Estimating the Economic Value of Amniotic Membrane as a Regenerative Therapy in Wound Care

Given that wounds present coalescing drivers of cost, including common medical etiologies, high complication rates, and high treatment costs, it would seem self-evident that any technology used to effectively treat wounds would be cost-effective. Emerging technologies struggle initially to gain a foothold in both adoption by clinicians and reimbursement by health plans/payers. An ability to define the evidence-based support on efficacy and safety of a product lies at the heart of the challenge.

The further assessment of economic impact by emerging technology, care/disease management strategy, or specific procedure is complicated but is also possible. The clinical application of amniotic membrane and its derivatives provides a great example of how such a technology has moved forward in both clinical and reimbursement domains. Economic valuation methods take different approaches, but an integrated method needs to address hard-dollar savings, soft-dollar savings, and quality-of-life or other intangible impacts. Our purpose is to review a multi-domain approach to the economic analysis of wound care products in general, and specifically examine determinants of economic value of amniotic membrane as a regenerative treatment for diabetic and venous ulcers.

Evaluation of data from multiple randomized controlled trials (RCTs) show that amniotic membrane products have financial advantages through lower product cost than composite synthetic skin substitutes, lower wastage through various sized allografts, improved time to healing, and fewer needed clinic visits. Operational advantages are also identified related to product storage characteristics and ease of use. In addition to offering a solid argument for both clinical efficacy and safety, the studies can also reflect added cost-effective value of the product.

The RCTs provide core information on healing kinetics of amniotic membrane and on overall costs within the trial and provide additional statistically useful information for creating complex models that further estimate broader cost savings.