running head: medication adherencebeatleyweb.simmons.edu/scholar/files/original/c...running head:...

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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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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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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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


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


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


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


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


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


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


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


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


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:


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


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,


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


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)


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,


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)


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).


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


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


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


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


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


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


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


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,


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


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.


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


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


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


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.


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


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


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


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


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.


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.


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.


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


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


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


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),


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.


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).


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)


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)


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


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


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


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


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


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


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


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.


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


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


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


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.


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Figures

Figure 1

02000400060008000100001200014000160001800020000

Full Intervention Fax only Authenticatedonly

Control

Members

Interventions

Sample Size by Treatment Groups


Figure 2

02000400060008000

1000012000

Members

Drug Classes

Sample Size by Drug Class by Treatment Group

Control

Authenticated only

Fax only

Full Treatment


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


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


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


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


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


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


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


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


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


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


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


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


Tables and Graphics

Table 1 Intervention Studies That Use IVR-Randomized and Quasi-Experimental


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


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


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%


Table 4 Antidepressants Socio-demographic Baseline Characteristics

Table 5 Beta Blockers Socio-demographic Baseline Characteristics


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


Table 6 Calcium Channel Blockers Socio-demographic Baseline Characteristics

Full treatment fax only auth only Control


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




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 <=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 <=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


Table 7 Diabetes Socio-demographic Baseline Characteristics


Table 8 Osteoporosis Socio-demographic Baseline Characteristics



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




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 <=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 <=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


Table 9 RAS Antagonist Socio-demographic Baseline Characteristics



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




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 <=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 <=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


Table 10 Statins Socio-demographic Baseline Characteristics



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




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 <=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 <=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


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


Table 12 Analysis Among Beta Blocker Users: Effectiveness of IVR on Adherence Compared to

Control

P<.05

Beta Blockers Differences

Variables PDC




2.129 0.569 0.000 -9.391 17.78 0.597-

321.258 148.687 0.031


-24.339

15.632 0.120-

298.477 130.727 0.022


-120.535

363.632 0.740


-1.032 0.996 0.300 44.292 31.143 0.155-

108.037 260.441 0.678


0.266 0.443 0.549 3.304 13.843 0.811 162.238 115.768 0.161


-0.235 0.468 0.615-

11.79714.61 0.419

-168.497

122.175 0.168


1.845 0.814 0.023 -8.615 25.433 0.735-

724.227 212.685 0.001


Table 13 Analysis Among Calcium Channel Blocker Users: Effectiveness of IVR on Adherence Compared to Control

Calcium Channel Blockers Differences

Variables PDC




3.480 0.915 0.000 -1.073 22.893 0.963 -198.56 239.144 0.406


-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


1.873 0.74 0.011 2.943 18.514 0.874-

129.762 193.403 0.502


0.252 0.76 0.740-

10.56719.002 0.578

-258.131

198.493 0.194


1.479 1.277 0.247 5.065 31.921 0.874-

149.021 333.448 0.655

P<.05


Table 14 Analysis Among Diabetes Users: Effectiveness of IVR on Adherence Compared to Control

Diabetes Differences

Variables PDC




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


0.308 0.815 0.706-

55.22650.059 0.270

-122.961

164 0.453


0.312 0.823 0.705-

54.39350.5 0.282 82.824 165.447 0.617


-2.092 1.369 0.127-

11.81184.111 0.888 -34.47 275.559 0.900

P<.05


Table 15 Analysis Among RAS Antagonist Users: Effectiveness of IVR on Adherence Compared to Control

RAS Antagonist Differences

Variables PDC



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


-0.804 1.55

7 0.606

-52.28

3

95.692

0.585

-174.2

34

313.499

0.578


0.308 0.81

5 0.706

-55.22

6

50.059

0.270

-122.9

61 164 0.453


0.312 0.82

3 0.705

-54.39

3 50.5

0.282

82.824

165.447

0.617


-2.092 1.36

9 0.127

-11.81

1

84.111

0.888

-34.47 275.5

59 0.900

P<.05


Table 16 Analysis Among Osteoporosis Users: Effectiveness of IVR on Adherence Compared to Control

Osteoporosis Differences

Variables PDC




2.110 0.783 0.007-

11.38817.961 0.526

-111.466

160.98 0.489


-16.977

16.314 0.298 -58.543 146.22 0.689


-344.581

339.17 0.310


0.214 1.649 0.897 24.596 37.846 0.516 8.514 339.202 0.980


-0.157 0.612 0.798 10.611 14.048 0.450 153.993 125.909 0.221


0.203 0.668 0.761 6.288 15.325 0.682 83.45 137.356 0.544


-1.662 1.193 0.163 2.632 27.35 0.923 144.476 245.128 0.556

P<.05


Table 17 Analysis Among Statins Users: Effectiveness of IVR on Adherence Compared to Control

