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, Inaki Gutierrez-Ibarluzea, Hans-Peter Dauben, Angela Brand

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

4 Citations (Web of Science)

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
JournalPersonalized Medicine
Volume10
Issue number7
DOIs
Publication statusPublished - Sept 2013

Keywords

  • health impact assessment
  • health needs assessment
  • health technology assessment
  • LAL model
  • life cycle
  • personalized healthcare
  • personalized medicine
  • public health genomics
  • technology transfer
  • translational research

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