Evolving Strategies for Optimal Care Management and Plan Benefit Designs

Published Online: November 30, 2012
John M. Cruickshank, DO, MBA, CPE
As a prevalent, complex disease, diabetes presents a challenge to managed care. Strategies to optimize type 2 diabetes care management and treatment outcomes have been evolving over the past several years. Novel economic incentive programs (eg, those outlined in the Patient Protection and Affordable Care Act of 2010 that tie revenue from Medicare Advantage plans to the quality of healthcare delivered) are being implemented, as are evidence-based interventions designed to optimize treatment, reduce clinical complications, and lower the total financial burden of the disease. Another step that can improve outcomes is to align managed care diabetes treatment algorithms with national treatment guidelines. In addition, designing the pharmacy benefit to emphasize the overall value of treatment and minimize out-of-pocket expenses for patients can be an effective approach to reducing prescription abandonment. The implementation of emerging models of care that encourage collaboration between providers, support lifestyle changes, and engage patients to become partners in their own treatment also appears to be effective.

(Am J Manag Care. 2012;18:S228-S233)
Why Managed Care Is Concerned About Diabetes

Diabetes is a striking example of how slowly population health improvements such as strengthened primary care, new models of care coordination, efforts to prompt consumers to change behavior, and payment incentives to stimulate quality improvement and appropriateness of care are implemented.1 As benefit designs evolve, many employer groups will shift to an intensive focus on diabetes management by investing in efforts that will help their employees achieve significant reductions in their out-of-pocket expenses for diabetes care. Diabetes can also serve as a model for other longterm care situations, such as hypertension and weight loss. Payment incentives can ultimately stimulate quality care improvement. Economic incentives are also necessary to motivate implementation of the patient-centered medical home and accountable care models of care delivery.

Diabetes currently affects 25.8 million adults in the United States, or 8.3% of the population.2 By 2050, the adult prevalence is projected to be as high as 1 in 3.3 Diabetes is the leading cause of adult blindness and end-stage kidney disease, and also increases the risk of cardiovascular disease by 2- to 4-fold.2 Prediabetes, an intermediate state between normal glucose homeostasis and diabetes, is projected to affect more than one-third of the American population by 2021.1

Total costs for plan members with diabetes are 2.7 times higher than those for patients without diabetes.1 If, as predicted, 15% of American adults have diabetes by 2021, the estimated total costs of the disease will exceed $3.5 trillion.1 The total cost of medical care in individuals with diabetes increases as the glycated hemoglobin (A1C) levels increase,4 most likely because elevated A1C level is associated with a greater number of complications.5 For example, every 1% increase in A1C level increases the risk of microvascular complications by 37%, any diabetes-related complication by 21%, fatal and nonfatal myocardial infarction by 14%, and diabetesrelated death by 21%.5

In addition to its value as an indicator of diabetic complications, A1C level is used to examine the relationship between glycemic control and healthcare utilization and cost. Successful glycemic control was shown to have positively affected healthcare utilization and cost when examining (1) baseline A1C levels and costs over a 3-year follow-up; (2) mean A1C level over a 3-year period and adjusted rates for hospital admissions; and (3) the effect of change in A1C level on total healthcare expenditures.6

Guidelines published by the American Diabetes Association7 (ADA) and the American Association of Clinical Endocrinology/American College of Endocrinology8 (AACE/ACE) emphasize the importance of lowering A1C level to less than 7.0%, a level that minimizes the risk of microvascular and macrovascular complications and is associated with lower overall costs (Table).6 However, nearly half of patients diagnosed with type 2 diabetes mellitus (T2DM) have suboptimal glycemic levels.6 Oglesby et al reported that compared with patients with “good” glycemic control (A1C level <7%), patients with “poor” control (A1C level >7.01%) account for the greatest proportion of the cost burden associated with diabetes.6 Thus, an investment in intensive glycemic control can provide a substantial cost benefit.6

Diabetes Treatment Guidelines

Guidelines developed by 2 of the most prominent professional organizations involved in the care of patients with diabetes, the ADA and AACE/ACE, provide evidence-based recommendations for the diagnosis and treatment of patients with diabetes.7,8 Although it is critical that guidelines reflect the most recent advances in medical therapy to be relevant, keeping the guidelines up-to-date can be difficult in a disease state with rapidly evolving treatment options.

