Personalized medicine (PM) approaches have revolutionized healthcare delivery by offering new insights that enable healthcare providers to select the optimal treatment approach for their patients. However, despite the consensus that these approaches have significant value, implementation across the US is highly variable. In order to address barriers to widespread PM adoption, a comprehensive and methodical approach to assessing the current level of PM integration within a given organization and the broader healthcare system is needed. A quantitative framework encompassing a multifactorial approach to assessing PM adoption has been developed and used to generate a rating of PM integration in 153 organizations across the US. The results suggest significant heterogeneity in adoption levels but also some consistent themes in what defines a high-performing organization, including the sophistication of data collected, data sharing practices, and the level of internal funding committed to supporting PM initiatives. A longitudinal approach to data collection will be valuable to track continued progress and adapt to new challenges and barriers to PM adoption as they arise.
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http://dx.doi.org/10.3390/jpm11030196 | DOI Listing |
BMC Med Res Methodol
January 2025
Systems Engineering & Operations Research, George Mason University, Fairfax, VA, 22030, USA.
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View Article and Find Full Text PDFCommun Med (Lond)
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International Research Center for Neurointelligence, The University of Tokyo, Tokyo, Japan.
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View Article and Find Full Text PDFSci Rep
January 2025
Department of Civil and Environmental Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
To enhance sustainability and resilience against climate change in infrastructure, a quantitative evaluation of both environmental impact and cost is important within a life cycle framework. Climate change effects can lead performance deterioration in bridge components during their operational phase, highlighting the necessity for a risk-based evaluation process aligned with maintenance strategies. This study employs a two-phase life cycle assessments (LCA) framework.
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View Article and Find Full Text PDFNat Commun
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Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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