More patients today have health insurance, but they also are being asked to dig deeper into their pockets to cover a larger portion—if not all&mdash;of the costs of their healthcare treatments. This situation is made even more difficult by the prevalence of high-deductible health plans (HDHPs), which offer low monthly costs in exchange for higher costs for doctor visits, medicines, or other necessary care.

Recognizing that HDHPs are forcing patients to make trade-offs in the management of their health, insurers, employers, and other healthcare stakeholders have been examining different ways to ensure that patients have access to the right treatments at the right time.  

In response to the Affordable Care Act, more insurers are offering HDHPs. With these plans, premiums are generally lower than in traditional plans because enrollees must meet the higher deductibles before traditional medical and pharmacy coverage begins.

Most HDHPs are offered in conjunction with a health savings account (HSA) or, less often, a health reimbursement account (HRA); both can be accessed by plan enrollees to offset the deductible expense. The intention is that these kinds of plans have the potential to contain healthcare costs and engage enrollees more fully in managing their health and healthcare decisions or, in other words, “have more skin in the game.”

The use of these plans is continuing to grow, with more than 70% of surveyed employers offering HDHPs.

 In 2016, 39% of individuals younger than 65 years with private insurance were enrolled in an HDHP—up 3% from 2015&mdash;and 15% of those were enrolled in an HDHP with an HSA.

The average aggregate deductibles for workers with family coverage are $4321 for HDHP/HRAs and $4364 for HSA-qualified HDHPs, yet there can be wide variation.3 HSA-qualified HDHPs are legally required to have a maximum annual out-of-pocket (OOP) liability of no more than $6550 for single coverage and $13,100 for family coverage in 2016. For a low-income family of 4 making $48,678 per year, meeting a high deductible that is a sizable portion of that income is a daunting task.

Under most healthcare plans, patients must satisfy their deductible before their coverage kicks in, making care more expensive for those with lower incomes or greater healthcare needs. As a result, lower-income families are getting less preventive care and putting off care they need. They had 4 times the rate of avoidable hospitalizations compared with higher-income workers and had 3 times the rate of emergency department visits, thus incurring higher costs for themselves and for the healthcare system.

 To address this challenge, employers and health plans have started to test other methods for increasing patient access to necessary medicines and reducing some of the negative impacts of HDHPs, particularly for lower-income workers. Among the best practices in use are: lowering the net deductibles, minus the company HSA contribution; varying contributions to the HSA based on income or linking to incentives for wellness or consumer behaviors; implementing a broad list of preventive drugs, combined with a value-based approach in HSAs; and implementing a value-based approach to prescription drug coverage in HRA-based plans.

Cost Sharing: Impact on Healthcare Outcomes and Potential Approaches

Cost sharing's impact on healthcare decisions was clearly demonstrated by the RAND Health Insurance Experiment and subsequent studies.

 Placing more of the economic burden for healthcare treatments on patients showed that cost sharing reduced unnecessary or excess care; however, it also reduced appropriate or needed care, as evidenced by the lower-income workers’ greater use of emergency care. The question, then, is how can we steer patients toward the treatments that they truly need while reducing unnecessary use?

Although it does not entirely mitigate the sting of the high deductible, some employers and insurers have lowered cost sharing for certain treatments or covered preventive drugs before the deductible, particularly for chronic conditions like hypertension, diabetes, high cholesterol, and asthma.

Aetna, for example, tested the elimination of co-payments for drugs prescribed after myocardial infarction. They found that enhanced prescription coverage improved medication adherence and rates of first major vascular events and decreased patient spending without increasing overall health costs.

Similarly, Pitney Bowes adopted a value-based benefit-design approach under which drug costs for the treatment of hypertension, diabetes, and asthma were greatly reduced. Pitney Bowes recognized that increased medication adherence can reduce employer healthcare costs and keep employees healthy.

Value-based insurance design (VBID) principles also are being adopted or tested. Tricare, the health plan administered by the Department of Defense, began a test program to lower co-payments for certain conditions, and Premera Blue Cross in the Pacific Northwest implemented a pilot program for diabetes, hyperlipidemia, and hypertension.

Like Pitney Bowes, most of these policies have been implemented for a class of drugs (eg, lowering patient OOP costs for all patients with diabetes or removing the OOP costs for eye exams) rather than for more clinically nuanced cost sharing. Under a clinically nuanced approach, a program could reduce cost sharing for patients with a positive genetic test requiring a higher-tier/higher-cost treatment, or reduce costs for patients who have tried step therapy approaches and require higher-tier/higher-cost treatments.

Under current regulations, however, this would not entirely eliminate the requirement for patients to meet their deductibles first. Several states, and some health plans, are capping the OOP costs for co-pays or coinsurance (eg, at $200), which addresses some of the big jumps in patient costs. Yet, the difference for patients who face a $10 or $50 OOP expense versus a $200 OOP expense is still significant. 

Understanding whether—or how well&mdash;these approaches are working requires an analysis of the data. Some insurers and employers have more sophisticated and interoperable information technology systems and are well equipped to track co-pays and clinical nuance, and are incorporating this information in their plan designs and performance measurement.

Other organizations have not yet made the necessary changes to their infrastructure. It will be very important to assess whether changes to co-pays and benefits designs are helping or hurting patients so that we can learn from these programs. Data is also important for consumers: providing them with easy-to-understand resources about their benefits and costs can help them to make better healthcare decisions.

It is important to reduce barriers that can hamper patient access to care, especially when we know that medication adherence and access to higher-value care can make a big difference in reducing costs for both patients and our healthcare system. Armed with evidence, perhaps we can work toward ways to make HDHPs and the OOP costs less harsh. 

: National Pharmaceutical Council, Washington, DC.

: Dr Dubois is an employee of the National Pharmaceutical Council, an industry-funded health policy research group that is not involved in lobbying or advocacy.

: Concept and design (RWD); acquisition of data (RWD); analysis and interpretation of data (RWD); drafting of the manuscript (RWD); critical revision of the manuscript for important intellectual content (RWD); statistical analysis (RWD).

