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.

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

Acknowledgements

The authors would like to extend their thanks to the research school GROW of the Faculty of Health, Medicine and Life Sciences of Maastricht University for supporting this research.

Financial & competing interests disclosure

This project is supported by a grant from the European Commission PHGEN II (duration period: June 2009–November 2012 EU-Project No. 20081302). This work is also supported by the Canadian Institutes for Health Research (CIHR), CIHR Institute of Genetics, the CIHR Institute of Health Services and Policy Research through a grant (No. ETG92250) to the APOGEE-Net/CanGèneTest Research and Knowledge Network on Genetic Services and Policy. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Notes

CLV: Customer lifetime value; CVM: Customer value management; ICT: Information and communication technology.

Data taken from [25], with the exception of Question 4.

Additional information

Funding

This project is supported by a grant from the European Commission PHGEN II (duration period: June 2009–November 2012 EU-Project No. 20081302). This work is also supported by the Canadian Institutes for Health Research (CIHR), CIHR Institute of Genetics, the CIHR Institute of Health Services and Policy Research through a grant (No. ETG92250) to the APOGEE-Net/CanGèneTest Research and Knowledge Network on Genetic Services and Policy. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.