Abstract
Worldwide, care programs for chronically ill are implemented without evidence that these programs make worthwhile use of scarce resources. Generally, these programs aim to improve quality of care and mitigate healthcare costs. They seek to identify chronic conditions more quickly, treat them more effectively and thereby slow the disease progression. This is pursued through a combination of
more effective team care and planned interactions;
self-management support;
integrated decision support;
electronic patient registries and other supportive information technology.
Although some positive results with regard to quality of care are shown, published evidence on cost-effectiveness remains largely inconclusive. Previous research also demonstrates that this uncertainty around cost-effectiveness of chronic care programs is not simply due to heterogeneity in programs. Rather, ignorance of the relation between healthcare structures, program-design and outcomes might be the cause. Hence, we hypothesized that the decision uncertainty around cost-effectiveness of chronic care programs decreases when parameters reflecting healthcare structure and program design are explicitly included in the economic evaluation of such programs. To test this hypothesis, the relation between structure parameters, program design and cost-effectiveness is investigated in a comparative international study including programs for adults with diabetes, heart failure, depression, asthma or Chronic Obstructive Pulmonary Disease. Subsequently, a decisionanalytic model is developed to estimate the short and long-term cost-effectiveness of programs for a given healthcare structure. Dependent on the remaining uncertainty around program cost-effectiveness, stakeholders can decide if these should be further implemented or that more information is required. Additionally, it is determined how the cost-effectiveness of programs can be maximized by adjustments in their design. With tools to better estimate the cost-effectiveness of chronic care programs in a given setting, and optimize their design accordingly, we will be able to improve care for the chronically ill in a systematic and cost-effective way. An outline of this advanced cost-effectiveness model and its potential application to case studies will be presented at the conference.
