finally, among the authenticated reminder call only group there was
no difference in inpatient admissions compared to the control group. See Figure 13 and Table 19
B for further details.
Among calcium channel blocker users a statistically significant reduction in inpatient
utilization with the fax only intervention group (RR = .713; 95% CI: .552–.921; (p-value < .010),
was found compared to control, while the full intervention and the authenticated reminder call
only were no different compared to control. See Figure 13 and Table 20B.
Among anti diabetic medication users a statistically significant reduction in inpatient
utilization with the full intervention group was found (RR = .697; 95% CI: .490–.991; (p-value <
.045) as was the fax only intervention (RR = .678; 95% CI: .505–.910; (p-value < .010)
compared to control. Finally, the authenticated reminder call only was not different. See Figure
13 and Table 21B.
Among RAS Antagonist users a statistically significant reduction in inpatient utilization
with the full intervention group was found (RR = .821; 95% CI: .657–1.025; (p-value < .082)
MEDICATION ADHERENCE 52
compared to control. The fax only intervention and the authenticated reminder call only were not
different. See Figure 13 and Table 22B.
Among osteoporosis medication users a statistically significant reduction in inpatient
utilization with the full intervention group was found (RR = .326; 95% CI: .111–.958; (p-value <
.042) compared to control. The fax only intervention and the authenticated reminder call only
were not different (Table 23B). Therefore the odds of an inpatient admission among beta
blocker, calcium channel blockers, diabetes medication, RAS Antagonist and osteoporosis
medication users ranged between 18-68% less than the control group.
Nursing home utilization/admissions. There were statistically significant differences in
nursing home utilization found in all drug classes except calcium channel blockers. Among
antidepressant users a statistically significant reduction in nursing home utilization was found
with the full intervention (RR = .662; 95% CI: .503–.871; (p-value < .003) and the fax only
intervention (RR = .775; 95% CI: .612–.982; (p-value < .035) compared to control. However,
the authenticated reminder call only was not significantly different compared to the control
group. See Figure 14 and (Table 18C).
Among beta blockers users a statistically significant reduction in nursing home utilization
was found with the full intervention (RR = .787; 95% CI: .0645–.960; (p-value < .018), and the
fax only intervention (RR = .826; 95% CI: .697–.978; (p-value < .026) compared to control.
Finally, the authenticated reminder call only was not different compared to control. See Figure
14 and (Table 19 C).
Among diabetes medication users a statistically significant reduction in nursing home
utilization was found with the full intervention (RR = .487; 95% CI: .298–.797; (p-value < .004)
MEDICATION ADHERENCE 53
compared to the control group. The fax only intervention and the authenticated reminder call
only were not different compared to the control group. See Figure 14 and (Table 21 C).
Additionally, among RAS Antagonist users a statistically significant reduction in nursing
home utilization was found with the full intervention (RR = .326; 95% CI: .111–.958; (p-value <
.042) compared to the control. The fax only intervention and the authenticated reminder call
only were not different compared to the control. See Figure 14 and (Table 22 C).
In addition, among osteoporosis users, a statistically significant reduction in nursing
home utilization with the full intervention was found (RR = .224; 95% CI: .050–.1.004; (p-value
< .051) compared to control. The fax only intervention and the authenticated reminder call only
were not different compared to the control group. See Figure 14 and (Table 23 C).
Finally, among statins users a statistically significant reduction in nursing home
utilization with the full intervention was found (RR = .538; 95% CI: .421–.686; (p-value < .001)
and the fax only intervention (RR = .791; 95% CI: .653–.959; (p-value < .017) (Table 24 C)
compared to control. The authenticated reminder call only was not different. Lastly, beta
blockers were not different with any intervention compared to the control. See Figure 14 and
Table 20 C for more details. Therefore the odds of a nursing home admission among
antidepressants, beta blocker, diabetes, RAS Antagonist, osteoporosis and statin users ranged
between 18-88% less than the control.
Results
MEDICATION ADHERENCE 54
Research question #2 MyCarePath (MCP) participants
Demographics
Two hundred and fifty-nine participants were included in the study. Forty eight members
were enrolled in MCP and two hundred and eleven were included in the reference group. Women
made up 88 (34%) of the study population. The average age of identified members was 77 years
old with individual participant ages ranging from 64-96 years of age. Forty-nine percent (126)
members were between 64 and 74 years of age. Twenty percent were over 85 year of age. Fifty
percent (131) of the participants lived in racial non-diverse areas (less than 15% non-white) and
70% of the study population had income in the top 15% based on the national median. Finally,
eighty percent (206) of the participants lived in New York.
There were statistically significant differences in baseline characteristics between the
intervention and control group with regard to race only. Variables such as age, gender, income
and geographic location were not statistically significantly different. The highest numbers of
members included in the study were on beta blocker medications 91 (35%) while the lowest
numbers of members were taking osteoporosis medications 9 (3%). There were 51(19%)
members included in the study taking antidepressants and 50 (19%) taking calcium channel
blockers. Finally there were 51(19%) on anti-depressants 28 (10%) on diabetes medication and
40 (15%) on RAS antagonists. Some individuals could be in more than one drug class based on
the number of drugs they were prescribed as was the case in the IVR study.
Pre-period-post-period Adherence
Adherence averaged 71% in the study population in the pre period with a standard
deviation of (+/-.092). The MCP participants average adherence was 76% compared to 65% in
the control group with adherence ranging from 66 percent to 94 percent in the MCP group
MEDICATION ADHERENCE 55
depending on drug group with a standard deviation (+/-.092) and 62 percent to 66 percent in the
control groups depending on drug group with a standard deviation (+/-.015).
Adherence averaged 71% in the study population in the post period with a standard
deviation (+/-.09). The MCP participants average adherence was 71% compared to 61% in the
control group with adherence ranging from 64 percent to 80 percent in the MCP group with a
standard deviation (+/-.05) and 52 percent to 64 percent in the control groups with a standard
deviation (+/-.036).
Adherence by Drug Category
There was no significant difference found with any of the medication classes. The PDC
for stain medications (4.1% higher) was found to be the highest compared to the usual care
group. Antidepressants medications were (2.6% higher), beta blockers (5.9% higher), calcium
channel blockers (3.2% higher), osteoporosis (1.9% higher) were all found to be less of a
reduction compared to usual care. The PDC for diabetes (-8.3%) and RAS Antagonist (-1.2%)
medications were found to be lower than those in the usual care group. Results can be seen in
tables 25-31. In addition, there was no significant difference in prescription drug costs associated
with program participation. See the details in Table 32. Finally, there was no significant
difference in total costs for those enrolled in MCP. See the details in Table 33.
Absolute Change in Adherence
Absolute change in adherence for MCP program population receiving an intervention,
ranged from -0.10 percent to -0.02 percent compared to -0.04 percent (std dev 0.03) in control.
The PDC average drop was found to be smaller in the intervention groups compared to the
control group.
Change in Drug Costs and Total Costs
MEDICATION ADHERENCE 56
There was no significant difference found in drug costs or total cost between members
receiving care-management services and the usual care cohort pre to post-period.
Impact of Interventions on Outcomes
Bivariate analysis. A paired samples t test revealed a statistically reliable difference
between the mean number of the MCP participants (M = .19, s = .389) and usual care (M = 1.63,
s = .779) participants that the groups have, t(258) = -19.897, p = .000, α = .05. McNemar's chi-
square statistic suggests that there is not a statistically significant difference in the MCP group.
Additional multivariate logistic regression models were used to estimate healthcare
utilization in the post period for emergency room (ER) visits inpatient (IP) utilization, and long-
term care (LTC) admission interventions compared to control.
There was no significant reduction in ER visits, inpatient utilization or nursing home
admissions associated with program participation. (Table 34-36). During the post period 20.8%
(10) of MCP patients and 27.5% of usual care patients were hospitalized; 12.5% (6) of MCP
patients and 18% (26) of usual care patients were seen in the emergency department; 8.3% (4) of
MCP patients and 15.6% (18) of usual care patients were admitted to nursing homes. When
compared to findings in the literature (Osterberg et al., 2005; Doggrell, 2010) found medication
non-adherence is responsible for at least 10% of hospitalizations while (Strandberg, 1984;Brown
& Bussell, 2011) found that nearly one quarter of nursing home admissions are related to the
inability of patients to properly self-administer medications.
Additionally, the analyses of this study demonstrated that program participation did not
improve PDC nor was a statistically significant difference found in PDC in any medication
group. This is similar to the findings of (Brown & Bussell, 2011) that adherence with chronic
MEDICATION ADHERENCE 57
medical therapy is <50% at 6 months following the initial prescription. However, members in
MCP taking osteoporosis medications remained adherent throughout the study. In the pre-period
member’s adherence level was 94% but dropped in the post-period to 80%. Members taking
calcium channel blockers started out adherent (82%) but dropped to (73%) and became non-
adherent in the post period. Those participants on RAS antagonists in the pre-period were very
close to being adherent at 79% but their adherence also dropped in the post-period to 76%. The
usual care group was non-adherent in both the pre- and post- period with the highest percentage
of adherence at 66% in all the drug classes except the osteoporosis medications which only
reached 62%. In addition members taking osteoporosis medications saw the greatest drop in
PDC from the pre to post period (62%-52%).
As a comparison, Chan and Cooke (2008) found there were no statistical differences in
adherence for statins, beta-blockers, and ACE inhibitors/angiotensin receptor blockers (ARBs)
between care management and usual care groups. Data demonstrated that adherence to
recommended medications regimes decreased over time, with 3-year medication continuation
rates of 44%, 48%, and 43% β-blockers, and ACE inhibitors/ARBs, respectively. These results
are of interest because the study included older patients who were well-insured with relatively
low out-of-pocket expenses for prescription drugs. The higher rate in adherence from the
participants in the current study, although initial identified as non-adherent in 2012, can be
explained by the studies broad inclusivity (requiring just one low PDC for one drug of interest
to qualify in the pre-index period in 2012).
