Information is essential to delivering effective healthcare. Health information traditionally resides with the entity providing healthcare to the patient. However, this is just one model for how information can be situated within a healthcare context. Decentralized health networks provide the infrastructure to realize a different information and computational model that centers the locus of health information around the patients. In this paper, we present a framework for building and testing decentralized health networks that enable patients to share their data with a communal network to gain value derived from aggregated data. While decentralized networks disperse control of health information among a network of individuals, they require careful design and testing to ensure the network’s incentives promote the interests of each member, scale cost-effectively, and protect privacy. Deploying a decentralized health network without appropriate testing presents multiple risks, including the risk of data breach, erroneous machine learning model predictions, and costly platform management. To counter these risks, we demonstrate how multi-agent-based evaluation can be central to testing and refining patient-centered decentralized health networks such that the incentives are robust, sustainable, and less susceptible to exploitation. We find that agent-based approaches can help speed up development and build confidence in scalability, safety, and effective functioning prior to real-world deployment. We also provide design recommendations and suggestions for future work.