: Robert W. Dubois, MD, PhD, Chief Science Officer, National Pharmaceutical Council, 1717 Pennsylvania Ave, NW, Washington, DC 20006. 

1. Consumer-directed health plans: pharmacy benefits & “better practices”. National Pharmaceutical Council website. http://www.npcnow.org/publication/consumer-directed-health-plans-pharmacy-benefits-better-practices. Published September 30, 2014. Accessed February 14, 2017.

2. Martinez ME, Zammitti EP, Cohen RA; National Center for Health Statistics. Health insurance coverage: early release of estimates from the National Health Interview Survey, January-September 2016. CDC website. 

https://www.cdc.gov/nchs/data/nhis/earlyrelease/insur201702.pdf

. Published February 2017. Accessed February 16, 2017.

3. 2016 Employer Health Benefits Survey. Kaiser Family Foundation website. http://kff.org/health-costs/report/2016-employer-health-benefits-survey/. Published September 14, 2016. Accessed February 14, 2017. 

4. Income, poverty and health insurance coverage in the United States: 2015. US Census Bureau website. https://www.census.gov/newsroom/press-releases/2016/cb16-158.html. Published September 13, 2016. Accessed February 16, 2017. 

5. How high-deductible health plans hurt consumers. Families USA website. http://familiesusa.org/blog/2017/02/how-high-deductible-health-plans-hurt-consumers. Published February 14, 2017. Accessed February 16, 2017.

6. Aron-Dine A, Einav L, Finkelstein A. The RAND Health Insurance Experiment, three decades later. J Econ Perspect. 2013;27(1):197-222. doi: 10.1257/jep.27.1.197.

7. Choudhry NK, Avorn J, Glynn RJ, et al; Post-Myocardial Infarction Free Rx Event and Economic Evaluation (MI FREEE) Trial. Full coverage for preventive medications after myocardial infarction. 

 2011;365(22):2088-2097. doi: 10.1056/NEJMsa1107913. 

8. Mahoney J. Pitney Bowes: long-term investment yields significant results for employees, customers & shareholders. National Business Coalition on Health website. http://www.nationalalliancehealth.org/Pitney-Bowes-Case-Study. Accessed February 2017.

9. Sullivan SD, Yeung K, Vogeler C, et al. Design, implementation, and first-year outcomes of a value-based drug formulary. Manag Care Spec Pharm. 2015;21(4):269-275.

Articles

Commentary

Adherence and access to higher-value care can make a big difference in reducing costs for patients and our healthcare system; however, the financial burden of high-deductible health plans and out-of-pocket costs must also be considered.


How Can We Address Barriers to Accessing Needed Care? 

: The impact of pharmacy benefit redesigns on increasing the use of generic drugs is not well studied in individuals using the most drugs: those with multiple chronic conditions. The objective of this study was to analyze whether generic substitution occurs in certain drug classes following a change in co-payments for working-aged adults with chronic conditions.

: This study uses pharmacy and medical claims data from Maryland’s high-risk pool from 2009 to 2011. An interrupted time series design exploits a natural experiment in plan drug benefit redesign that occurred in 2010. The pool lowered co-payments on generic drugs and raised them on preferred and nonpreferred brands. 

: Generalized estimating equations were used to analyze the impact of the policy change on the percentage of generics utilized in the most common chronic disease medication classes. 

: Individuals’ generic use decreases as their number of chronic conditions increases. Antidepressant use increased 9% as a result of the policy, but this was not different for those with varying numbers of chronic conditions. The generic utilization rate remained unchanged for most other classes in the quarter immediately following the policy change. 

: The policy change impacted the generic utilization rate of antidepressants only, and across all classes and time points, generic use was lower among those individuals with more chronic conditions. Understanding why generic use decreases with more chronic conditions will be important in designing health insurance policies to encourage the use of generics, as the number of individuals with multimorbidities continues to increase. 

Generic medication use has grown substantially in the United States over the last 3 decades and now accounts for 83% of all prescriptions filled.

 Despite this overall increase, generic use is not this high across all plans, drug classes (with generic equivalents), or specific subpopulations.

As such, health plans continue to experiment with ways to increase the use of lower-cost generic drugs in the hopes of decreasing overall pharmacy expenditures. Newer value-based designs seek to incorporate the clinical effectiveness of drugs—not just their cost&mdash;when creating cost-sharing structures, such as lowering co-payments on highly effective drugs to treat chronic conditions.

The savings to health insurers and patients can be substantial from increased generic substitution. One study estimated that the savings on just 3 drugs could be $100 million for state Medicaid programs.

 Another estimated that the savings to Medicare’s Part D program could be as much as $1 billion for every 10% increase in the use of generics.

 Using employer data, Liberman and Roebuck found that a 1% increase in the use of generics could lower plan expenditures for pharmaceuticals by 2.5%.

Despite the potential for savings, the use of generics varies across particular drug classes. The Office of the Inspector General of the Department of Health and Human Services used the Medicare Part D program to examine the generic substitution rate across several drug classes in Part D plans (number of generic fills divided by the total number of generic plus multisource brand fills).

Across the Part D drug plans studied, the use of generics varied widely, within class across plans. The generic utilization rate could vary across drug classes over time as new brand drugs come onto the market or older ones go off patent. However, when generics were available, they accounted for 75% to 98% of diuretic prescriptions and for only 33% to 77% of diabetes therapies.

 To increase generic usage, one of the primary formulary tools has been to lower co-payments on generic drugs, or raise them on brand name drugs, or both, in some combination. In much of the literature on the impact of co-payments on prescription drug use, consumers have been shown to be responsive to co-payments when filling prescriptions.

Several studies have examined the overall shift to generics from increased cost sharing for particular drug classes; however, they have neither focused on how the co-payment changes may impact those with multimorbidities differently, nor on whether they are more or less likely to shift to generics when brand name prices rise.

Drug use is highest in those with multiple chronic conditions,

 and therefore, their responses may be different than those found in previous studies: these patients could be more responsive because they are taking more drugs or less responsive if they feel particularly committed to their current mix of medications. There is some indication from previous studies that adults with multiple chronic conditions may be less responsive to shifts in cost sharing even though co-payment increases can raise their out-of-pocket costs, sometimes substantially.