Discussion
MEDICATION ADHERENCE 58
The discussion is divided into two sections. The first section will focus on the findings
from the first two study questions and the later section will focus on the results of the MCP
research.
A relatively inexpensive, population-based Pharmacological Adherence program was
designed to address one or more medication adherence gaps among Medigap insureds with CAD,
CHF, diabetes, depression, or osteoporosis. The key features that distinguished this program
from most MTM programs include its broad inclusivity (requiring just one low PDC for one drug
of interest to qualify in the pre-index period). In addition, this program allowed for transfer to a
registered nurse to discuss adherence or other issues or to make referrals to disease management
programs for four of the five conditions of interest although only 1373 (8%) of participants who
authenticated transferred to Nurse Health Line. Thus the new Pharmacological Adherence
program served the needs of a broader group of Medigap enrollees in multiple ways, with the
goal of helping them increase medication adherence.
This project’s inquiry began with the investigation of the first research question, “how
will implementing an interactive voice response (IVR) refill reminder call program plus provider
fax notification (full intervention) of non-adherence status among a very high risk group of
multi-comorbid older adults influence medication adherence in seven drug classes as measured
by the Proportion of Days Covered (PDC), compared to IVR alone, or provider fax alone or no
intervention among older adults with chronic disease? The analyses of this study demonstrated
that program participation did not improve PDC. The randomized IVR studies referenced in the
literature review demonstrated improvements in PDC from the pre to post period (Okeke et al.,
2009; Sherrard et al., 2009; Bender et al., 2010; Stacey et al., 2011), although only one study
found an adherence rate above 80% (Fulmer et al.,1999). However, Castle et al. (2012) found
MEDICATION ADHERENCE 59
little impact from IVR using a quasi-experimental design with over 39,000 adults newly given
antidepressants. Results demonstrated a five-fold improvement in adherence for those over 65
years of age compared to younger adults (ages 18-24). Unlike the above studies found in the
literature, this IVR study used a convenience sample of members who were free to choose to
participate or not.
The second research question was “how is each intervention associated with healthcare
outcomes, specifically healthcare cost and utilization of services like inpatient hospitalization
and emergency room visits.” The analyses found that IVR interventions with several drug classes
and interventions were statistically significantly associated with a decrease in emergency room
visits, inpatient visits and nursing home admissions for this population. This association persisted
after adjusting for other variables, including age, sex, race, income, geographic location. Further,
the program wasn’t associated with an increase in savings in either prescription drug or total
costs.
In contrast to other studies in older adults (Bayer &Tadd, 2000), we placed no upper age
limit on eligibility in order that our results would be applicable to very elderly individuals.
Subjects in the current study were drawn from a United Healthcare SHIP population. The extent
to which the observed impact of the intervention would generalize to other populations (e.g.,
Medicare Advantage patients or patients with no regular source of care) or the elderly is
unknown.
This study was piloted among a cohort of homogenous English-speaking community-
dwelling seniors. Certain characteristics, age, sex, geographic location, were not associated with
an increase in utilization. A lower PDC in the seven drug classifications may be more
MEDICATION ADHERENCE 60
widespread among certain ethnic and racial groups (Krousel-Wood, Muntner, Islam, Morisky, &
Webber, 2009).
Finally, there are several plausible explanations for the much lower PDC percentages in
the IVR study including: study designs, sample sizes, a different patient population and diseases,
number and type of drugs, and length of the studies. Okeke et al. (2009) tested the use of IVR
with glaucoma patients, using eye drops. The duration, intervals for intervention and design were
very different. In addition the IVR component had a built in questionnaire with an opportunity to
respond to questions. Bender et al. (2010) tested the use of IVR and combined it with educational
content with a very small sample (50) subjects’ ages 18 to 65 years old diagnosed with only
asthma, for one month, for only 10 weeks. This study was a randomized smaller sample with
different intervals and shorter duration. Friedman et al. (1999) tested IVR with much smaller
sample, on only antihypertensive medication with weekly reminders over a six month period
verses this IVR study in which subjects ages ranged from 64-104 and received bi- monthly IVR
reminders over a year. Fulmer et al. (1999) tested daily video-telephone with cardiac medication
and a much shorter duration- (ten weeks)- resulting in adherence of 83%. Stacey et al. (2009)
studied IVR for a shorter duration- (6 months) with a younger population (mean age 54) only
using statins resulting in a 9% increase in adherence (60.70% to 70.40% P<0.05). Sherrard et al.
(2009) tested eleven IVR messages over a shorter duration-(six months) - with smaller sample
(331) seniors (mean age 64). One similarity to this IVR study was the fact that patients were
asked if they were continuing to take each medication and then offered the option of hearing
more information. Fifty six percent of the participants in the study requested more information
on their medication and 25% listened to information on more than one medication compared to
only 8% in current study.
MEDICATION ADHERENCE 61
All of the RCT’s found greater medication adherence among treatment group participants
than among those in the control group in contrast to the current study. All of the studies used
much shorter term treatments compared to this study, with none longer than six months, and the
timing of interventions varied as well from daily, weekly, biweekly and monthly intervals.
Sample sizes were dramatically smaller, except for (Castle et al., 2012) along with the study
designs. The differences in study designs, sample size, different patient populations and
diseases, number and type of drugs, and finally the length of the studies may explain some of the
differences between the current study and those found in the literature.
To answer the second objective of the study; specifically to evaluate whether
implementing MCP program among a very high risk group of multi-comorbid older adults
would: Decrease emergency room emergency room utilization, inpatient utilization, and nursing
home admissions? Improve medication adherence as measured by PDC in comparison to
providing usual care? The analyses found that fewer care-managed patients were less likely to
have a diagnosis of hypertension, congestive heart failure, and diabetes and had slightly lower
HCC scores compared to control. These differences as well as differences in race, income and
utilization were small, although sometimes statistically significant, and were controlled for in the
model. Members enrolled in MCP were associated with a decrease in emergency room visits,
inpatient visits and nursing home admissions for this population although not statistically
significant. There was a statistically significant difference found in the pre-period for nursing
home admissions (p-value < 0.0484). The MCP utilization findings are consistent with the two
year evaluation of the entire MCP program done for the years 2008 and 2009. The program was
associated with increased quality of care; however, most of these increases were not statistically
significant. The program was also associated with cost savings; however, the decreases in costs
MEDICATION ADHERENCE 62
were not statistically significant. Finally, based on sensitivity analyses, the savings estimates
were sensitive to the inclusion or exclusion of a few participants with very high or very low
expenditures (Ronald Ozminkowski & Diane Cempellin, “Description of a High Risk Case
Management (HRCM) program” (presentation, American Society on Aging Conference,
Washington, DC, March 31, 2012). Chan and Cooke (2008) found there were no statistical
differences in utilization associated adherence for statins, beta-blockers, and ACE
inhibitors/angiotensin receptor blockers (ARBs) between care management and usual care
groups. However, Kumar & Klein (2013) found a greater reduction in emergency department
utilization among patients enrolled in case management interventions compared with those who
were not enrolled.
Limitations
There are several limitations of the IVR study. First, there was no follow-up to OptumRX
from any provider as a result of the fax intervention. Therefore, we were unable to determine if
the provider reviewed the fax, contacted the member, or took any action as a result of the provide
letter fax intervention. Second, there were limitations in the study population, which was a
convenience sample of AARP members who are reported to be very “over‐sampled.” There was
no incentive provided to participate or complete the survey which has been known to lead to low
response rates in other surveys. Using a randomized sample would have allowed for more equal
distribution between the intervention and control groups, the difference between groups raises
questions about unmeasured confounders. However, it is important to note that given the sample
size, and the homogeneity of this sample, the findings could be generalized to a similar senior
patient population. Third, it can be difficult to engage the desired member on the IVR call as
seen by the authenticated only group size. Edits to the IVR script to sound more like a typical
MEDICATION ADHERENCE 63
telephone call may help resolve this issue. Additionally, there was no method to estimate how
many patients had caller ID, or were not at home when the calls were placed and whether that
impacted authentication rates. Fourth, since the satisfaction survey was placed at the end of the
reminder call we were unable to conclude how many members found the IVR reminder call
helpful as we could not determine which members obtained the reminder information but did not
complete the survey. Fifth, the study did not address failure to adhere with other classes of
medications; therefore, we were unable to measure the impact on a member’s individual overall
PDC, particularly if they were taking other medications. We did not have information on
adherence with the other drug classes.
The final limitation was the use of a pharmacy claims data to collect member adherence
information. Only claims processed through the claims system are reflected in the data. Sample
medications dispensed in a provider office or medications purchased outside of the claims
system are not captured in the data. Adherence measures were calculated based on claims data;
the assumption was that prescriptions filled were taken by the patient.
There are limitations to the MCP study with some being similar to the IVR study. There
were limitations in the study population, which was a very small sample of AARP members
enrolled in MCP. A key question given the criteria used to create the sample is whether the
results can be generalized to all seniors with chronic complex conditions. Although differences
in baseline values between members of the treatment and control group were small, these
differences could, in principle, bias the estimates.
The study did not address failure to adhere with other classes of medications; therefore,
we were unable to measure the impact on a member individual overall PDC, particularly if they
were taking other medications. Finally, only pharmacy claims processed through the claims
MEDICATION ADHERENCE 64
system are reflected in the data. Adherence measures were calculated based on claims data; the
assumption was that prescriptions filled were taken by the patient. In summary, the results
reported here are important. The study demonstrated the effect that telephonic care management
may have in helping senior with complex care needs in changing behaviors, lowering
hospitalization rates and health care costs.
