OBJECTIVES: This study examines KRAS mutational analysis in metastatic colorectal cancer as an example of a successfully adopted personalized medicine technology.  Objectives are to describe factors involved in the adoption of KRAS mutational analysis and to examine relationships between factors over time.  METHODS: We used a framework called the Evaluation Data for Assessing Personalized Medicine’s Translation (EDAPT) to collect and organize data on KRAS mutational analysis.  EDAPT focuses on collecting data on clinical application, economic, policy and regulatory issues using structured reviews of academic and gray literature.  We used Innovation-Decision Theory to analyze how factors emerged over time and relationships between factors. RESULTS: Initial studies suggesting association between KRAS status and drug efficacy led to industry-supported retrospective analyses of RCT data. After assessing evidence via systematic review, National Comprehensive Cancer Network and American Society of Clinical Oncology signaled acceptance of KRAS mutational analysis by issuing clinical guidelines, thereby making KRAS mutational analysis standard of care.  The clinical guidelines were influential in implementation of payer coverage policies and discussions at the FDA about retrospective data.     We detect a pattern: 1) Retrospective analyses of RCT data increased knowledge of the technology among stakeholders;  2) A systematic review of evidence concluded that evidence surrounding the technology was robust;  3) Persuaded by the review’s conclusion, provider organizations decided to accept the technology and communicated this decision through clinical guidelines;  4) Clinical guidelines influenced payer policies, which framed the implementation of the technology into health care.  Provider organization decisions swayed regulatory agents’ recognition of these retrospective analyses, thereby confirming acceptance of the technology.  CONCLUSIONS: This example illustrates how evidence of clinical utility and provider organization support can drive rapid adoption.  We propose an application of the Innovation-Decision Theory that may help to anticipate future trends in personalized medicine.