If those with more chronic conditions are less responsive to shifts in co-payment overall, they may be more resistant to switching to generics for reasons other than cost, such as perceptions of effectiveness, provider characteristics, or regimen stability. Shrank et al conducted a patient survey on attitudes toward generic usage and found that although 70% of respondents said the generics were a better value, only 38% agreed that they would rather take generics.

Physicians may also affect generic usage if they do not realize the variation in out-of-pocket costs each patient faces for various medications.

 Unfortunately, few studies have looked at predictors of generic usage in those with multiple chronic conditions and whether increased co-payments are a significant predictor of increased generic usage.

The objective of this paper is to examine whether the percentage of generic fills increased within particular drug classes for chronic diseases after changes in drug co-payment requirements. In July 2010, Maryland’s high-risk pool reduced co-payments on generic drugs and raised them on preferred and nonpreferred brand name drugs, creating a natural experiment.

The policy change was designed to increase incentives for people to substitute generics. Prior to the Affordable Care Act (ACA), high-risk pools offered coverage to those with pre-existing conditions who did not have affordable coverage in the private, individual market. High-risk pool enrollees were a group with chronic conditions, ideal for studying the impact of co-payment changes in generic usage for those with multiple morbidities.

Understanding how to control spending while maintaining health for those with multiple chronic conditions will be key in helping policy makers and insurers design better health plans, particularly as high-risk pools again enter the national policy discussion as one possible part of current reform proposals.

The ACA established preexisting condition plans to help those individuals with chronic conditions gain health insurance, that ended as the marketplaces came online in 2014.

 High-risk pools, however, existed in 35 states before the ACA—some for more than 30 years. Maryland had the country’s fourth largest high-risk pool until high-risk enrollees began entering the health insurance exchanges in 2014.

The reforms to the rating rules for setting premiums in the individual market made the high-risk pools obsolete. Insurers are required to cover those with preexisting conditions, and there are premium tax credits that cap premium payments to a percentage of income if the person purchases the second-lowest cost silver plan. Several recent health care reform proposals at the federal level would re-instate the high-risk pool mechanism, making the data presented in this study helpful in understanding the potential health care utilization of high-risk pool enrollees.

To have qualified for a high-risk pool prior to the ACA, the person must have attempted to purchase coverage on the private individual market and been denied because of preexisting medical conditions. The Maryland Health Insurance Plan (MHIP) used funds from a statewide hospital tax to subsidize health insurance premiums, which were set at about 125% of the average premium in Maryland’s individual market.

For the average individual in 2012, the premium on the individual market was about $500 per month. High-risk pool enrollees did not have the option to enroll in other government programs, either because they had incomes higher than Medicaid eligibility levels or were not eligible for Medicare. 

In July 2010, MHIP lowered the co-payments for generic drugs by 33% to 50% depending on the formulary tier and raised the co-payments on brand name and nonpreferred brand name drugs as much as 37%. The co-payments on the newly created specialty tier increased 40% for most of the MHIP plans. Table 1 shows the difference in co-payments for the various pharmacy drug tiers and for the different plan types within MHIP. 

An interrupted time series study design was used to analyze the changes in the generic utilization rate (GUR) for selected classes, using administrative claims data from July 2009 to June 2011. The sample consisted of those who met the following requirements: continuously enrolled for the 2-year analysis period, aged 18 to 64 years, and had at least 1 fill in the selected classes in the year before the policy change. 

Drugs were assigned to classes using Multum’s Lexicon database, which groups National Drug Codes into therapeutic classes. Sixteen classes were initially selected: antihyperlipidemic agents; antidiabetic agents; beta-adrenergic blocking agents (beta-blockers); angiotensin-converting enzyme (ACE) inhibitors; antihypertensive combinations; sex hormones; thyroid hormones; bronchodilators; diuretics; calcium channel blockers; leukotriene modifiers; antidepressants; anxiolytics, sedatives, and hypnotics; anticonvulsants; antipsychotics; and central nervous system (CNS) stimulants.

Due to the high proportion of generics already being used in this sample for diuretics, calcium channel blockers, ACE inhibitors, and beta-blockers in the pre-period, these classes were removed from consideration for further analysis. Leukotriene modifiers were also removed from further analysis because of the absence of generic alternatives.

The particular classes in this analysis are used to treat chronic conditions and therefore should be taken regularly; they were also the classes of drugs with the greatest volume of fills. Two exclusions should be noted: analgesics and antivirals were among the top classes by volume.

Analgesics were removed because of their use in acute pain management. A separate program in Maryland (and similar ones in many other states) paid some cost sharing for patients with HIV/AIDS, so these were also removed from the analysis. 

The main covariates used are age, gender, number of chronic conditions, and plan type. Age is measured continuously, and gender is binary (1 = female). The number of chronic conditions is continuous. International Classification of Disease, Ninth Revision, Clinical Modification (ICD-9-CM) codes were translated into categories of conditions and chronic diseases using the Clinical Classifications Software from the Agency for Healthcare Research and Quality.

 For a condition to be counted as chronic, the ICD-9-CM code had to appear in at least 2 outpatient visits or at least 1 inpatient visit. 

Plan type, such as preferred provider organization (PPO) or health maintenance organization (HMO), was also added as a series of dummy variables, which represent differences across plan benefit structure. Plan type also reflects differences in income, since MHIP provides a second set of plans, called MHIP Plus (MHIP+), for low-income individuals.

Prior to the implementation of the ACA, low-income childless adults in Maryland were also not eligible for Medicaid, and parents were only covered to 116% to 122% of the Federal Poverty Level (FPL).

 To qualify for MHIP+, the person must have had an annual income below 200% of the FPL ($21,660 in 2010). 

The main outcome is the percentage generic of all drugs filled in a particular class. The GUR is calculated as the number of generic fills in the class divided by the total number of fills in the class. Other work in this area also calculates the generic substitution rate, which is the number of generics in the class divided by the total number of generics and multisource brands in a class.