Conclusion
Suboptimal medication adherence is common among Medicare and Medigap enrollees
(Corkrey & Parkinson, 2012). This population can benefit from medication adherence
interventions to maximize prescription drug utilization. Interventions are needed that can
broaden the reach into Medigap populations beyond those currently delivered by MTM
programs. Use of innovative communication technology programs to change health behavior is a
developing field of investigation, and not all attempts to change behavior with such technologies
have been successful. PDC change from the pre period to the post period was negatively
associated. However, The IVR the study found a significant reduction of ER visits, inpatient
utilization and nursing home admissions among the treatment groups compared to controls.
There was not a significant reduction in prescription costs or total costs. The MCP study found
members receiving care-management services increased drug utilization compared with control
subjects while decreasing total costs. As tough health policy choices are made in the future
regarding the allocation of finite resources for health care, the use of IVR does not support an
effective means for improving PDC, while case management programs for seniors with chronic
disease might be attractive option for effective improvements in behavior change and lowering
health care costs.
MEDICATION ADHERENCE 65
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Figures
Figure 1
02000400060008000100001200014000160001800020000
Full Intervention Fax only Authenticatedonly
Control
Members
Interventions
Sample Size by Treatment Groups
MEDICATION ADHERENCE 77
Figure 2
02000400060008000
1000012000
Members
Drug Classes
Sample Size by Drug Class by Treatment Group
Control
Authenticated only
Fax only
Full Treatment
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Figure 3 PDC by Drugs and Treatment Category Pre to Post-period
Antidepressant Beta‐Blocker Ca Channel Blocker Diabetes Osteoporosis
RAS Antagonist Statin
Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post Pre Post
Full Treatment 0.64 0.59 0.64 0.62 0.65 0.63 0.65 0.64 0.68 0.62 0.64 0.63 0.63 0.59
Fax Only 0.63 0.58 0.64 0.61 0.64 0.60 0.64 0.62 0.66 0.59 0.64 0.62 0.63 0.59
Authentication Only
0.63 0.58 0.65 0.63 0.64 0.62 0.64 0.66 0.66 0.62 0.64 0.60 0.62 0.60
Control 0.65 0.58 0.65 0.60 0.66 0.61 0.65 0.63 0.65 0.57 0.66 0.63 0.65 0.60
MEDICATION ADHERENCE 79
Figure 4
0.99
0.95
0.88
0.83
0.61
0.79
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Full intervention
Fax Only
Authentication Only
Odds Ratio
Antidepressants‐Odds of Improving Adherence using 80% Cut‐Point or PDC Difference >=0 Compared to Control Analysis
pdc >= 0.8
pdcdif>=0
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Figure 5
1.06
1.02
0.98
0.91
0.74
0.96
0.00 0.50 1.00 1.50
Full intervention
Fax Only
Authentication Only
Odds Ratio
Beta Blockers‐odds of Improving Adherence using 80% Cut‐Point or PDC Difference >=0 Compared to Control Analysis
pdc >= 0.8
pdcdif>=0
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Figure 6
1.03
0.85
0.72
0.94
0.68
0.65
0.00 0.50 1.00 1.50
Full intervention
Fax Only
Authentication Only
Odds Ratio
Ras Antagonists‐Odds of Improving Adherence using 80% Cut‐Point or PDC Difference >=0 Compared to Control Analysis
pdc >= 0.8
pdcdif>=0
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Figure 7
0.96
0.91
0.99
0.86
0.72
0.67
0.00 0.50 1.00 1.50
Full intervention
Fax Only
Authentication Only
Odds Ratio
Statins‐Odds of Improving Adherence using 80% Cut‐Point or PDC Difference >=0 Compared to Control
pdc >= 0.8
pdcdif>=0
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Figure 8 Absolute Changes in PDC After Intervention For All Drug Categories
Full Treatment Fax only IVR only Control
Average Absolute Change % ‐0.03 ‐0.04 ‐0.02 ‐0.05
Median Absolute Change% ‐0.02 ‐0.02 ‐0.02 ‐0.02
Min Range Change % ‐0.06 ‐0.08 ‐0.05 ‐0.08
Max Range Change % ‐0.01 ‐0.02 0.02 ‐0.02
Standard Deviation 0.02 0.02 0.02 0.02
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Figure 9
2.1 2.1
3.5
1.6
2.1
1.1
0.7
2.3
1.72.0
0.4
2.3
‐0.2 ‐0.2
1.9 1.8
3.0
3.7
1.9
3.9
2.3
‐0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
PDC %Chan
ge
Drug Classes
Analysis PDC Percentage Change by Drug Class Compared to Control
Full Intervention
Fax only
Authenticated only
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Figure 10
‐$60
‐$40
‐$20
$0
$20
$40
$60
$80
$100
$120
Drug Classes
Changes in Drug Cost By Treatment Group by Drug Categories
Full Treatment
Fax only
Authenticated only
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Figure 11
Figure 12
‐$1,000
‐$500
$0
$500
$1,000
$1,500
$2,000
Dollars
Drug Classes
Change in Total Cost By Treatment Group by Drug Categories
Full Intervention
Fax only
Auth
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0 0.2 0.4 0.6 0.8 1 1.2
Antidepressant
Beta‐Blocker
Ca Channel Blocker
Diabetes
Osteoporosis
RAS Antagonist
Statin
Odds of ER Admissions for IVR Compared to Control Analysis
Auth
Fax
Fax+Auth
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Figure 13
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Antidepressant
Beta‐Blocker
Ca Channel Blocker
Diabetes
Osteoporosis
RAS Antagonist
Statin
Odds Ratios
Drug Classes
Odds of Inpatient Admissions for IVR Compared to Control Analysis
Authenticated Only
Fax Only
Full Intervention
MEDICATION ADHERENCE 89
Figure 14
0.00 0.20 0.40 0.60 0.80 1.00 1.20
Antidepressant
Beta‐Blocker
Ca Channel Blocker
Diabetes
Osteoporosis
RAS Antagonist
Statin
Odds Ratios
Drug Classes
Odds of Nursing Home Admissions for IVR Compared to Control Analysis
Authenticated Only
Fax Only
Full Intervention
MEDICATION ADHERENCE 90
Tables and Graphics
Table 1 Intervention Studies That Use IVR-Randomized and Quasi-Experimental
MEDICATION ADHERENCE 91
Table 2 Drugs included in the Pharmaceutical Adherence Program
Chronic Condition Drug Class Drug Main Use/Effects Single condition CAD Calcium Channel
Blocker Containing Medication
elerenone/Inspra® Reduces blood pressure
Depression
Antipsychotic Containing Medication
haloperidol, clozapine, risperidone, Geodon®, Seroquel®, Zyprexa®, Abilify®
Improves mood changes or depression
Bupropion buproprion Affects specific chemicals within the brain to treat depression or mood swings
Lithium lithium Improves mood changes or depression Mirtazapine mirtazapine Improves mood changes or depression Nefazodone nefazodone Improves mood changes or depression Selective Serotonin Reuptake Inhibitor (SSRI)
Citalopram/Celexa®; fluoxetine/Prozac®; sertraline/Zoloft®; paroxetine/Paxil®, Pexeva®; Lexapro®; Symbyax®
Improves depression by affecting serotonin levels in the brain
Serotonin Norepinephrine Reuptake Inhibitor (CNRI)
Venlafaxine/Effexor®, Pristiq®; duloxetine/Cymbalta®
Improves depression by affecting specific chemicals levels in the brain
Tricyclic Antidepressant
amitriptyline, imipramine, nortriptyline
Improves depression and other conditions
Diabetes Alpha-Glucosidase Inhibitor
acarbose/Precose®, miglitol/Glyset®
Lowers blood sugar by delaying the rise in blood sugar level after meal
Biguanide Containing Medication
Metfomin Reduces blood sugar level
Exenatide exenatide/Byetta® Reduces blood sugar level Sulfonyluria Glimepridine, Glipizide,
glipizide ER, Glyburide Lowers blood sugar by increasing the amount of insulin body releases
Thiazolidinedione Containing Medication
pioglitazone/Actos®, rosiglitazone/Avandia®, Duetac®, Avadamet®, Avandaryl®
Helps body to better respond to the insulin it produces
CHF Digoxin digoxin Helps lower hospital admission for heart failure Hydralazine Containing Medication
hydralazine Decreases the amount of water and salt in body and helps heart pump blood easier
MEDICATION ADHERENCE 92
Chronic Condition Drug Class Drug Main Use/Effects Selective Aldosterone Receptor Antagonist
spronolactone/Aldactone®, eplerenone/Inspra®