Statins Differences

Variables PDC



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


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


-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


Lower Upper


-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


Lower Upper


-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


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


Table B

Beta Blockers-Inpatient admissions


Lower Upper



Reminder call only -0.048 0.236 0.838 0.953 0.600 1.514

Table C

Beta Blockers-Nursing home admissions


Lower Upper




P<.05


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




Table B

Calcium Channel Blockers-Inpatient admissions


Lower Upper




Table C

Calcium Channel Blockers-Nursing home admissions


Lower Upper




P<.05


Table 21 Analysis Among Diabetes Users: Healthcare Utilization Outcomes

Table A Diabetes-ER admissions


Lower Upper




Table B

Diabetes-Inpatient admissions B S. E. Sig. Exp(B) 95% C.I.for EXP(B)

Lower Upper




Table C

Diabetes-Nursing home admissions


Lower Upper


Provider fax only 0.058 0.172 0.737 1.060 0.756 1.486


P<.05


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




Table B

RAS Antagonist-Inpatient admissions


Lower Upper




Table C

RAS Antagonist-Nursing home admissions


Lower Upper




P<.05


Table 23 Analysis Among Osteoporosis Users: Healthcare Utilization Outcomes

Table A Osteoporosis-ER admissions


Lower Upper




Table B

Osteoporosis-Inpatient admissions


Lower Upper




Table C

Osteoporosis-Nursing home admissions


Lower Upper




P<.05


Table 24 Analysis Among Statins Users: Healthcare Utilization Outcomes

Table A Statins-ER admissions


Lower Upper




Table B

Statins-Inpatient admissions B S. E. Sig. Exp(B) 95% C.I.for EXP(B)

Lower Upper




Table C

Statins-Nursing home admissions


Lower Upper




P<.05


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


0.021 0.03 0.475

Income low(< 30% median income) -0.009 0.061 0.889

P<.05


Table 26 Analysis Among Beta Blocker Users: Effectiveness of MCP on Adherence Compared to Control

Beta blockers Differences Variables PDC Difference


Race high (60% non-white) -0.102 0.084 0.231

Race low(< 15% non-white) 0.045 0.04 0.262


-0.028 0.045 0.545

Income low(< 30% median income) 0.057 0.071 0.422

P<.05


Table 27 Analysis Among Calcium Channel Blocker Users: Effectiveness of MCP on Adherence Compared to Control

Calcium Channel Blockers

Differences Variables PDC Difference





-0.074 0.088 0.403


P<.05


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




0.036 0.066 0.592


P<.05


Table 29 Analysis Among Osteoporosis Users: Effectiveness of MCP on Adherence Compared to Control

Osteoporosis Differences Variables PDC Difference





0.002 0.073 0.975

Income low(< 30% median income) 0.026 0.169 0.879

P<.05


Table 30 Analysis Among RAS Antagonists Users: Effectiveness of MCP on Adherence Compared to Control

RAS Antagonist Differences Variables PDC Difference





0.002 0.057 0.977


P<.05


Table 31 Analysis Among Statins Users: Effectiveness of MCP on Adherence Compared to Control

Statins Differences Variables PDC Difference





-0.005 0.041 0.896


P<.05


Table 32 Analysis of Drug Costs of MCP on Adherence Compared to Control

Drug Cost Differences

Variables RX Cost

Difference





22.729 23.907 0.343


P<.05


Table 33 Analysis of Total Cost of MCP on Adherence Compared to Control

Total Cost Differences

Variables Total Cost Difference





763.887 454.558 0.094


P<.05


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


.470 .612 .443 1.599 .482 5.302


.368 .315 .243 1.445 .779 2.678


-.567 .340 .096 .567 .291 1.105


-1.002 .654 .125 .367 .102 1.322

P<.05


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


-.843 .516 .102 .430 .157 1.183


.583 .663 .379 1.792 .488 6.575


.225 .346 .516 1.252 .635 2.467


.195 .416 .640 1.215 .538 2.745


-.119 .699 .865 .888 .225 3.496

P<.05


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


-.843 .516 .102 .430 .157 1.183


.583 .663 .379 1.792 .488 6.575


.225 .346 .516 1.252 .635 2.467


.195 .416 .640 1.215 .538 2.745


-.119 .699 .865 .888 .225 3.496

P<.05


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.


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.


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


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.


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.


Text

English:



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:



[[Custom5]] puede que no haya reabastecido sus recetas de [[Custom2]] y de

[[Custom3]].


sus medicamentos? Por favor diga sí o no. Para volver a escuchar este

mensaje, diga REPETIR.

English:



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:



Text


[[Custom5]] puede que no haya reabastecido sus recetas de [[Custom2]], de

[[Custom3]] y de [[Custom4]].


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


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.


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


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.


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.


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


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.


Appendix D- Report and Physician Letter

Appendix D-Pharma Report_Letter_Repor

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