This particular dataset did not have enough multisource brand fills to create a meaningful measure. An additional measure that could have been used would have been, at a more granular level, the number of days supplied. Because the objective of the paper was to focus on chronic disease medications that should be taken regularly, this outcome is saved for future analyses. 

An interrupted time series design using individual-level data was used. The key variable of interest was the policy change indicator, where the null hypothesis is that in the absence of the policy change, the trend of generic utilization would have remained constant:

Yit = ƒ(β0 + β1timet + β2policyt + β3post timet + β4chronici + β5chronic × policyit + β6chronic × post timeit + Xitλ) 

β1timet is the time trend, measured quarterly since July 2009 to account for secular increases in the use of generic drugs. MHIP runs on a fiscal year from July to June. The policy change is included as a binary indicator equal to 0 in the pre-period and 1 in the post period.

The post time variable measures the quarters continuously since the policy change, so it is equal to 0 in the pre-period and begins at 1 in July 2010. β2policyt + β3post timet represents the impact of the policy change for the whole sample, including both the initial impact and the change in the trend after the intervention.

β4chronici represents how patients with different numbers of chronic conditions will react. β5chronic × policyit represents how the policy impact changes across groupings of numbers of chronic conditions, and β6chronic × post timeit represents whether the trend in the post period is different for different numbers of chronic conditions after the policy change. Xitλ represents the vector of control covariates: age, gender, and plan type.

The outcomes are analyzed using generalized estimating equations (GEEs) specifications to analyze the impact of the policy change, because having a prescription in 1 month is highly correlated with having a prescription drug in the next month, particularly for patients with a chronic disease. GEE allows for the specification of the family (ie, Gaussian) and link function (ie, identity or log) for the mean, making this a very flexible regression model.

The correlation structure was assessed through examining the correlation between the outcome overtime. Binomial models were used given the distribution of the outcomes (a proportion varying from 0 to 1).

The final sample consisted of 6215 individuals, all of whom were aged 18 to 64 years, were continually enrolled across the 2-year study period, and filled at least 1 prescription in the selected classes in the pre-year (Table 2). Their average age was 51 years, with just over half female.

Table 2 also shows that as the number of chronic conditions increases, the number of different drug classes the person used increases. For example, individuals with 6 or more chronic conditions used more than 10 different classes of medications each quarter.

Table 3 shows the average GURs in the pre- and post-period. Without controlling for the general increase in generics over time, this table suggests whether increases in generic usage arose in particular classes after the policy change.

The percentage of generics used in the year before the policy change ranged from a low of 5% in the bronchodilators class to a high of 84% for the anxiolytics/sedatives. The quarterly GUR increased for most classes in the post period, but by small amounts.

Anxiolytics/sedatives, antidepressants, CNS stimulants, antihypertensives, antihyperlipidemics, sex hormones, and thyroid medications are classes that demonstrated significant increases in generic percentage in the post period (P <.05). For example, the GUR for antidepressants increased from 62% to 70% in the post period. The GUR of antihypertensives increased from 57% to 65% in the post period. Nevertheless, these unadjusted results do not control for the general increase in generic use over time.

Figure 1 shows the quarterly trend in generic usage separated by the number of chronic conditions. This figure shows that once a person has a chronic condition, they are less likely to be using generic drugs. The graph also shows that the usage of generic drugs is increasing steadily over time across all chronic conditions. However, visual inspection does not detect a marked shift in the trend or the level of generic usage at the policy change time point (marked with the vertical line). 

The policy does not have a differential impact on those with different numbers of chronic conditions. Across most of the drug classes, the interaction of the number of chronic conditions and the policy change is not statistically significant, nor is the odds ratio on the interaction of the number of chronic conditions and the post time trend.

The eAppendix (available at ajpb.com) shows the full regression results for each drug class. For the classes of mental health medications, antidepressants are the only class with a statistically significant increase in the GUR in the post period, according to the significance level of the coefficient on the policy change in the regressions. 

For the other coefficients in the model, plan type (eg, HMO, PPO) varies as to whether it is a significant driver in determining the GUR. The $500 PPO group was held as the reference group to examine the impact on the low-income plans in MHIP+.

Original Research

Patients with multiple chronic conditions are less likely to use generic drugs, even when facing lower co-payments.


Generic Use Under Changing Co-Payments: Implications for Those With Multiple Chronic Conditions 

: The purpose of this study was to evaluate the impact of pharmacist-delivered medication therapy management (MTM) services via telephone (enhanced MTM intervention) versus the impact of an informative detailed medication letter sent via mail (minimal MTM intervention) on patients’ acceptance of guideline-recommended pharmacotherapies, specifically angiotensin-converting enzyme (ACE) inhibitors/angiotensin II receptor blockers (ARBs) and statins. 

: A retrospective database analysis was completed using pharmacy claims and enrollment data from 1 national pharmaceutical benefits manager. 

: Medicare Part D beneficiaries with diabetes, managed by 1 pharmacist-based medication management center, received either: 1) a pharmacist’s recommendation, delivered via telephone, to add an ACE inhibitor or ARB and/or a statin to existing therapy, or 2) an informative letter detailing current therapies. The primary outcome measure was acceptance of guideline-recommended therapy determined by the presence of at least 1 prescription claim for the target drug in the postintervention period. Propensity score matching and conditional logistic regression methodologies were used to assess the comparative effectiveness of the interventions. 

: Patients who received the telephone intervention were 6.33 times more likely to be taking both medications during the postintervention period compared with those who received the letter intervention (P <.001). A greater proportion of patients who received the telephone intervention were taking both drugs during the postintervention period, with the greatest difference in those initially receiving a statin to which ACE inhibitor/ARB therapy was added (41.18% vs 7.23%). 

: Telephone-based MTM services provided to Medicare Part D beneficiaries with diabetes positively impacted acceptance of guideline-recommended ACE inhibitor/ARB and/or statin therapies relative to the letter intervention.

 According to 2014 national estimates provided by the CDC, 29.1 million people in the United States live with diabetes, with 8.1 million of these individuals being undiagnosed.

 The prevalence of this disease in those 65 years or older is 25.9%, with this age group accounting for more than a third of all individuals with diabetes in the United States.