Decreases the amount of water and salt in body and helps heart pump blood easier
Osteoporosis Estrogen estradiol, estriopipate, Premarin®
Reduces risk for broken bones
Oral Bisphosphonate
Actonel®, alendronate, Boniva®
Reduce risk for broken bones
Parathyroid Hormone (PTH)
Forteo® Reduce risk for broken bones
Raloxifene raloxifene/Evista® Reduce risk for broken bones Multiple conditions CAD, CHF, Diabetes Angiotensin
Converting Engyme (ACE) Inhibitor Containing Medication
benezepril, enalapril, lisinopril and ramipril
Relaxes blood vessels and helps the heart work easier
Angiotensin II Receptor Antagonist Containing Medication
Benicar®, Diovan® Relaxes blood vessels and helps the heart work easier
CAD, CHF Beta-Blocker Containing Medication
atenolol, Bystolic®, carvedilol and metoprolol
Lower blood pressure by improving the ability of the heart to pump blood
Nitrate isosorbide dinitrate, isosorbide mononitrate
Prevents or relieves chest pain
CAD, Diabetes Statin Containing Medication
Lipitor®, Crestor®, Lescol®, Vytorin®, simvastatin/Zocor®, lovastatin, pravastatin
Lowers cholesterol by decreasing the ability of body to make cholesterol
MEDICATION ADHERENCE 93
Table 3 Attrition Table
Anti‐
depressant Beta
blocker
Calcium channel blocker
Diabetes Osteo‐porosis
RAS antagonist
Statin
Start 5,277 7,977 3,664 2,791 863 7,165 11,635
< 60 days pre‐index
0.60% 0.40% 0.60% 0.40% 0.20% 0.50% 0.40%
< 90 days post‐index
4.60% 4.70% 4.90% 4.10% 3.30% 3.90% 3.30%
Index before enrollment
0% 0% 0% 0% 0% 0% 0%
$0 Costs 0.50% 0.40% 0.30% 0.40% 0.60% 0.50% 0.50% Missing
HCC 0% 0% 0.10% 0% 0% 0% 0%
Missing Medigap plan type
0.20% 0.20% 0.20% 0.20% 0.10% 0.20% 0.20%
Missing Supply Side Measures
1.40% 1.20% 1.30% 1.50% 0.80% 1.60% 1.30%
Missing income
0% 0.10% 0% 0% 0% 0% 0%
Drop diseases
1.50% 0.90% 1.10% 0.70% 1.80% 0.60% 0.40%
Kept 4,823 7,359 3,357 2,587 805 6,644 10, 942
91.3% 92.3% 91.6% 92.7% 93.3% 92.7% 94.0%
MEDICATION ADHERENCE 94
Table 4 Antidepressants Socio-demographic Baseline Characteristics
Table 5 Beta Blockers Socio-demographic Baseline Characteristics
MEDICATION ADHERENCE 95
Table 5 Beta Blockers Socio-demographic Baseline Characteristics
Full treatment
fax only auth only Control
mean or % N mean or % N mean or % N mean or % N P-value
N 1417 2063 178 3701
demographics
Age
<64 yrs 1.6% 22 2.0% 42 1.1% 2 2.3% 85 0.2197
64 to 74 yrs 41.2% 584 37.7% 778 40.5% 72 39.8% 1474
75 to 84 yrs 34.5% 489 35.3% 729 38.8% 69 35.4% 1311
85 yrs and above 22.7% 322 24.9% 514 19.7% 35 22.5% 831
Gender
Female 59.3% 840 58.5% 1206 64.0% 114 59.9% 2217 0.4352
Race
High-Minority Area 6.4% 90 8.0% 164 6.7% 12 3.4% 126 0.0001
Med-Minority Area 62.0% 878 60.9% 1257 60.7% 108 46.6% 1725
Low-Minority Area 31.7% 449 31.1% 642 32.6% 58 50.0% 1850
Income
High Income 61.5% 872 62.0% 1279 69.7% 124 61.4% 2271 0.4859
Medium Income 28.7% 407 28.7% 593 23.0% 41 29.4% 1088
Low Income 9.7% 138 9.3% 191 7.3% 13 9.2% 342
State
CA 17.8% 252 15.6% 322 25.8% 46 18.2% 675 0.0001
FL 11.8% 167 9.7% 199 7.3% 13 10.3% 381
NC 14.5% 206 12.6% 260 14.6% 26 11.4% 421
NY 40.4% 573 48.5% 1000 37.1% 66 47.9% 1774
OH 15.5% 219 13.7% 282 15.2% 27 12.2% 450
Health Status
HCC Score pre period
HCC Score <=1.0 45.9% 650 43.9% 906 47.2% 84 47.1% 1743 0.1591
HCC Score 1.0-2.8 42.1% 597 44.4% 915 42.1% 75 40.3% 1493
HCC Score >2.8 12.0% 170 11.7% 242 10.7% 19 12.6% 465
HCC Score post period
HCC Score <=1.0 39.8% 564 37.6% 776 39.9% 71 41.7% 1542 0.0609
HCC Score 1.0-2.8 44.7% 634 47.7% 984 46.1% 82 43.2% 1598
HCC Score >2.8 15.5% 219 14.7% 303 14.0% 25 15.2% 561
Long Term Nursing Hom 9.0% 128 11.6% 240 10.1% 18 12.1% 448 0.0177
Long Term Nursing Hom 6.9% 98 9.2% 190 7.9% 14 10.7% 397 0.0004
ER Visits prior to Index d 26.5% 376 26.2% 541 24.7% 44 28.7% 1061 0.1345
ER Visits during post pe 21.5% 305 21.3% 440 24.2% 43 22.3% 825 0.7125
Hospital admission prio 17.9% 253 19.3% 398 17.4% 31 21.2% 784 0.0335
Hospital admission duri 10.8% 153 12.0% 247 12.4% 22 13.7% 508 0.0273
Beta Blocker
MEDICATION ADHERENCE 96
Table 6 Calcium Channel Blockers Socio-demographic Baseline Characteristics
Full treatment fax only auth only Control
mean or % N mean or % N mean or % N mean or % N P-value
N 694 1003 67 1593
demographics
Age
<64 yrs 1.0% 7 1.3% 13 0.0% 0 2.8% 45 0.0051
64 to 74 yrs 37.5% 260 39.3% 394 41.8% 28 40.9% 651
75 to 84 yrs 34.4% 239 31.5% 316 38.8% 26 33.1% 527
85 yrs and above 27.1% 188 27.9% 280 19.4% 13 23.2% 370
Gender
Female 65.3% 453 64.7% 649 49.3% 33 64.2% 1023 0.0729
Race
High-Minority Area 9.9% 69 12.1% 121 7.5% 5 4.6% 73 0.0001
Med-Minority Area 58.9% 409 60.3% 605 68.7% 46 45.6% 727
Low-Minority Area 31.1% 216 27.6% 277 23.9% 16 49.8% 793
Income
High Income 62.0% 430 60.3% 605 67.2% 45 57.6% 917 0.2753
Medium Income 28.5% 198 28.9% 290 23.9% 16 32.3% 514
Low Income 9.5% 66 10.8% 108 9.0% 6 10.2% 162
State
CA 20.9% 145 21.0% 211 31.3% 21 18.6% 296 0.0953
FL 13.3% 92 9.9% 99 10.5% 7 10.5% 167
NC 12.3% 85 11.7% 117 7.5% 5 12.1% 192
NY 39.5% 274 43.5% 436 34.3% 23 45.5% 725
OH 14.1% 98 14.0% 140 16.4% 11 13.4% 213
Health Status
HCC Score pre period
HCC Score <=1.0 46.7% 324 46.3% 464 40.3% 27 45.0% 717 0.8202
HCC Score 1.0-2.8 42.5% 295 42.0% 421 47.8% 32 42.3% 673
HCC Score >2.8 10.8% 75 11.8% 118 11.9% 8 12.7% 203
HCC Score post period
HCC Score <=1.0 40.4% 280 40.5% 406 32.8% 22 40.1% 639 0.7929
HCC Score 1.0-2.8 45.4% 315 45.9% 460 52.2% 35 44.6% 710
HCC Score >2.8 14.3% 99 13.7% 137 14.9% 10 15.3% 244
Calcium Channel Blocker
MEDICATION ADHERENCE 97
Table 7 Diabetes Socio-demographic Baseline Characteristics
MEDICATION ADHERENCE 98
Table 8 Osteoporosis Socio-demographic Baseline Characteristics
Full treatment fax only auth only Control
mean or % N mean or % N mean or % N mean or % N P-value
N 88 143 8 566
demographics
Age
<64 yrs 1.1% 1 0.7% 1 0.0% 0 1.2% 7 0.6722
64 to 74 yrs 45.5% 40 45.5% 65 12.5% 1 39.4% 223
75 to 84 yrs 34.1% 30 30.8% 44 62.5% 5 35.5% 201
85 yrs and above 19.3% 17 23.1% 33 25.0% 2 23.9% 135
Gender
Female 90.9% 80 95.1% 136 87.5% 7 94.0% 532 0.5199
Race
High-Minority Area 4.6% 4 7.7% 11 12.5% 1 2.7% 15 0.0001
Med-Minority Area 56.8% 50 65.0% 93 62.5% 5 45.8% 259
Low-Minority Area 38.6% 34 27.3% 39 25.0% 2 51.6% 292
Income
High Income 71.6% 63 65.0% 93 62.5% 5 60.1% 340 0.2180
Medium Income 22.7% 20 25.2% 36 12.5% 1 30.4% 172
Low Income 5.7% 5 9.8% 14 25.0% 2 9.5% 54
State
CA 20.5% 18 16.8% 24 37.5% 3 17.1% 97 0.6349
FL 14.8% 13 12.6% 18 0.0% 0 12.9% 73
NC 13.6% 12 15.4% 22 12.5% 1 10.8% 61
NY 31.8% 28 39.9% 57 25.0% 2 43.1% 244
OH 19.3% 17 15.4% 22 25.0% 2 16.1% 91
Health Status
HCC Score pre period
HCC Score <=1.0 63.6% 56 59.4% 85 25.0% 2 57.2% 324 0.2857
HCC Score 1.0-2.8 31.8% 28 31.5% 45 62.5% 5 36.6% 207
HCC Score >2.8 4.6% 4 9.1% 13 12.5% 1 6.2% 35
HCC Score post period
HCC Score <=1.0 54.6% 48 54.6% 78 25.0% 2 51.6% 292 0.261
HCC Score 1.0-2.8 39.8% 35 32.9% 47 62.5% 5 40.1% 227
HCC Score >2.8 5.7% 5 12.6% 18 12.5% 1 8.3% 47
Osteoporosis
MEDICATION ADHERENCE 99
Table 9 RAS Antagonist Socio-demographic Baseline Characteristics
Full treatment fax only auth only Control
mean or % N mean or % N mean or % N mean or % N P-value
N 1343 2131 154 3016
demographics
Age
<64 yrs 1.3% 17 1.4% 29 1.3% 2 1.8% 54 0.0357
64 to 74 yrs 47.7% 640 44.8% 955 42.9% 66 48.4% 1459
75 to 84 yrs 32.3% 434 33.1% 706 31.8% 49 32.8% 989
85 yrs and above 18.8% 252 20.7% 441 24.0% 37 17.0% 514
Gender
Female 62.3% 836 59.6% 1269 67.5% 104 61.2% 1846 0.1339
Race
High-Minority Area 9.2% 124 9.2% 195 8.4% 13 4.1% 122 0.0001
Med-Minority Area 60.8% 817 63.1% 1344 69.5% 107 46.9% 1415
Low-Minority Area 29.9% 402 27.8% 592 22.1% 34 49.0% 1479
Income
High Income 58.0% 779 62.5% 1331 69.5% 107 59.3% 1787 0.0205
Medium Income 30.8% 414 28.1% 598 22.1% 34 30.8% 928
Low Income 11.2% 150 9.5% 202 8.4% 13 10.0% 301
State
CA 19.3% 259 19.4% 414 40.3% 62 19.9% 601 0.0001
FL 13.4% 180 9.7% 206 7.8% 12 10.9% 329
NC 16.3% 219 11.1% 236 11.0% 17 12.1% 365
NY 36.9% 495 45.6% 972 31.2% 48 43.8% 1322