Although there are minor differences in the glucose targets recommended by the ADA and AACE/ACE (<7.0% and <6.5% for the ADA and AACE/ACE, respectively), both sets of guidelines emphasize early diagnosis and intensification of therapy to achieve and maintain control of A1C level.7,8 Treatment goals should be aligned with patient characteristics: more stringent A1C targets (eg, 6.0%-6.5%) might be considered in patients with long life expectancy, no significant risk of cardiovascular disease, and short-duration disease if an A1C target of less than 6.5% can be achieved without significant hypoglycemia or other adverse treatment effects.7,8 Conversely, less-stringent A1C goals (eg, 7.5%- 8.0%) are appropriate for patients with a history of severe hypoglycemia, short life expectancy, or extensive comorbid conditions, and those in whom the A1C target is difficult to attain.9 Progress toward the goal should be monitored every 2 to 3 months and therapeutic adjustments made to ensure achievement and/or maintenance of the desired glycemic response.7,8

Both sets of guidelines recommend intervention at the time of diagnosis with metformin in combination with lifestyle changes (eg, weight loss, exercise, diabetes selfmanagement education, and healthy dietary changes) and ongoing timely augmentation of therapy with additional agents as a means of achieving and maintaining target levels of glycemic control.7,8,10 If A1C targets are not achieved, treatment should be intensified by the addition of another agent from a different class.10 For example, if lifestyle changes and metformin monotherapy do not achieve/maintain glycemic control after approximately 3 months of treatment, the next step would be to add a second oral agent, a GLP-1 receptor agonist, or basal insulin. If no clinically meaningful glycemic reduction is demonstrated with the addition of the second drug, that agent should be discontinued and another with a different mechanism of action substituted.9 If hypoglycemia or weight gain are of concern, an incretin-related drug is recommended.10,11 Because there are insufficient data from long-term comparative effectiveness trials, uniform recommendations on the best agent to be combined with metformin cannot be made. Thus, advantages and disadvantages of specific drugs for each patient should be considered.9

The AACE/ACE and ADA algorithms are similar with the major exception that the AACE/ACE treatment recommendations are stratified by A1C levels.8 When A1C level is between 6.5% and 7.5%, the AACE/ACE generally recommends metformin monotherapy as first-line therapy, although therapies such as incretins and thiazolidinediones (TZDs) are recommended when necessary.8 For patients with A1C levels between 7.6% and 9.0%, dual therapy is the initial recommendation, with earlier addition of a GLP-1 receptor agonist and a TZD.8 For these patients, the sulfonylureas are introduced earlier because of concerns about hypoglycemia and/or weight gain.8 When A1C level exceeds 9.0%, or for symptomatic patients, immediate insulin therapy is recommended along with other agents.8

Managed Care Treatment Algorithms

To successfully impact treatment outcomes, treatment algorithms must be easy to use, serve as a quick reference, act as a shared voice within the health plan and its affiliated practice groups, and advocate classes of agents rather than specific products. Treatment recommendations contained within the algorithm must also be consistent with the pharmacology of the available agents, offer several therapeutic options, and provide follow-up guidelines. Most importantly, the algorithm must be successful in getting patients to goal. Step therapy and treatment algorithms are used in many managed care settings to encourage use of preferred therapies to control cost while also ensuring the delivery of highquality care. Step therapy utilizes an algorithm that requires first-line use of a medication(s) within the drug class (usually a generic) before receiving coverage for a second-line agent (usually a branded agent).12 Step therapy is promoted through the use of online claim edits, prior authorization, or implementation of approved guidelines.12 At the current time, most managed care algorithms position metformin as the primary firstline agent. In many cases, it is the only real step edit in place. Beyond first line, barriers to the other classes of drugs have mostly been removed.13 The development of many managed care diabetes treatment algorithms is driven not by the health plan, but by the medical group practices affiliated with the plan. The role of the health plan is to review patient outcomes resulting from use of the algorithm. Rather than looking at whether a patient is on or off a protocol, the focus of the plan administrators is on the clinical data. For example, plan administrators review data on adverse events, achievement of A1C targets, titration of the current therapy, timing of the implementation of a new therapy, introduction of multidrug therapy, and clinical laboratory data.13 Predictive modeling is used to analyze outcomes, with the results used to make adjustments in the clinical pathways and treatment plans. This process is relatively straightforward when the health plan and affiliated medical groups share an integrated electronic medical record system.

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