One study projected that by 2050, incident cases of diabetes are expected to almost double and the prevalence will increase to between 21% and 33% of the entire population based on 2010 estimates.

 The economic burden of diabetes has been estimated at $174 billion, including direct medical costs, reduced productivity, and costs to treat diabetes-related chronic conditions.

 Chronic conditions that have the largest impact on resource consumption in patients with diabetes include cardiovascular disease (CVD), neurological symptoms, and renal complications.

Behind healthcare expenditures for inpatient hospital stays for general medical conditions, CVD-related inpatient resource consumption is the second largest category of associated costs.

 Moreover, CVD is the leading cause of morbidity and mortality in diabetics, with death from coronary heart disease (CHD) 2 to 3 times higher in patients with diabetes than those without.

Multiple associations have each developed guidelines for the management of CVD risk factors.

 The 2010 American Diabetes Association (ADA) guidelines detail current standards of medical care and CVD management in diabetics.

 Recommendations for the prevention and management of CVD include blood pressure control, lipid management, use of antiplatelet agents, smoking cessation, and screening for/treatment of CHD.

Hypertension and dyslipidemias remain the most prevalent comorbidities in patients with diabetes. In addition to diet and lifestyle modifications, first-line pharmacotherapy for diabetics with a diagnosis of hypertension include either an angiotensin-converting enzyme (ACE) inhibitor or angiotensin II receptor blocker (ARB).

Studies have demonstrated risk reductions in morbidity and mortality associated with cardiac events resultant from primary or secondary prevention interventions.

 Aggressive lipid management with hydroxymethylglutaryl coenzyme-A reductase inhibitors (statins), along with diet and lifestyle modifications, is also a first-line recommendation of the ADA.

Patients 40 years or older with diabetes and either overt CVD or without overt CVD (but who have 1 other risk factor for CVD regardless of baseline cholesterol levels) are indicated for pharmacotherapy.

 The efficacy of statins in primary and secondary prevention of cardiovascular events in patients with diabetes has also been well established.

 Although the evidence supports the use of these pharmacotherapies for CVD in patients with diabetes, they remain underutilized.

 Medication therapy management (MTM) programs could be one tool used to assist in the initiation and adherence to guideline-recommended therapies. The 2003 Medicare Modernization Act stipulated subsidized drug coverage for Medicare beneficiaries under Medicare Part D, with a mandate for sponsors to establish MTM programs as part of each plan structure.

 Consequently, the potential impact of MTM on subsequent outcomes is the focus of greater study. 

MTM services have been delivered in person, via telephone, and/or via mail. It is important to note that although many models and mechanisms of MTM delivery have been implemented over time, in the early phases of implementation, mailed letter interventions often consisted solely of detailed lists of medications intended to provide a tool for future patient—provider discussion during follow-up visits.

This model of MTM intervention can be considered a passive intervention for which success is contingent upon the patient remembering to actively contact their physician for follow-up and bringing the medication list to future visits. On the other hand, in-person and telephone-based proactive MTM models allow for 2-way MTM provider—patient communication and greater patient engagement in decision making regarding their treatment. As such, improved outcomes may result from proactive and patient-engaged models of MTM. 

Physicians, nurses, and pharmacists have delivered—and continue to deliver&mdash;varying models of these services to patients with a variety of chronic diseases. Diabetes continues to be a priority for MTM, with 96.8% of all programs approved in 2010 indicating diabetes as a core targeted disease.

 Positive clinical and economic outcomes of pharmacist-delivered care have been demonstrated in multiple programs, but most notably in the Asheville Project.

A paucity of research in this area remains, but continuous evaluation of the impact of MTM programs on health outcomes is necessary to investigate potential economic and policy implications for stakeholders. Therefore, an evaluation of the impact of the various types of interventions aimed at increasing adherence to recommended clinical guidelines is warranted. 

The purpose of this study was to evaluate the impact of pharmacist-delivered MTM services via telephone (enhanced MTM intervention) versus the impact of an informative detailed medication letter sent via mail (minimal MTM intervention) on patients’ acceptance of guideline-recommended pharmacotherapies, specifically ACE inhibitors/ARBs and statins. All patients had diabetes and were enrolled in a Medicare Part D program. 

Claims and enrollment data from January 1, 2006, through June 30, 2007, for patients enrolled in a Medicare Part D plan, administered by 1 national pharmaceutical benefits manager (PBM), were utilized in this retrospective database analysis. The PBM identified probable patients with diabetes via pharmacy claims from January 1, 2006, through October 30, 2006.

Patients were included if they: 1) were taking at least 1 medication used in the treatment of diabetes with 2 fills or at least a 60-day supply of medication during the preintervention period, including injectable and oral therapies; or 2) met MTM eligibility requirements, which included at least $4000 in projected annual prescription drug expenditures, presence of multiple chronic conditions, and taking multiple Medicare Part D medications. Additionally, patients in this analysis had at least 6 months of continuous plan enrollment before and after the intervention and had a documented receipt of either a telephone or letter intervention. 

Finally, patients must not have had a prescription for an ACE inhibitor/ARB and/or statin between January 1, 2006, and the time of the intervention, which occurred from November 1, 2006, through December 31, 2006. These patients may have initially received an ACE inhibitor/ARB but not a statin, a statin but no ACE inhibitor/ARB, or neither an ACE inhibitor/ARB nor statin.

All brand, generic, and combination products listed in the Medi-Span Drug Information Database and available in the United States by prescription during the study time period were used to identify whether patients had received 1 of the 3 medication classes. Subjects were excluded from the current analysis if there was invalid member identification, they were recommended for or residing in long-term care facilities, or they were younger than 40 years. 

Database management and statistical analyses were performed in SAS version 9.2 (SAS Institute Inc, Cary, North Carolina) and STATA/IC 11 (StataCorp, College Station, Texas). Human subjects approval for this study was obtained from the University of Arizona Institutional Review Board. 