OH 14.2% 190 14.2% 303 9.7% 15 13.2% 399
HCC Score pre period
HCC Score <=1.0 55.3% 742 53.6% 1143 46.8% 72 53.5% 1614 0.6127
HCC Score 1.0-2.8 36.5% 490 37.7% 804 44.2% 68 38.0% 1145
HCC Score >2.8 8.3% 111 8.6% 184 9.1% 14 8.5% 257
HCC Score post period
HCC Score <=1.0 50.3% 675 47.9% 1021 40.3% 62 49.1% 1480 0.188
HCC Score 1.0-2.8 40.4% 543 40.9% 872 48.7% 75 40.2% 1213
HCC Score >2.8 9.3% 125 11.2% 238 11.0% 17 10.7% 323
RAS Antagonist
MEDICATION ADHERENCE 100
Table 10 Statins Socio-demographic Baseline Characteristics
Full treatment fax only auth only Control
mean or % N mean or % N mean or % N mean or % N P-value
N 2408 3474 259 4801
demographics
Age
<64 yrs 1.1% 26 1.2% 42 0.0% 0 1.8% 86 0.0001
64 to 74 yrs 49.1% 1183 47.4% 1647 51.7% 134 50.2% 2410
75 to 84 yrs 33.8% 814 32.3% 1122 28.6% 74 33.6% 1614
85 yrs and above 16.0% 385 19.1% 663 19.7% 51 14.4% 691
Gender 0
Female 64.9% 1563 61.0% 2118 57.5% 149 59.8% 2870 0.0002
Race
High-Minority Area 6.7% 162 8.3% 288 7.3% 19 3.5% 168 0.0001
Med-Minority Area 61.0% 1469 63.0% 2187 60.6% 157 46.9% 2253
Low-Minority Area 32.3% 777 28.8% 999 32.1% 83 49.6% 2380
Income
High Income 64.2% 1546 65.7% 2282 68.0% 176 62.5% 2998 0.0049
Medium Income 26.8% 646 24.9% 865 24.3% 63 28.9% 1388
Low Income 9.0% 216 9.4% 327 7.7% 20 8.6% 415
State
CA 15.2% 366 16.7% 581 25.1% 65 17.4% 837 0.0001
FL 12.5% 300 8.8% 306 7.7% 20 11.6% 557
NC 13.9% 335 11.5% 399 12.7% 33 11.7% 563
NY 43.0% 1036 51.6% 1794 37.8% 98 47.5% 2281
OH 15.4% 371 11.3% 394 16.6% 43 11.7% 563
Health Status
HCC Score pre period
HCC Score <=1.0 59.3% 1429 58.5% 2032 55.6% 144 58.1% 2788 0.3208
HCC Score 1.0-2.8 34.1% 821 35.2% 1222 35.1% 91 34.6% 1659
HCC Score >2.8 6.6% 158 6.3% 220 9.3% 24 7.4% 354
HCC Score post period
HCC Score <=1.0 54.2% 1305 52.4% 1821 51.0% 132 52.4% 2516 0.1787
HCC Score 1.0-2.8 37.4% 901 39.9% 1385 39.4% 102 38.5% 1846
HCC Score >2.8 8.4% 202 7.7% 268 9.7% 25 9.1% 439
Statin
MEDICATION ADHERENCE 101
Table 11 Analysis Among Antidepressants Users: Effectiveness of IVR on Adherence Compared to Control
Antidepressants Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P< Reminder call and Provider fax
2.11 0.783 0.007-
11.38817.961 0.526
-111.466
160.98 0.489
Provider fax only 2.269 0.711 0.001
-16.977
16.314 0.298 -58.543 146.22 0.689
Reminder call only 1.982 1.653 0.231 3.229 37.843 0.932
-344.581
339.17 0.310
Race high (60% non-white)
0.214 1.649 0.897 24.596 37.846 0.516 8.514 339.202 0.98
Race low(< 15% non-white)
-0.157 0.612 0.798 10.611 14.048 0.450 153.993 125.909 0.221
Income high (top 30% median income)
0.203 0.668 0.761 6.288 15.325 0.682 83.45 137.356 0.544
Income low(< 30% median income)
-1.662 1.193 0.163 2.632 27.35 0.923 144.476 245.128 0.556
P<.05
MEDICATION ADHERENCE 102
Table 12 Analysis Among Beta Blocker Users: Effectiveness of IVR on Adherence Compared to
Control
P<.05
Beta Blockers Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P< Reminder call and Provider fax
2.129 0.569 0.000 -9.391 17.78 0.597-
321.258 148.687 0.031
Provider fax only 1.652 0.5 0.001
-24.339
15.632 0.120-
298.477 130.727 0.022
Reminder call only 1.796 1.391 0.197 2.125 43.483 0.961
-120.535
363.632 0.740
Race high (60% non-white)
-1.032 0.996 0.300 44.292 31.143 0.155-
108.037 260.441 0.678
Race low(< 15% non-white)
0.266 0.443 0.549 3.304 13.843 0.811 162.238 115.768 0.161
Income high (top 30% median income)
-0.235 0.468 0.615-
11.79714.61 0.419
-168.497
122.175 0.168
Income low(< 30% median income)
1.845 0.814 0.023 -8.615 25.433 0.735-
724.227 212.685 0.001
MEDICATION ADHERENCE 103
Table 13 Analysis Among Calcium Channel Blocker Users: Effectiveness of IVR on Adherence Compared to Control
Calcium Channel Blockers Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P< Reminder call and Provider fax
3.480 0.915 0.000 -1.073 22.893 0.963 -198.56 239.144 0.406
Provider fax only 2.039 0.818 0.013
-14.111
20.479 0.491-
553.204 213.923 0.010
Reminder call only 3.032 2.508 0.227 36.192 62.836 0.565 -8.486 656.387 0.990 Race high (60% non-white)
-0.585 1.378 0.671 5.071 34.459 0.883-
289.622 359.962 0.421
Race low(< 15% non-white)
1.873 0.74 0.011 2.943 18.514 0.874-
129.762 193.403 0.502
Income high (top 30% median income)
0.252 0.76 0.740-
10.56719.002 0.578
-258.131
198.493 0.194
Income low(< 30% median income)
1.479 1.277 0.247 5.065 31.921 0.874-
149.021 333.448 0.655
P<.05
MEDICATION ADHERENCE 104
Table 14 Analysis Among Diabetes Users: Effectiveness of IVR on Adherence Compared to Control
Diabetes Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P< Reminder call and Provider fax
1.699 1.048 0.105-
24.29164.375 0.706 144.411 210.901 0.494
Provider fax only 0.403 0.891 0.651 57.405 54.72 0.294 -69.17 179.27 0.700 Reminder call only 3.766 2.732 0.168 9.064 167.927 0.957 238.227 550.154 0.665 Race high (60% non-white)
-0.804 1.557 0.606-
52.28395.692 0.585
-174.234
313.499 0.578
Race low(< 15% non-white)
0.308 0.815 0.706-
55.22650.059 0.270
-122.961
164 0.453
Income high (top 30% median income)
0.312 0.823 0.705-
54.39350.5 0.282 82.824 165.447 0.617
Income low(< 30% median income)
-2.092 1.369 0.127-
11.81184.111 0.888 -34.47 275.559 0.900
P<.05
MEDICATION ADHERENCE 105
Table 15 Analysis Among RAS Antagonist Users: Effectiveness of IVR on Adherence Compared to Control
RAS Antagonist Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P<
Reminder call and Provider fax
1.699 1.04
8 0.105
-24.29
1
64.375
0.706
144.411
210.901
0.494
Provider fax only 0.403
0.891
0.651 57.40
5 54.72
0.294
-69.17 179.2
7 0.700
Reminder call only 3.766
2.732
0.168 9.064167.9
27 0.957
238.227
550.154
0.665
Race high (60% non-white)
-0.804 1.55
7 0.606
-52.28
3
95.692
0.585
-174.2
34
313.499
0.578
Race low(< 15% non-white)
0.308 0.81
5 0.706
-55.22
6
50.059
0.270
-122.9
61 164 0.453
Income high (top 30% median income)
0.312 0.82
3 0.705
-54.39
3 50.5
0.282
82.824
165.447
0.617
Income low(< 30% median income)
-2.092 1.36
9 0.127
-11.81
1
84.111
0.888
-34.47 275.5
59 0.900
P<.05
MEDICATION ADHERENCE 106
Table 16 Analysis Among Osteoporosis Users: Effectiveness of IVR on Adherence Compared to Control
Osteoporosis Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P< Reminder call and Provider fax
2.110 0.783 0.007-
11.38817.961 0.526
-111.466
160.98 0.489
Provider fax only 2.269 0.711 0.001
-16.977
16.314 0.298 -58.543 146.22 0.689
Reminder call only 1.982 1.653 0.231 3.229 37.843 0.932
-344.581
339.17 0.310
Race high (60% non-white)
0.214 1.649 0.897 24.596 37.846 0.516 8.514 339.202 0.980
Race low(< 15% non-white)
-0.157 0.612 0.798 10.611 14.048 0.450 153.993 125.909 0.221
Income high (top 30% median income)
0.203 0.668 0.761 6.288 15.325 0.682 83.45 137.356 0.544
Income low(< 30% median income)
-1.662 1.193 0.163 2.632 27.35 0.923 144.476 245.128 0.556
P<.05
MEDICATION ADHERENCE 107
Table 17 Analysis Among Statins Users: Effectiveness of IVR on Adherence Compared to Control
Statins Differences
Variables PDC
Difference Drug Cost Difference
Total Cost Difference
Beta S.E.M P< Beta S.E.M P< Beta S.E.M P< Reminder call and Provider fax 0.652 0.432 0.131 -9.611 8.608 0.264
-241.848 90.526 0.008
Provider fax only -0.191 0.388 0.623 -0.018 7.719 0.998 -24.308 81.179 0.765 Reminder call only 2.364 1.103 0.032 -0.512 21.967 0.981 261.618 231.023 0.257 Race high (60% non-white) -0.342 0.748 0.648 -8.768 14.881 0.556 27.07 156.495 0.863 Race low(< 15% non-white) 0.166 0.350 0.635 5.964 6.963 0.392 59.364 73.227 0.418 Income high (top 30% median income) 0.044 0.373 0.906 0.841 7.43 0.910 -6.171 78.134 0.937 Income low(< 30% median income) -0.294 0.645 0.649 13.034 12.823 0.309 166.864 134.854 0.216
P<.05
MEDICATION ADHERENCE 108
Table 18 Analysis Among Antidepressants Users: Healthcare Utilization Outcomes
Table A Antidepressants-ER admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax
-0.181
0.095 0.055 0.834 0.693 1.004
Provider fax only
-0.122
0.086 0.154 0.885 0.748 1.047
Reminder call only
