This dissertation is motivated by the demand to address spiraling healthcare costs, in particular the costs of bringing new medical treatments to patients as quickly as possible. In three parts, we investigate the relationship between the process of learning about the performance of new treatments and the economic concerns of the companies that produce these treatments and the healthcare payers that reimburse these companies. Each part focuses on a different portion of the medical treatment development process. The first part proposes an approach to clinical trial design that integrates important, emerging trends intended to improve trial designs and the health value for money of treatments that they select: design for cost-effectiveness, which ensures health-economic improvement of a new treatment over the current standard of care; adaptive design, which dynamically adjusts the sample size and allocation of patients to different treatments; and multi-arm trial design, which compares multiple treatments simultaneously. We identify a sequential sampling policy that dynamically decides the treatments to which patients should be allocated, as well as when to stop patient recruitment. We develop the first tractable allocation and stopping rules that model both correlation and dynamic stopping times. The second part of the dissertation studies a family of price functions that can be used in risk-sharing contracts between companies and healthcare payers, in which a treatment’s price is a function of data captured after the treatment has entered the market. An understanding of the equilibrium outcomes for different types of agreements is valuable to payers as they decide which contract forms to offer. Our insights are grouped according to the type of the treatment: small-molecules and biologics. The third part of the dissertation examines the impact of healthcare payer's choice of reimbursement policy on the time spent testing new medical treatments, with the goal of shortening the time needed for a new treatment to reach patients. We find that the use of value-based pricing can extend the length of clinical trials. The use of post-marketing price update together with value-based pricing may slightly reduce the optimal trial length.