The telephone intervention, or the enhanced MTM intervention, group consisted of patients who had complete and accurate telephone numbers and were not receiving guideline- recommended ACE inhibitor/ARB and/or statin therapy, as deemed appropriate by a pharmacist review of medications and conditions. These patients were contacted via telephone by a pharmacist who confirmed conditions, recommended the addition of the target medications, and suggested follow-up and discussion with their physician.

Afterward, a fax was sent to the patient’s physician with the same recommendation, referencing the published ADA treatment guidelines supporting the use of these medications. This study considers the “telephone” intervention group to be those who were contacted by a pharmacist for a 1-on-1 discussion of specific recommendations. In addition, the patient’s physician was also contacted via fax with the same recommendations discussed with the patient. 

The letter, or minimal, intervention group consisted of all other patients eligible for inclusion for whom telephone contact information was unavailable or inaccurate at the time of outreach and upon review of their medication history were noted as not receiving guideline-recommended ACE inhibitor/ARB and/or statin therapy for inferred conditions. These patients were mailed an informative letter that listed their current prescribed medications and suggested that patients take the list to all medical appointments for discussion with their physicians; however, no specific recommendations were provided regarding any medication and no physician outreach was conducted.

This study considers the “letter” intervention group to be those sent a letter with no specific recommendations and no physician outreach. 

The primary outcome and success of the intervention was defined as the patient having at least 1 prescription claim for both an ACE inhibitor/ARB and statin in the postintervention period. Covariates of interest included patient age at the time of the intervention, gender, total prescription costs, and total number of claims in the preintervention timeframe. As only prescription medication claims were available for analysis, the RxRisk algorithm was used to calculate a risk-adjustment score, with higher scores reflecting a higher number of possible chronic diseases and comorbid conditions.

To control for possible bias that may have been introduced due to differential selection in this observational study, patients in the telephone intervention group were matched to patients in the letter intervention based on calculated propensity scores.

 To assure an exact match on the preintervention drug group (ie, ACE inhibitor/ARB only, statin only, neither), matches were performed for each subgroup separately, then aggregated for all analyses. In cases where 3 letter intervention patients could not be adequately matched to a telephone intervention recipient, a 2-to-1 match was allowed. 

Descriptive statistics were used to characterize the cohort of matched and unmatched patients. To verify that the propensity score matching removed any significant differences between groups, χ2 tests for categorical variables or Student’s t tests for continuous variables were performed for the matched and unmatched cohorts.

A conditional logistic regression analysis was performed in the matched cohort to determine the relationship between independent variables of interest and the primary outcome of the presence of target drug-related claims during the postintervention period. The a priori 2-sided level of significance was P <.05.

Proportions of patients achieving the outcome of interest in each initial drug group (ie, ACE inhibitor/ARB only, statin only, neither) and RxRisk categories within each intervention were reported due to the inability to perform regressions due to sample size limitations. Chi-squared statistics indicating significant differences among these subgroups were also reported. 

Baseline characteristics of the unmatched and matched cohorts are delineated in Table 1. In the unmatched cohort, all but 2 of the covariates were statistically significantly different between the telephone and letter intervention groups. After matching, the intervention groups were similar on all independent variables of interest.

All patients in the telephone group (n = 182) were matched to 3 letter intervention patients, except for 11 patients who could be suitably matched to only 2 controls, thus providing 535 letter intervention recipients. RxRisk scores were categorized, with patients receiving a score of 1 or 2 classified in the “low” risk group, those with a score of 3 or 4 classified in the “intermediate” risk group, and those with a score of 5 or higher classified in the “high” risk group.

Total costs and number of claims were considerably skewed; thus, a log transformation was employed to achieve normality assumption. As a result, the model fit improved. The matched population (n = 717) was predominantly female, with a mean age of 65.21 years. The mean RxRisk score was 3.32 (standard deviation = ± 1.37), with 54% of patients falling into the intermediate RxRisk category.

Untransformed total costs in the 2006 baseline period were approximately $4792 per patient, or $399 per member per month, with an average of 71 claims per patient. More than 46.7% of patients were already receiving statin therapy only, 21.4% were receiving an ACE inhibitor/ARB only, and 31.9% were not receiving either guideline-recommended medication. 

Results from the conditional logistic regression are displayed in Table 2. Patients receiving the telephone intervention were 6.33 times more likely to be taking both medications after the intervention compared with those who received the letter intervention (P <.001). Other covariates in the model did not reach statistical significance. Postestimation model checks for this regression indicated good overall model fit. 

Outcomes in 2 main subgroups were investigated to explore in which populations these interventions had the least and greatest possible impact. Table 3 presents the proportion of those achieving a successful outcome by intervention type and initial drug group classification.

All groups showed a higher percentage of success with the telephone intervention versus the letter intervention, although this difference was greatest in patients initially taking a statin to which an ACE inhibitor/ARB was added (41.18% vs 7.23%, respectively). Table 4 presents those with a successful outcome with each intervention by RxRisk category.

Again, a greater proportion of patients in the telephone intervention group had a successful outcome compared with the letter intervention in each of the 3 RxRisk categories. A greater overall percentage of the lowest and intermediate RxRisk groups achieved a successful outcome, regardless of intervention (11.40% and 14.77%, respectively), compared with the highest RxRisk group (7.87%).

However, it is important to note that a larger proportion of patients in the intermediate and highest risk groups who had received the telephone intervention were taking both guideline-recommended medications during the postintervention period. 

Previous literature has shown the positive impact that pharmacist-delivered MTM and disease management services have on clinical, economic, and humanistic outcomes in the diabetic patient population.

Overall, based on pharmacy claims, 28% of patients in the unmatched cohort and 32% of patients in the matched cohort were not receiving either guideline-recommended therapy during the period of time under study, thus demonstrating possible underuse of these effective medications in this population.

The telephone intervention was statistically significantly more effective than the letter intervention in achieving the successful outcome of patients receiving both guideline-recommended therapies, demonstrating that this model of program and mechanism for intervention delivery was able to educate and engage patients, as well as inform and ultimately influence prescribers about guideline-recommended cardiovascular therapies for patients with diabetes.