-0.169
0.211 0.424 0.845 0.559 1.277
Table B
Antidepressants-Inpatient admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax
-0.178
0.125 0.154 0.837 0.655 1.069
Provider fax only
-0.139
0.111 0.21 0.87 0.700 1.081
Reminder call only
-0.502
0.309 0.104 0.605 0.330 1.109
Table C
Antidepressants-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax
-0.413
0.14 0.003 0.662 0.503 0.871
Provider fax only
-0.255
0.121 0.035 0.775 0.612 0.982
Reminder call only
-0.989
0.430 0.022 0.372 0.160 0.864
P<.05
MEDICATION ADHERENCE 109
Table 19 Analysis Among Beta Blockers Users: Healthcare Utilization Outcomes
Table A
Beta Blockers-ER admissions B S. E. Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.068 0.078 0.382 0.934 0.802 1.088
Provider fax only -0.065 0.069 0.346 0.937 0.819 1.072
Reminder call only 0.098 0.182 0.591 1.103 0.772 1.575
Table B
Beta Blockers-Inpatient admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.240 0.101 0.018 0.787 0.645 0.960
Provider fax only -0.192 0.086 0.026 0.826 0.697 0.978
Reminder call only -0.048 0.236 0.838 0.953 0.600 1.514
Table C
Beta Blockers-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.514 0.122 0.000 0.598 0.471 0.760
Provider fax only -0.220 0.097 0.024 0.803 0.663 0.971
Reminder call only -0.323 0.292 0.268 0.724 0.408 1.282
P<.05
MEDICATION ADHERENCE 110
Table 20 Analysis Among Calcium Channel Blocker Users: Healthcare Utilization Outcomes
Table A Calcium Channel Blockers-ER
admissions B S. E. Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.146 0.111 0.186 0.864 0.695 1.073
Provider fax only -0.290 0.102 0.004 0.748 0.613 0.913
Reminder call only -0.257 0.318 0.419 0.773 0.414 1.443
Table B
Calcium Channel Blockers-Inpatient admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.197 0.142 0.166 0.821 0.621 1.085
Provider fax only -0.338 0.131 0.01 0.713 0.552 0.921
Reminder call only -0.164 0.392 0.676 0.849 0.394 1.829
Table C
Calcium Channel Blockers-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.265 0.162 0.101 0.767 0.559 1.053
Provider fax only -0.160 0.142 0.260 0.852 0.644 1.126
Reminder call only -0.482 0.532 0.365 0.617 0.218 1.753
P<.05
MEDICATION ADHERENCE 111
Table 21 Analysis Among Diabetes Users: Healthcare Utilization Outcomes
Table A Diabetes-ER admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.141 0.136 0.302 0.869 0.665 1.135
Provider fax only -0.033 0.114 0.771 0.967 0.773 1.21
Reminder call only -0.072 0.352 0.837 0.930 0.467 1.853
Table B
Diabetes-Inpatient admissions B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.361 0.180 0.045 0.697 0.49 0.991
Provider fax only -0.389 0.150 0.010 0.678 0.505 0.910
Reminder call only -0.876 0.605 0.148 0.417 0.127 1.365
Table C
Diabetes-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.719 0.251 0.004 0.487 0.298 0.797
Provider fax only 0.058 0.172 0.737 1.060 0.756 1.486
Reminder call only -0.489 0.621 0.431 0.613 0.182 2.069
P<.05
MEDICATION ADHERENCE 112
Table 22 Analysis Among RAS Antagonist Users: Healthcare Utilization Outcomes
Table A
RAS Antagonist-ER admissions
B S. E. Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.140 0.085 0.101 0.869 0.736 1.028
Provider fax only -0.155 0.074 0.037 0.857 0.74 0.991
Reminder call only -0.380 0.23 0.098 0.684 0.436 1.073
Table B
RAS Antagonist-Inpatient admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.198 0.113 0.082 0.821 0.657 1.025
Provider fax only -0.063 0.094 0.506 0.939 0.781 1.130
Reminder call only -0.036 0.267 0.894 0.965 0.571 1.629
Table C
RAS Antagonist-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.401 0.138 0.004 0.669 0.511 0.877
Provider fax only -0.099 0.109 0.363 0.905 0.731 1.122
Reminder call only -0.305 0.345 0.376 0.737 0.375 1.448
P<.05
MEDICATION ADHERENCE 113
Table 23 Analysis Among Osteoporosis Users: Healthcare Utilization Outcomes
Table A Osteoporosis-ER admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.267 0.317 0.401 0.766 0.411 1.427
Provider fax only -0.194 0.257 0.450 0.823 0.497 1.363
Reminder call only 0.041 0.858 0.961 1.042 0.194 5.605
Table B
Osteoporosis-Inpatient admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -1.122 0.550 0.042 0.326 0.111 0.958
Provider fax only 0.017 0.319 0.957 1.017 0.545 1.900
Reminder call only -0.206 1.129 0.855 0.814 0.089 7.437
Table C
Osteoporosis-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -1.496 0.765 0.051 0.224 0.05 1.004
Provider fax only 0.073 0.384 0.849 1.076 0.507 2.286
Reminder call only 0.130 1.127 0.908 1.139 0.125 10.381
P<.05
MEDICATION ADHERENCE 114
Table 24 Analysis Among Statins Users: Healthcare Utilization Outcomes
Table A Statins-ER admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.257 0.069 0.000 0.773 0.676 0.885
Provider fax only -0.176 0.061 0.004 0.839 0.745 0.945
Reminder call only -0.048 0.165 0.773 0.953 0.689 1.319
Table B
Statins-Inpatient admissions B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.139 0.094 0.138 0.870 0.723 1.046
Provider fax only -0.113 0.082 0.169 0.893 0.760 1.049
Reminder call only -0.189 0.242 0.435 0.828 0.516 1.330
Table C
Statins-Nursing home admissions
B S. E. Sig. Exp(B) 95% C.I.for EXP(B)
Lower Upper
Reminder call and Provider fax -0.621 0.124 0.000 0.538 0.421 0.686
Provider fax only -0.234 0.098 0.017 0.791 0.653 0.959
Reminder call only -0.557 0.321 0.083 0.573 0.305 1.076
P<.05
MEDICATION ADHERENCE 115
Table 25 Analysis Among Antidepressants Users: Effectiveness of MCP on Adherence Compared to Control
Antidepressants Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants 0.026 0.028 0.354
Race high (60% non-white) 0.051 0.056 0.364
Race low(< 15% non-white) -0.006 0.027 0.835
Income high (top 30% median income)
0.021 0.03 0.475
Income low(< 30% median income) -0.009 0.061 0.889
P<.05
MEDICATION ADHERENCE 116
Table 26 Analysis Among Beta Blocker Users: Effectiveness of MCP on Adherence Compared to Control
Beta blockers Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants 0.059 0.042 0.164
Race high (60% non-white) -0.102 0.084 0.231
Race low(< 15% non-white) 0.045 0.04 0.262
Income high (top 30% median income)
-0.028 0.045 0.545
Income low(< 30% median income) 0.057 0.071 0.422
P<.05
MEDICATION ADHERENCE 117
Table 27 Analysis Among Calcium Channel Blocker Users: Effectiveness of MCP on Adherence Compared to Control
Calcium Channel Blockers
Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants 0.032 0.075 0.669
Race high (60% non-white) -0.009 0.1 0.929
Race low(< 15% non-white) -0.03 0.067 0.655
Income high (top 30% median income)
-0.074 0.088 0.403
Income low(< 30% median income) -0.159 0.143 0.272
P<.05
MEDICATION ADHERENCE 118
Table 28 Analysis Among Diabetes Users: Effectiveness of MCP on Adherence Compared to Control
Diabetes Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants -0.083 0.07 0.243
Race high (60% non-white) 0.086 0.089 0.346
Race low(< 15% non-white) -0.029 0.07 0.683
Income high (top 30% median income)
0.036 0.066 0.592
Income low(< 30% median income) -0.096 0.092 0.305
P<.05
MEDICATION ADHERENCE 119
Table 29 Analysis Among Osteoporosis Users: Effectiveness of MCP on Adherence Compared to Control
Osteoporosis Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants 0.19 0.085 0.033
Race high (60% non-white) 0.031 0.134 0.816
Race low(< 15% non-white) 0.114 0.068 0.104
Income high (top 30% median income)
0.002 0.073 0.975
Income low(< 30% median income) 0.026 0.169 0.879
P<.05
MEDICATION ADHERENCE 120
Table 30 Analysis Among RAS Antagonists Users: Effectiveness of MCP on Adherence Compared to Control
RAS Antagonist Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants -0.012 0.047 0.795
Race high (60% non-white) -0.013 0.07 0.849
Race low(< 15% non-white) -0.027 0.048 0.574
Income high (top 30% median income)
0.002 0.057 0.977
Income low(< 30% median income) -0.001 0.084 0.994
P<.05
MEDICATION ADHERENCE 121
Table 31 Analysis Among Statins Users: Effectiveness of MCP on Adherence Compared to Control
