The biomedical discovery journey from laboratory to population often encompasses meandering paths, iterative cycles, replication and validation efforts, complex trade-offs between adaptability and fidelity, long lag times and often high costs. The path is sometimes described as a translational spectrum or continuum, with linear or serial-parallel segments and specific transitions. The starting point is usually represented by the inception, ideation or funding opportunity (T0), progressing to laboratory studies (T1), towards human specimen-based studies (T2) and clinic or hospital-based trials (T3), then to community (T4) and populations at large (T5). The latter translation is often accomplished through new policies, laws, regulations or societal normative changes. An innovation-friendly ecosystem requires powerful, effective, and efficient platforms to accelerate adoption of innovations and discoveries not only in academic organizations that generate new knowledge or provide cutting-edge education, but also in organizations trying to maintain their relevance and competitive advantage by embracing novelty, incremental or breakthrough discoveries, swiftly and efficiently. Local research and development hubs that provide nurture and upkeep of traditional, ‘secular’ innovation trees often must create de novo, ‘engineered’ trees to compensate for gaps in the investigative capability portfolio, and sometimes need to graft ‘engineered’ branches to create ‘hybrid’ trees, able to fulfill successfully and completely the discovery journey. This type of innovation orchard framework may be a powerful environment, ecosystem or milieu, regulating the growth and development of all these types of innovation trees, all with the purpose of agile functionality in implementing research and discovery in population health and clinical care.
Octavian C. Ioachimescu (Mon,) studied this question.