Abstract
Background: Recently, there has been a substantial increase in relevant genome-based technologies into market. Compared with its utilization in healthcare systems, we notice a huge gap. In order to address this bottleneck, we previously developed the Learning-Adapting-Leveling (LAL) model. Aim: In this article, we aim to demonstrate the overarching reach of the model for translation to market and implementation into healthcare systems moving towards personalized healthcare. Methods: We use qualitative logical reasoning with the LAL model as a reference. Results: We found that technology transfer, health needs assessment, health technology assessment and health impact assessment are justified for their inclusion. In addition, the public health wheel is justified as a good reference frame along with value of information. Conclusion: We conclude that as the LAL model covers all dimensions and tools for translation and implementation in a defined method; it can therefore be considered as the overarching framework for translation and implementation into healthcare.
Original language | English |
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Pages (from-to) | 41-62 |
Number of pages | 22 |
Journal | Personalized Medicine |
Volume | 11 |
Issue number | 1 |
DOIs | |
Publication status | Published - 01-2014 |
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All Science Journal Classification (ASJC) codes
- Medicine(all)
- Molecular Medicine
- Pharmacology
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The overarching framework of translation and integration into healthcare : A case for the LAL model. / Lal, Jonathan A.; Morré, Servaas A.; Brand, Angela.
In: Personalized Medicine, Vol. 11, No. 1, 01.2014, p. 41-62.Research output: Contribution to journal › Article
TY - JOUR
T1 - The overarching framework of translation and integration into healthcare
T2 - A case for the LAL model
AU - Lal, Jonathan A.
AU - Morré, Servaas A.
AU - Brand, Angela
PY - 2014/1
Y1 - 2014/1
N2 - Background: Recently, there has been a substantial increase in relevant genome-based technologies into market. Compared with its utilization in healthcare systems, we notice a huge gap. In order to address this bottleneck, we previously developed the Learning-Adapting-Leveling (LAL) model. Aim: In this article, we aim to demonstrate the overarching reach of the model for translation to market and implementation into healthcare systems moving towards personalized healthcare. Methods: We use qualitative logical reasoning with the LAL model as a reference. Results: We found that technology transfer, health needs assessment, health technology assessment and health impact assessment are justified for their inclusion. In addition, the public health wheel is justified as a good reference frame along with value of information. Conclusion: We conclude that as the LAL model covers all dimensions and tools for translation and implementation in a defined method; it can therefore be considered as the overarching framework for translation and implementation into healthcare.
AB - Background: Recently, there has been a substantial increase in relevant genome-based technologies into market. Compared with its utilization in healthcare systems, we notice a huge gap. In order to address this bottleneck, we previously developed the Learning-Adapting-Leveling (LAL) model. Aim: In this article, we aim to demonstrate the overarching reach of the model for translation to market and implementation into healthcare systems moving towards personalized healthcare. Methods: We use qualitative logical reasoning with the LAL model as a reference. Results: We found that technology transfer, health needs assessment, health technology assessment and health impact assessment are justified for their inclusion. In addition, the public health wheel is justified as a good reference frame along with value of information. Conclusion: We conclude that as the LAL model covers all dimensions and tools for translation and implementation in a defined method; it can therefore be considered as the overarching framework for translation and implementation into healthcare.
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U2 - 10.2217/pme.13.97
DO - 10.2217/pme.13.97
M3 - Article
AN - SCOPUS:84893853118
VL - 11
SP - 41
EP - 62
JO - Personalized Medicine
JF - Personalized Medicine
SN - 1741-0541
IS - 1
ER -