The Learning-Adapting-Leveling model: from theory to hypothesis of steps for implementation of basic genome-based evidence in personalized medicine

Abstract

We see a backlog in the effective and efficient integration of personalized medicine applications such as genome-based information and technologies into healthcare systems. This article aims to expand on the steps of a published innovative model, which addresses the bottleneck of real-time integration into healthcare. We present a deconstruction of the Learning-Adapting-Leveling model to simplify the steps. We found out that throughout the technology transfer pipeline, contacts, assessments and adaptations/feedback loops are made with health needs assessment, health technology assessment and health impact assessment professionals in the same order by the academic–industrial complex, resulting in early-on involvement of all stakeholders. We conclude that the model steps can be used to resolve the bottleneck of implementation of personalized medicine application into healthcare systems.