The value of remote monitoring systems for treatment of chronic disease

Caring for patients with chronic illnesses is costly—75% of U.S. healthcare spending can be attributed to treating chronic conditions (CDC, 2009a,b). Several components contribute to the cost of treating chronic disease. There are the direct costs associated with treating the disease, and those associated with complications that arise as a result of the disease. There are also indirect costs associated with loss of productivity and quality of life. Technological advances in remote monitoring systems (RMS) may provide a more cost-effective and less labor-intensive way to manage chronic disease by focusing on preventive measures and continuous monitoring instead of emergency care and hospital admissions. In this paper, we develop a model that estimates the total potential savings associated with broad introduction of RMS, and considers how capacity constraints and fairness concerns should impact RMS allocation to target populations. To illustrate the value and insight the model may provide, we conduct a small computational study that focuses on direct costs that would be real costs to a healthcare provider or payer for a subset of the most common chronic diseases: diabetes, heart failure, and hypertension. The computational study shows that, under reasonable assumptions, broad introduction of RMS will lead to substantial cost savings for target populations. The study provides proof of concept that the model could serve as a useful tool for policy makers, as it allows a decision maker to modify cost, risk, and capacity parameters to determine reasonable policies for the allocation of and reimbursement for RMS.