Programmable Interface for Statistical Simulation Models (PRISM): Towards Greater Accessibility of Clinical and Healthcare Decision Models
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Background: Increasingly, decision-making in healthcare relies on computer models, be it clinical prediction models at point of care or decision-analytic models at the policymaking level. Given the important role models play in both contexts, their structure and implementation be rigorously scrutinized. The ability to interrogate input/output associations without facing barriers can improve quality assurance mechanisms while satisfying privacy/confidentiality concerns and facilitating the integration of models into decision-making. This paper reports on the development of Programmable Interface for Statistical Simulation Models (PRISM), a cloud-based platform for model accessibility. Methods: PRISM emphasizes two main principles: 1) minimal specifications on the side of model developer to make the model fit for cloud hosting, and 2) making client access completely independent of the resource requirement and software dependencies of the model. The server architecture integrates a RESTful Application Programming Interface (API) infrastructure, JSON for data transfer, a routing layer for access management, container technology for management of computer resources and package dependencies, and the capacity for synchronous or asynchronous model calls. Results: We discuss the architecture, the minimal API standards that enable a universal language for access to such models, the underlying server infrastructure, and the standards used for data transfer. An instance of PRISM is available as a service via the Peer Models Network http://peermodelsnetwork.com. Through a series of case studies, we demonstrate how interrogating models becomes possible in standardized fashion, in a way that is irrespective of the specifics of any model. Conclusions: We have developed a publicly accessible platform and minimalist standards that facilitate model accessibility for both clinical and policy models.
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