Public health and valorization of genome-based technologies: a new model

Abstract

BackgroundThe success rate of timely translation of genome-based technologies to commercially feasible products/services with applicability in health care systems is significantly low. We identified both industry and scientists neglect health policy aspects when commercializing their technology, more specifically, Public Health Assessment Tools (PHAT) and early on involvement of decision makers through which market authorization and reimbursements are dependent. While Technology Transfer (TT) aims to facilitate translation of ideas into products, Health Technology Assessment, one component of PHAT, for example, facilitates translation of products/processes into healthcare services and eventually comes up with recommendations for decision makers. We aim to propose a new model of valorization to optimize integration of genome-based technologies into the healthcare system.MethodsThe method used to develop our model is an adapted version of the Fish Trap Model and the Basic Design Cycle.ResultsWe found although different, similarities exist between TT and PHAT. Realizing the potential of being mutually beneficial justified our proposal of their relative parallel initiation. We observed that the Public Health Genomics Wheel should be included in this relative parallel activity to ensure all societal/policy aspects are dealt with preemptively by both stakeholders. On further analysis, we found out this whole process is dependent on the Value of Information. As a result, we present our LAL (Learning Adapting Leveling) model which proposes, based on market demand; TT and PHAT by consultation/bi-lateral communication should advocate for relevant technologies. This can be achieved by public-private partnerships (PPPs). These widely defined PPPs create the innovation network which is a developing, consultative/collaborative-networking platform between TT and PHAT. This network has iterations and requires learning, assimilating and using knowledge developed and is called absorption capacity. We hypothesize that the higher absorption capacity, higher success possibility. Our model however does not address the phasing out of technology although we believe the same model can be used to simultaneously phase out a technology.ConclusionsThis model proposes to facilitate optimization/decrease the timeframe of integration in healthcare. It also helps industry and researchers to come to a strategic decision at an early stage, about technology being developed thus, saving on resources, hence minimizing failures.

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“…Based on these recommendations, a technique, technology, tool, or process is implemented in the policy of healthcare, and these policy decisions determine the acceptability, the applicability in healthcare and the reimbursements. This in our observations has been generally neglected [52] by the academic-industrial complex [64]. Therefore, it is of uttermost importance to take into consideration the HTA process while developing a tool, diagnostic kit or technology, thus, in order to efficiently, effectively and in a timely manner introduce an innovation into the healthcare system.…”

“…Health policy is generally developed through evidence-based interventions and around general public health instruments, such as Health Technology Assessment (HTA) among others [60,61]. We use the term Public Health Assessment Tools (PHAT) [52] when we refer collectively to Health Needs Assessment, HTA and Health Impact Assessment. Health Needs Assessment is a systematic method of reviewing the health issues facing a population, leading to agreed upon priorities and resource allocation that will improve health and reduce inequalities [61].…”

“…more stratified) that may even become ‘truly' personalized in the near future with the incorporation information derived from dynamic fields of investigation such as systems biomedicine and epigenomics [51]. The major obstacle to implementation is not the CE (Conformtie Europeene) IVD or FDA approval of diagnostic application, but originates with the healthcare integration [52] and policy embedment processes.…”

“…The Learning-Adapting-Leveling (LAL) model [18] is a model of translating scientific data from the lab through the market and implementing it into public health policy; it will be discussed later in this review as a possible way to assimilate new findings in clinical settings.…”

“…In order to minimize failure, it is important that researchers take into account the policy aspect and the acceptance of diagnostic applications in the healthcare systems [52] itself, instead of just considering the market.…”