More patients in the telephone group were successfully taking both medications in all initial drug groups (ie, neither drug, ACE inhibitor/ARB only, statin only) after the intervention than those receiving the letter intervention. The most pronounced difference was in the group of patients who were initially only taking statins (ie, ACE inhibitor/ARB was successfully added): 41.18% of patients were on both medications after telephone intervention compared with 7.23% receiving the letter intervention. The subgroup in which the pharmacist-delivered MTM service did not have a significant impact, compared with the letter intervention, was the group in which patients were initially taking only an ACE inhibitor/ARB.

Results of the current analysis indicate that the patients in the intermediate RxRisk group were slightly more likely to be taking both medications after either intervention, whereas those in the highest RxRisk group were less likely, compared with the lowest RxRisk category. When examining outcomes by RxRisk group and intervention type, it is possible the relative ineffectiveness of the letter intervention in the intermediate and highest RxRisk groups was responsible for the results, as the largest impact of the telephone intervention was in the patients whose RxRisk score indicated higher numbers of chronic conditions. 

Results of previous studies concur with the findings of the current program evaluation, showing the effectiveness of clinician-led medication management interventions in patients with diabetes indicated for treatment with cardiovascular pharmacotherapies, such as ACE inhibitors/ARBs and statins. A retrospective chart review of 75 patients with diabetes in a collaborative care model family medicine clinic determined the influence a pharmacist intervention had on the use of ADA guideline-recommended ACE inhibitor, ARB, and aspirin use.

At baseline, 59% of these patients were receiving ACE inhibitor or ARB therapy and 34% were receiving aspirin, with postintervention use increased to 90% and 68%, respectively (P <.0001). In a similar study, a pharmacist-coordinated diabetic disease management program effectively increased statin use from 35% before pharmacist intervention to 50% post intervention, ACE inhibitor use from 40% to 48%, ARB use from 15% to 19%, and daily aspirin use from 45% to 80%.

 However, Monte et al, while reporting reductions in primary diabetic and metabolic endpoints, were not able to show that pharmacist-led pharmacotherapy management significantly increased use of aspirin, ACE inhibitors/ARBs, or statins.

A telephonic pharmacist-delivered medication therapy management intervention is an effective method for increasing the utilization of guideline-recommended cardiovascular therapies in high-risk Medicare patients with diabetes.


Telephone-Based Cardiovascular Medication Therapy Management in Medicare Part D Enrollees With Diabetes

: The risk of clinically significant drug-related problems is a major concern, particularly among elderly, polypharmacy-treated patients. Previous efforts to develop computerized alert systems have shown limited effectiveness. The purpose of this study was to evaluate the efficacy of a novel technology, the Drug-Drug Interaction Plus (DDI+) decision support system, used in a real-world ambulatory health maintenance organization (HMO) setting and developed to overcome the limitations of currently available programs.

: We conducted a controlled trial of the DDI+ system in Leumit Health Services (LHS), an HMO in Israel that provides coverage to approximately 700,000 members with approximately 2000 physicians nationally. All LHS physicians were geographically allocated by region to a study or control arm. Physician access rates, alert resolution rates, and resource utilization of their patients were evaluated and compared between the intervention and control arms.

 = .001) fewer mean episodes of hospitalization, 1.5% (

= .01) fewer mean drugs dispensed, and 2.1% (

 = .055) fewer mean episodes of imaging in

the intervention population versus controls. Drug-related problems that were re-encountered were often rectified without

: Comparing the acceptance by physicians of the DDI+ system to reports of acceptance of similar systems, DDI+ was observed to be better accepted. This will possibly initiate improvements in resource utilization patterns. We surmise that improved physician willingness to access the system can be attributed to the novel graphic presentation, specificity, and integration of DDI+ into the work flow of routine clinical practice.

Drug prescribing is among the most powerful and ubiquitous tools employed by physicians for treating patients. Over the decades, thousands of safe and effective drugs have been added to the armamentarium at physicians’ disposal, empowering them to treat most of the acute and chronic conditions encountered in the community setting. 

These developments, however, while generally positive, have not emerged without adding concomitant risks of harm to the patient. Administration of multiple drugs, and of herbal preparations, vitamins, and food supplements, directly increases the risk of avoidable medication error—induced sequelae, which include adverse effects, pharmacokinetic interactions, decreased efficacy, morbidity, mortality, and unnecessary resource use.

Furthermore, the degree of risk is exacerbated when multiple physicians practicing various subspecialties independently prescribe medications for a patient in the absence of a case manager whose job would be to actively coordinate a cohesive care plan. 

The risk of clinically significant drug-related problems (CSDRPs) is of major concern, particularly among elderly, polypharmacy-treated patients in whom the incidence of major iatrogenic events is estimated at more than 25%.

 Overall, CSDRPs are responsible for approximately 2% to 5% of all hospital admissions.

 Unfortunately, the growing use of electronic health record (EHR)-based modules for addressing CSDRPs has not dramatically changed the occurrences of such outcomes, given that more than 90% of physicians override or do not use these modules.

Accordingly, the development and implementation of intervention strategies, such as the requirement for pharmacists to assess drug-related problems prior to dispensing and improved uptake of existing technologies to prevent CSDRPs, is a well-established practice in managed care systems. 

Although significant progress has been made in developing new technologies, the applications currently available provide suboptimal prevention of CSDRPs. Their many shortcomings include lack of systematic assessment of pharmacokinetic/pharmacogenetic data,

 long and difficult-to-comprehend textual outputs,

 lack of suggestions for alternative drug options, an inability to evaluate drug combinations involving more than 2 concomitantly prescribed drugs, and limitation of use to the prescribing phase of the visit.

Additionally, because these systems are seldom evaluated in robust controlled trials, few data are generally available to clinicians and medical managers to evaluate the potential value of these technologies in a real-life clinical setting. 

 A new effort to overcome many of these limitations is the Drug-Drug Interaction Plus (DDI+) decision support system (Mediseen eHealth Ltd, Teva Pharmaceutical Industries Ltd, Israel). The DDI+ output is based on a unique technology that synchronizes and integrates pharmacodynamic, pharmacokinetic, and pharmacogenetic data from leading international databases (First Data Bank and GeneMedRx/Genelex United States) along with patient-specific factors accessed from the patient’s EHR in real time.