Statins Differences Variables PDC Difference
Beta S.E.M P< MyCarePath Participants 0.041 0.04 0.313
Race high (60% non-white) 0.007 0.061 0.903
Race low(< 15% non-white) -0.083 0.038 0.035
Income high (top 30% median income)
-0.005 0.041 0.896
Income low(< 30% median income) -0.064 0.074 0.39
P<.05
MEDICATION ADHERENCE 122
Table 32 Analysis of Drug Costs of MCP on Adherence Compared to Control
Drug Cost Differences
Variables RX Cost
Difference
Beta S.E.M P< MyCarePath Participants 3.781 25.269 0.881
Race high (60% non-white) 53.284 40.537 0.19
Race low(< 15% non-white) 2.294 20.579 0.911
Income high (top 30% median income)
22.729 23.907 0.343
Income low(< 30% median income) -0.656 39.308 0.987
P<.05
MEDICATION ADHERENCE 123
Table 33 Analysis of Total Cost of MCP on Adherence Compared to Control
Total Cost Differences
Variables Total Cost Difference
Beta S.E.M P< MyCarePath Participants -323.439 480.473 0.501
Race high (60% non-white) 152.337 770.774 0.843
Race low(< 15% non-white) -432.34 391.283 0.27
Income high (top 30% median income)
763.887 454.558 0.094
Income low(< 30% median income) -14.508 747.402 0.985
P<.05
MEDICATION ADHERENCE 124
Table 34 Analysis of MCP on ER Visits Utilization
ER Visits
B S.E. Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
MyCarePath Participants
-.067 .388 .863 .935 .437 2.000
Race high (60% non-white)
.470 .612 .443 1.599 .482 5.302
Race low(< 15% non-white)
.368 .315 .243 1.445 .779 2.678
Income high (top 30% median income)
-.567 .340 .096 .567 .291 1.105
Income low(< 30% median income)
-1.002 .654 .125 .367 .102 1.322
P<.05
MEDICATION ADHERENCE 125
Table 35 Analysis of MCP on Inpatient Utilization Outcomes
Inpatient Admissions
B S.E. Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
MyCarePath Participants
-.843 .516 .102 .430 .157 1.183
Race high (60% non-white)
.583 .663 .379 1.792 .488 6.575
Race low(< 15% non-white)
.225 .346 .516 1.252 .635 2.467
Income high (top 30% median income)
.195 .416 .640 1.215 .538 2.745
Income low(< 30% median income)
-.119 .699 .865 .888 .225 3.496
P<.05
MEDICATION ADHERENCE 126
Table 36 Analysis of MCP on Nursing Home Utilization Outcomes
Inpatient Admissions B S.E. Sig. Exp(B)
95% C.I.for EXP(B)
Lower Upper
MyCarePath Participants
-.843 .516 .102 .430 .157 1.183
Race high (60% non-white)
.583 .663 .379 1.792 .488 6.575
Race low(< 15% non-white)
.225 .346 .516 1.252 .635 2.467
Income high (top 30% median income)
.195 .416 .640 1.215 .538 2.745
Income low(< 30% median income)
-.119 .699 .865 .888 .225 3.496
P<.05
MEDICATION ADHERENCE 127
Appendix
Appendix A-PDC Calculation
Step by Step calculation of Proportion of Days Covered (PDC) based upon industry standards for
calculating PDC.
Step 1: Determine the dates of the member’s PDC measurement period
Each member’s PDC measurement period starts on the date of the first fill of the target
medication class during the PDC review period and then extends to the last day of the PDC
review period.
Step 2: Determine the number of days in the member’s PDC measurement period
The number of days in each member’s PDC measurement period is determined by counting the
number of days from the first fill of the target medication class during the PDC review period
until the last day of the PDC review period. This calculation is represented as follows:
o Number of days in the PDC measurement period = Number of days between the first fill and last
day of the PDC review period + 1.
The number of days calculated in this step is the denominator in the PDC calculation.
Step 3: Count the number of days covered by the target medication class during the PDC
measurement period
Count the days during the PDC measurement period where the member was covered by at least
one medication within the target medication class based on the prescription fill date and the days
of supply.
MEDICATION ADHERENCE 128
If prescription fills for the same medication overlap, adjust the prescription start date to be the
day after the previous fill has ended. The same medication is defined as a medication with the
same generic name (same GENERIC_NAME in Medispan).
The number of days calculated in this step is the numerator in the PDC calculation.
Step 4: Calculate the PDC for the target medication class
Divide the member’s number of covered days for the target medication class from Step 3 by the
member’s number of days in the PDC measurement period for the target medication class from
Step 2. This is the member’s PDC for the target medication class.
MEDICATION ADHERENCE 129
Appendix B-Call Script
Text
Hello, this is [[Custom1]] calling with important health information for
[[NameFirst]] [[NameLast]]. If you would like to take this call in English, please
stay on the line. Para escuchar este mensaje en español, por favor marque el
8.
Spanish:
Hola, habla [[Custom5]] y estamos llamando para hablar con [[NameFirst]]
[[NameLast]] con importante información sobre la salud.
English:
Please say "Yes" or "No". Is this [[NameFirst]]?
Spanish:
Por favor diga "Sí" o "No". ¿Habla [[NameFirst]]?
English:
Great. To protect your privacy and to be sure we're speaking to the correct
person, we need to confirm your date of birth and zip code. . After the chime,
please say your full date of birth. For example, if you were born on March 5th,
1963, please say March 5th, 1963.”
Spanish:
Genial. Para proteger su privacidad y para asegurarnos que estamos hablando
con la persona correcta, necesitamos confirmar su fecha de nacimiento y su
código postal. Después del tono, diga la fecha completa de su nacimiento. Por
MEDICATION ADHERENCE 130
Text
ejemplo, si usted nació el 5 de marzo de 1963, diga 5 de marzo, 1963.
English:
I’m sorry, but the date of birth you stated did not match our records. After the
chime, please say your full date of birth.
Spanish:
Lo lamento, pero la fecha de nacimiento que indicó no coincide con nuestros
registros. Después del tono, diga la fecha completa de su nacimiento.
English:
I’m sorry, but the information you entered does not match our records. Please
contact the member services department by calling the customer services
number on the back of your insurance card. Thank you. Goodbye.
Spanish:
Lo lamento, pero la información que ingresó no coincide con nuestros
registros. Por favor contáctese con el departamento de servicios para los
miembros llamando al número de servicio al cliente que aparece en la parte
posterior de su tarjeta de seguro. Gracias. Adiós.
English:
Thank you. Now, using your telephone keypad, after the chime, please enter
the 5 digit zip code of your home address.
Spanish:
Gracias. Ahora, usando su teclado numérico, después del tono, marque el
código postal de 5 dígitos que corresponde a la dirección de su casa.
English:
I’m sorry, but the zip code you entered did not match our records. After the
chime, please enter the 5 digit zip code of your home address.
MEDICATION ADHERENCE 131
Text
Spanish:
Lo lamento, pero el código postal que ingresó no coincide con nuestros
registros. Después del tono, marque el código postal de 5 dígitos que
corresponde a la dirección de su casa.
DRUG Decision Component
If CUSTOM6 = “1” B1a,
If CUSTOM6 = “2” B1b,
Otherwise B1c
English:
Thanks. What’s small, keeps your health on the right track, and will go
wherever you take it? Your medicine! According to our records at [[Custom1]]
you may have not refilled your [[Custom2]] prescription.
To make sure our records are correct, have you refilled your medicine? Please
say yes or no To hear this message again, please say REPEAT
Spanish:
Gracias. ¿Qué es pequeño, mantiene a su salud en el camino correcto, y va a
dondequiera que lo lleve? ¡Su medicina! De acuerdo con nuestros registros en
[[Custom5]] puede que no haya reabastecido su receta de [[Custom2]].
Para asegurarnos que nuestros registros sean los correctos, ¿ha reabastecido
su medicamento? Por favor diga sí o no. Para volver a escuchar este mensaje,
diga REPETIR.
