Mixed results from studies of disease management programs in general indicate there is no conclusive evidence of the impact of the programs on cost-effectiveness and health outcomes. According to the Congressional Budget Office’s 2004 analysis of disease management studies for several chronic diseases, there is insufficient evidence to conclude that disease management programs can generally reduce overall health spending. A more recent review of the disease management literature found there is some evidence of improved clinical outcomes for certain conditions (congestive heart failure, coronary artery disease, and depression) – and little or no evidence that disease management improves outcomes for asthma and chronic obstructive pulmonary disease (Chen & Au, 2008). In addition, the review found that, compared to evidence of improved clinical outcomes, there is even less evidence that disease management programs produce savings. It seems that the potential for disease management to control costs might depend on whether the populations targeted for intervention have conditions that leave little room for overall improvement in their health status. Depending on the conditions a population has – for example, frail, elderly patients with co-occurring illnesses like heart disease, lung disease, and diabetes – cost savings could result from reductions in high-cost acute care episodes, but their need for physician visits or costly prescription medications may not be affected by DM intervention. Even where there is limited return on investment or cost savings resulting from DM, the potential for improving long-term clinical outcomes may be a reason to pursue DM and care coordination approaches.
Disease management interventions are sometimes referred to as patient self-management, care coordination, and care management. Among the problems that have been identified as major challenges to evaluation of the impact of disease management programs is the lack of a standardized taxonomy of mechanisms used in managing patients with complex conditions. DM programs are heterogeneous. Lack of standardization among DM providers regarding what DM entails makes it difficult to compare effectiveness across providers. In addition, the short-term nature of many studies cannot measure benefits that may take years to materialize (Luck et al., 2007). When the start-up costs of a program and the costs of the number of staff involved in providing DM and care coordination services are considered, the prospects for cost savings may be low in the short term. There might also be costs from greater utilization of primary care services, specialty care (depending on comorbidities), and pharmaceuticals (or increased adherence to medication regimens) that add to program costs (Billings & Mijanovich, 2007). For example, Indiana’s Chronic Disease Management Program for Medicaid enrollees with diabetes and congestive heart failure used two interventions – an intensive nurse care management program and a telephonic program – both of which included patient education. An analysis of Indiana’s program results, incorporating programmatic costs, found that the largest savings to Medicaid were for the low-risk enrollees offered the telephonic intervention (Holmes et al., 2008). However, the analysis covered only a 21-month follow-up period and, therefore, could not account for any possible longer term program impacts that might result among the high-risk enrollees. In theory, over the long term, there might be potential for cost savings – or alternatively, reduced growth in the cost of care – due to reduced hospitalizations or other complications associated with the chronic illnesses.
Disease management programs typically focus on managing an individual chronic illness. Many DM programs focus on single diseases and exclude people with multiple chronic illnesses, so there has been little opportunity to conduct research on the health and cost impacts of DM for individuals with multiple chronic conditions, and this is a challenge to developing effective, targeted clinical management approaches for patients with comorbidities (Vogeli et al., 2007). Due to demographic trends and the increasing number of individuals with chronic illnesses that may lead to costly hospitalizations, it is likely that states will continue to explore disease management as a way to address rising health care costs for Medicaid, especially for patients with multiple chronic conditions.
For populations enrolled in Medicaid or Medicare, the prevalence of multiple chronic conditions makes disease management complex. The impact of DM may depend on the characteristics of the population receiving the intervention. An evaluation of the Medicare Health Support programs indicated that care management interventions among beneficiaries with chronic conditions did not result in savings for Medicare (Chen et al., 2008). The Medicare Health Support programs targeted heart disease and diabetes; other Medicare disease management demonstrations included patients with cancer and chronic obstructive pulmonary disease and did not significantly improve clinical outcomes or result in cost savings.