The system is periodically updated with new relevant data; it incorporates innovations designed to overcome the limitations of other currently available systems to improve physician uptake and to harness the data available from EHRs to enhance the precision and specificity of CSDRP identification. 

DDI+ presents real-time output via a visual user interface that enables detection, prioritization, and alternative suggestions within a few seconds while presenting only essential output to physicians throughout the entire patient office visit. DDI+ is designed to flag most of the clinically significant interactions that may occur between a drug and any other factor that may affect the pharmacokinetics/pharmacodynamics of administered medications (allergy/sensitivities, renal functions and liver function, food, vitamins, herbals, genomics, smoking, age, gender).”

Additionally, in contrast to other programs limited to evaluating 2 drugs at a time, the design of DDI+ allows it to evaluate drug regimens composed of 2 or more agents to be administered concomitantly. Furthermore, DDI+ can incorporate pharmacokinetic data to prevent not only improper dosing, but also false-positive alerts that might otherwise occur when inadequately robust literature would indicate interactions. DDI+ can detect when drug-serum levels would not be affected by particular doses of 2 concomitantly prescribed drugs, despite less-specific information to the contrary in the literature. 

We hypothesized that incorporating these improvements would overcome the barriers that have impeded physician use of previous systems, thereby yielding measurable improvements in the use of the following resources: hospitalization, number of drugs purchased, and imaging. The purpose of this study was to prospectively evaluate the efficacy of the DDI+ system to improve the efficiency of care in a real-world ambulatory health maintenance organization (HMO) setting via a large, robust, controlled trial.

DDI+ is a Web-based system that interfaces and operates in the background of an EHR. Throughout a patient visit, the physician sees that the relevant icon on the toolbar of the EHR’s main page is either “red” or “green”: red signifies potential CSDRPs and green signifies their absence. When the icon is red, the physician can enter the DDI+ system either volitionally or mandatorily, depending on organizational policy.

The system immediately displays the detected CSDRPs and provides information necessary to choose alternative regimens or make drug changes. Upon the physician’s selection of a regimen modification or the drug to be changed, the system displays potential alternatives; it then calculates the outcomes of these (1 or more) new potential regimens, facilitating the prescription of safer alternatives.

Once a CSDRP is rectified, the icon automatically switches from red to green. This platform thereby is designed to reinforce, albeit not replace, sound clinical judgment (nor does it obviate the need for independent learning). 

The DDI+ system platform was installed to interface with the EHR program used by Leumit Health Services (LHS), an HMO in Israel that provides health coverage to approximately 700,000 members with approximately 2000 physicians practicing in LHS-operated clinics or in private practice nationally.

LHS first implemented its EHR system in 1999 and has achieved 100% uptake among all physicians treating LHS patients in the community setting. Complete computerization has facilitated the construction and maintenance of a data warehouse that includes comprehensive records of the following: drugs prescribed and dispensed within the system; laboratory tests ordered and their results; referrals to specialists; diagnostic imaging; hospitalizations, including scanned discharge letters; and ambulatory procedures performed at LHS clinics.

Drugs prescribed outside the LHS system are not initially captured, but if a prescription is filled at an LHS-affiliated pharmacy or if the drug is later re-prescribed by an LHS physician, the system will document the prescription. Furthermore, LHS has developed and implemented data-capture and analysis tools for the EHR data warehouse that enable both longitudinal and retrospective observation of resource use in its defined patient and physician populations.

All elderly patients (≥65 years) receiving polypharmacy (≥5 concomitant chronic medications) were included for healthcare use assessments. Access rates and the system’s educational capacities were evaluated only for primary care physicians who treated these elderly patients (n = 369 and n = 346 for the intervention and control arms, respectively) as they were the main target population of the DDI+ system from the LHS perspective. 

Study Design, Data Accrual, and Outcome Variables

The LHS management structure is subdivided into 4 geographic regions subordinate to a central management. Physicians were geographically allocated by region where they practice to a study arm that received access to the DDI+ platform (North and Central regions) or a control arm that was not (Jerusalem and South regions).

Because the North and South regions and the Central and Jerusalem regions are similar in patient and physician demographics, the 4 regions were accordingly allocated into 2 groups. The allocation of these 2 groups into study and control arms was arbitrary. We chose to implement the system on regions in their entirety and not to randomize individual physicians in order to prevent cross-group contamination among physicians in different arms of the study working in the same region.

Neither arm was exposed to other decision-support technologies. After a run-in period that included an individual 30-minute training program, the system was formally launched in the study arm of physicians on January 1, 2013, for a 6-month controlled study period. Use of the program was voluntary, and physician access patterns during the study were not monitored to promote use among physicians who declined to do so.

Data were accrued and compared for both groups. To evaluate the efficacy of the system to reduce resource use, we calculated and compared the average number of episodes of hospitalization and imaging and the number of drugs dispensed per patient in both arms during the study period with those observed in these patients during the 12-month preintervention period. We then calculated the relative changes observed between the pre- and postintervention periods.

The rationale for analyzing the use of imaging was that because a physician may not immediately identify a primary complaint as a CSDRP, these patients are frequently referred to diagnostic procedures, such as imaging, to rule out suspected pathologies included in the differential diagnosis.

The effectiveness of the system to rectify alerted problematic drug combinations was measured by calculating the percentage of red alerts that turned green among patients treated by physicians who accessed the system compared with the rate observed among patients treated by physicians in the intervention arm who did not access the system. 

A controlled trial of a system for rectifying and preventing drug-related problems was conducted to evaluate improvement in the efficiency of resource utilization.


March/April 2017

How Can We Address Barriers to Accessing Needed Care?

Generic Use Under Changing Co-Payments: Implications for Those With Multiple Chronic Conditions

Telephone-Based Cardiovascular Medication Therapy Management in Medicare Part D Enrollees With Diabetes

Large-Scale, Community-Based Trial of a Personalized Drug-Related Problem Rectification System

Follow the Numbers: A Focus on Anti-Inflammatory Medications