Decisions in healthcare often have to do with the optimal allocation of scarce resources under conditions of uncertainty. To maximize the value that can be obtained from the available resources, healthcare decisions can be supported by decision-analytic models. These models provide an analytic framework to synthesize data from various sources to quantify the consequences of a decision regarding both health outcomes and resource utilization. Although decision-analytic models are considered the most suitable approach to inform decision-making in healthcare, they tend to be complex and cumbersome to review. Therefore, these models are sometimes referred to a “black-box”, a weakness of the field, which begs for more transparency. Scripts from open-source programming languages, such as R or Python, have the potential to increase transparency. However, no clear guidance for the implementation of decision-analytic models in open-source languages exists. This thesis aimed to develop a structured programming method that is generic, uses an open-source programming language, and can be applied to a wide range of topics. To demonstrate how open-source decision-analytic models can be used for resource prioritization we used two COVID-19 related decision problems. One topic related to prioritization of semielective surgical procedures. The other focused on treatment implementation and research prioritization decisions regarding emerging COVID-19 therapies. The open-source programming language used in this thesis is R. Nevertheless, the developed methodological modeling approaches can be generalized to other open-source programming languages. This thesis has shown that R can be used to develop transparent decision-analytic models for healthcare decision making. We have also demonstrated that R-based decision-analytic models can be used for a dynamic value-based allocation of scarce resources, which is especially relevant in times of rapidly changing conditions, such as a pandemic.