MEDICATION ADHERENCE 132
Text
English:
Thanks. What’s small, keeps your health on the right track, and will go
wherever you take it? Your medicine! According to our records at [[Custom1]]
you may have not refilled your [[Custom2]] and [[Custom3]] prescriptions.
To make sure our records are correct, have you refilled your medicines? Please
say yes or no, To hear this message again, please say REPEAT
Spanish:
Gracias. ¿Qué es pequeño, mantiene a su salud en el camino correcto, y va a
dondequiera que lo lleve? ¡Su medicina! De acuerdo con nuestros registros en
[[Custom5]] puede que no haya reabastecido sus recetas de [[Custom2]] y de
[[Custom3]].
Para asegurarnos que nuestros registros sean los correctos, ¿ha reabastecido
sus medicamentos? Por favor diga sí o no. Para volver a escuchar este
mensaje, diga REPETIR.
English:
Thanks. What’s small, keeps your health on the right track, and will go
wherever you take it? Your medicine! According to our records at [[Custom1]]
you may have not refilled your [[Custom2]] [[Custom3]], and [[Custom4]]
prescriptions.
To make sure our records are correct, have you refilled your medicines? Please
say yes or no, To hear this message again, please say REPEAT
Spanish:
Gracias. ¿Qué es pequeño, mantiene a su salud en el camino correcto, y va a
MEDICATION ADHERENCE 133
Text
dondequiera que lo lleve? ¡Su medicina! De acuerdo con nuestros registros en
[[Custom5]] puede que no haya reabastecido sus recetas de [[Custom2]], de
[[Custom3]] y de [[Custom4]].
Para asegurarnos que nuestros registros sean los correctos, ¿ha reabastecido
sus medicamentos? Por favor diga sí o no. Para volver a escuchar este
mensaje, diga REPETIR.
English:
Thanks. If you’re having trouble taking your medicine the way your doctor
explained, you’re not alone. Some people have a hard time remembering to
take their medicines or to get them refilled. Others don’t like the side effects
or simply don’t think their medicines are helping them. We encourage you to
talk to your doctor or call the Nurse Health Line if you have any questions or
concerns regarding your medicine .
The Nurse Health Line is available twenty‐four hours a day, seven days a week
at no cost to you. A registered nurse will take your call and address your
questions or concerns. If you’d like, I can transfer you to a nurse right now.
Would you like to be transferred to a nurse? Please say ‘yes’ or ‘no’, To hear
this message again, please say REPEAT.
Spanish:
Gracias. Si está tendiendo problemas para tomar su medicamento de la
manera en que se lo explicó su doctor, usted no está solo. A algunas personas
les cuesta recordar el tomar sus medicamentos o reabastecerlos. A otros no
MEDICATION ADHERENCE 134
Text
les gustan sus efectos secundarios, o simplemente no creen que sus
medicamentos los están ayudando. Lo instamos a que hable con su doctor o a
que llame a la Línea de Salud de la Enfermera si tiene cualquier pregunta o
preocupación respecto a su medicamento.
La Línea de Salud de la Enfermera está disponible las 24 horas, los 7 días de la
semana sin costo para usted. Una enfermera matriculada atenderá su llamada
y se encargará de responder sus preguntas o preocupaciones. Si quisiera, lo
puedo transferir con una Enfermera ahora mismo.
¿Quisiera que lo transfiera con una enfermera? Por favor diga ‘sí’ o ‘no’. Para
volver a escuchar este mensaje, diga REPETIR.
English:
Great. We’re glad you’re taking charge of your health by taking your medicine
as prescribed.
If you have questions or concerns about your medicine, we encourage you to
call the Nurse Health Line that’s available twenty‐four hours a day, seven days
a week at no cost to you. A registered nurse will take your call and address
your questions. If you’d like, I can transfer you to a nurse right now.
Would you like to be transferred to a nurse? Please say ‘yes’ or ‘no’, To hear
this message again, please say REPEAT.
MEDICATION ADHERENCE 135
Spanish:
Genial. Nos complace que esté tomando las riendas de su salud al tomar sus
medicamentos como fueron recetados.
Si tiene preguntas o preocupaciones acerca de su medicamento, lo alentamos
a que llame a la Línea de Salud de la Enfermera, que está disponible las 24
horas, los 7 días de la semana sin costo para usted. Una enfermera
matriculada atenderá su llamada y se encargará de responder sus preguntas o
preocupaciones. Si quisiera, lo puedo transferir con una Enfermera ahora
mismo.
¿Quisiera que lo transfiera con una enfermera? Por favor diga ‘sí’ o ‘no’. Para
volver a escuchar este mensaje, diga REPETIR.
English:
OK. If you would like to speak to a nurse from the Nurse Health Line at
another time, please call us at 1‐888‐543‐5630. Again that number is 1‐888‐
543‐5630.
This call has been provided to you to help you get the most benefit from your
medicines. On scale of 1 to 5, where 1 is not at all helpful and 5 is very helpful;
after the chime, please tell me how helpful you found this call.
Spanish:
Bien. Si quisiera hablar con una enfermera de la Línea de Salud de la
Enfermera en otro momento, llámenos al 1‐888‐543‐5630. De nuevo, ese
número es el 1‐888‐543‐5630.
Se le ha brindado esta llamada para ayudarle a obtener el máximo beneficio
MEDICATION ADHERENCE 136
de sus medicamentos. En una escala del 1 al 5, donde el 1 es “para nada útil” y
el 5 es “muy útil”; después del tono, dígame cuán útil encontró esta llamada.
English:
Great! Hold on the line for just a moment as I connect you with a health nurse.
Spanish:
¡Genial! Aguarde en línea por un momento mientras lo conecto con una
enfermera.
English:
Thank you for your feedback. Again, if you would like to speak to a nurse from
the Nurse Health Line at another time, please call us at 1‐888‐543‐5630. Again
that number is 1‐888‐543‐5630. Thank you for your time today. Goodbye.
Spanish:
Gracias por su devolución. De nuevo, si quisiera hablar con una enfermera de
la Línea de Salud de la Enfermera en otro momento, llámenos al 1‐888‐543‐
5630. De nuevo, ese número es el 1‐888‐543‐5630. Gracias por su tiempo el
día de hoy. Adiós.
English:
Hello, this is [[Custom1]] calling with important information for [[NameFirst]]
[[NameLast]]. Please call us back toll free at ONE [[InboundNumber]] and enter
the 7 digit security code [[ReturnPIN]] so we know it’s you. Again this is
[[Custom1]], please call us back toll free at ONE [[InboundNumber]] and enter
the 7 digit security code [[ReturnPIN]]. Thank you.
MEDICATION ADHERENCE 137
English:
Thank you for taking a message. This [[Custom1]] calling with a reminder call
for [[NameFirst]] [[NameLast]]. Please ask [[NameFirst]] to call us back toll free
at ONE [[InboundNumber]] and enter the 7 digit security code [[ReturnPIN]].
Again this is [[Custom1]], please call us back toll free at ONE
[[InboundNumber]] and enter the 7 digit security code [[ReturnPIN]]. Thank
you.
Spanish:
Gracias por tomar un mensaje. Habla [[Custom5]] y estamos llamando con una
llamada recordatoria para [[NameFirst]] [[NameLast]]. Pídale a [[NameFirst]]
que nos vuelva a llamar gratuitamente al UNO [[InboundNumber]] y que
marque el código de seguridad de 7 dígitos [[ReturnPIN]]. De nuevo, habla
[[Custom1]], vuélvanos a llamar gratuitamente al UNO [[InboundNumber]] y
marque el código de seguridad de 7 dígitos [[ReturnPIN]]. Gracias.
Text
Hello, thanks for returning our call. I’m sorry but I don’t recognize the number
you are calling from. Using your keypad, after the chime, please enter the 7‐
digit security code we left in our recent phone message.
Thank you for calling us back. We recognize the number you’re calling from so
we don’t need your 7‐digit security code.
You’ve reached [[Custom1]]. To continue in English, please stay on the line.
Para escuchar este mensaje en español, por favor marque el 8.
MEDICATION ADHERENCE 138
Appendix C- Member Satisfaction Survey
Hello, this is [[Custom1]] calling [[NameFirst]] [[NameLast]] with important
health information. If you would like to take this call in English, please stay
on the line. Para escuchar este mensaje en español, por favor marque el 8.
Spanish:
SP TRANSLATION NEEDED: Hello, this is [[Custom1]] calling [[NameFirst]]
[[NameLast]] with important health information.
English:
Please say "Yes" or "No". Is this [[NameFirst]]?
Spanish:
English:
Great. To protect your privacy and to be sure we're speaking to the correct
person, we need to confirm your date of birth and zip code.
English:
Thank you. We’re calling on behalf of your AARP Medicare Supplement
Plan, insured by [[Custom1]].
We recently called you with a reminder to refill your medicines. During that
call, you were transferred to a nurse to help with any questions or concerns
you had. We would like for you to tell us how helpful it was to have a
conversation with a nurse about your medicines. On scale of 1 to 5, where
MEDICATION ADHERENCE 139
1 is not at all helpful and 5 is very helpful; please tell us how helpful you
found this call.
Spanish:
English:
Thank you. We recently called you with a reminder to refill your medicines.
We would like to know how helpful you found the refill reminder call. We
provided the call to help improve your adherence to your medicines. On
scale of 1 to 5, where 1 is not at all helpful and 5 is very helpful; please tell
us how helpful you found that call.
Spanish:
English:
Thanks. Staying adherent with your medicines is important and will help
you continue to stay healthy.
If you would like to speak to a nurse, we encourage you to call the Nurse
Health Line that’s available 24/7 at no cost to you. A registered nurse will
take your call and address any of your questions or concerns. Thank you for
your time today. Goodbye.
MEDICATION ADHERENCE 140
Appendix D- Report and Physician Letter
Appendix D-Pharma Report_Letter_Repor