“You devoted an entire issue to draw attention to the fact that if you make people pay more for something they will buy less of it?”
—Lyn Beamesderfer, Editorial Director
Although projections foretelling the financial collapse of our healthcare system are customarily published in journals such as The American Journal of Managed Care, we must not forget that the goal of healthcare expenditures is to improve health, not save money. Substantial savings could be achieved, at least in the short run, if we stopped providing health insurance, an immensely popular benefit that provides access to care individuals otherwise could not afford. Yet, low out-of-pocket costs at the point of service, a common feature of many plans, leads consumers to use services whose clinical benefits do not justify the cost.1
One common approach to combat excess consumption specifically, and healthcare cost growth in general, is increased patient cost sharing. The successful use of cost sharing presumes that individuals have access to information on cost and quality, and respond appropriately to prices of medical interventions (questionable assumptions when applied to healthcare). Several studies, including those by Landon and colleagues2 and Gilman and Kautter3 in this theme issue of the Journal, demonstrate that costs are effectively reduced under scenarios of increased cost sharing. The extent of adverse clinical effects secondary to decreased use of services remains controversial; reports by Brixner and colleagues4 and Kephart et al5 make important contributions to this issue.
Most copayment systems and formulary arrangements have uniform copay rates that do not differ by type of service or by patient group. This “across the board” system fails to acknowledge the heterogeneity that exists within clinical care because medical services differ in the level of clinical benefit they provide. Further, the clinical benefit derived from a specific medical service likely varies (whether it be considered high or low value), depending on the patient population using it. In theory, equal copayments applied to all services would discourage utilization of low-value care only. A growing body of evidence suggests that patients do not distinguish between high-value and low-value therapies when faced with higher prices.6,7 In fact, the studies by Mager and Cox8 and Zeber et al9 demonstrate that a copay increase of just a few dollars has a marked impact on prescription fill rates.
Bringing attention to the utilization effects of higher copays was our goal long before we became Co—Editors-in-Chief of the Journal. In 2001, we introduced the benefit-based copay,10 a design that lowered cost sharing for high-value services to mitigate the adverse health consequences of out-of-pocket expenditures. We were certain (and naïve) that stakeholders would embrace a benefit design which would improve the amount of health achieved per dollar spent. While the latest iteration of the concept, value-based insurance design (VBID), has gained momentum and has been implemented by forward-thinking employers nationwide,11-14 it took us nearly a decade to realize that controlling healthcare cost growth is a more pressing problem than shortcomings in healthcare quality. The marketplace was loud and clear: The financial implications of VBID must be known before widespread implementation would occur.
Despite this “call to arms,” it remains difficult to provide a routine answer regarding the bottom-line effects of VBID, because there is no single VBID intervention. From an aggregate cost perspective (employer plus employee), a portion of costs associated with a VBID program includes the expenditures on additional high-value services used because of lower copays.12 Added to these expenditures on “incremental” users, the employer must incur the additional share of copayments of the value services that would have been utilized anyway. Thus, in a VBID implementation that only provides copay relief, the employer expenditures will often exceed the aggregate costs, since employees’ savings in lower copayments are borne as expenses by the employer.
The net costs of the VBID program depends critically on whether the incremental expenditures on high-value services can be offset through a decrease in adverse events as a result of increased utilization. These savings will be enhanced if the VBID services are targeted to specific patients at high risk of a preventable adverse event (eg, lower β-blocker copayments only for patients with congestive heart failure and post—myocardial infarction). Some reports, based on uncontrolled analysis of VBID programs, suggest that these savings are large enough to offset the extra employer spending.15 However, controlled studies suggest the health benefits are not enough to finance the entire investment in lower copayments.16
Improved health due to increased use of highly valued interventions also generates a second potential source of savings: improved employee productivity and lower disability rates. Although common, these savings are difficult to quantify and attribute to specific medical services. In one study, Nicholson17 estimated absentee costs for workers with diabetes mellitus to be approximately $1000 annually; their estimates for presenteeism costs are 6 times more than that of absenteeism. If these predictions are accurate, and if improved adherence to high-value services can reduce these costs, savings to employers can be substantial.
Despite efforts to adopt highly valued interventions, it comes as no surprise that payers are unlikely to implement programs that increase spending—even if they are on high-value services—without precise estimates of financial impact. Until empirical analyses better quantify the resultant medical cost offsets and productivity gains of additional spending on high-value services, an obvious way to fund these programs is to increase cost sharing for less-valued services.
Goldman et al18 used a cost-sharing approach in a simulation model developed to assess the impact of increased statin adherence among patients at increased risk for cardiac adverse events. The cost of reduced copays for those patients at highest risk for a cardiac event was paid for by copay increases in lower risk statin users, a patient group where this medication is of moderate value. Instead of limiting the financing to a singledrug class or specific clinical diagnosis, we advocate distributing the costs over a wide array of services, thereby minimizing the copay increase for any particular service. The more interventions that share the cost of copay reductions, the smaller increase in copayment necessary to fund the subsidy.
Although a designation of high-, moderate-, and low-value services is likely to ignite considerable debate,19 there are numerous services already identified by disease management programs, pay-for-performance initiatives, and health plan accrediting organizations, such as the National Committee for Quality Assurance. Admittedly, the imperfection in identifying VBID services will result in increasing the costs for certain high-value services not identified immediately. Clearly, investments in comparative effectiveness research20 and information technology21 will allow consumers access to more unbiased information on quality and cost of care that may preclude the need for a “soft paternalistic” approach to guide individuals toward high-value care. But information technology and comparative effectiveness research are merely tools, and the ultimate impact of these tools on population health depends on how they are used. As the evidence base expands, information technology progresses, and our understanding of how consumers respond to clinical and financial data improves, VBID plans that are both fiscally responsible and clinically sensitive will help to reduce cost growth and preserve quality of care.
Author Affiliations: From the Departments of Internal Medicine and Health Management and Policy, University of Michigan, Ann Arbor, Mich (AMF); the Department of Health Care Policy, Harvard Medical School, Boston, Mass (MEC).
Correspondence Author: A. Mark Fendrick, MD, University of Michigan, 300 North Ingalls Bldg, Rm 7E06, Ann Arbor, MI 48109-0429. E-mail: firstname.lastname@example.org.
Author Disclosure: The authors (AMF, MEC) are consultants for ActiveHealth Management, New York, NY.
Authorship Information: Concept and design, drafting of the manuscript (AMF, MEC); critical revision of the manuscript for important intellectual content (AMF, MEC); administrative, technical, or logical support (AMF, MEC).
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