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

Jonathan A. Lal, Anil Vaidya, Iñaki Gutiérrez-Ibarluzea, Hans Peter Dauben, Angela Brand

Research output: Contribution to journalReview article

4 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)683-701
Number of pages19
JournalPersonalized Medicine
Volume10
Issue number7
DOIs
Publication statusPublished - 09-2013

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All Science Journal Classification (ASJC) codes

  • Molecular Medicine
  • Pharmacology
  • Medicine(all)

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