New findings
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Key words used to retrieve relevant studies included ‘valley of death’, ‘bench to bedside’, ‘translational research’ and ‘commercialisation’. Our search was not intended to be exhaustive, but included key policy documents and searches of Google Scholar, Web of Science, PubMed and EBSCO. This leads to a discussion on the current state of understanding about time lags and considers the implications for future practice and policy.įor the first part of the study we identified literature that described conceptual models of translation. The second part of the paper presents a review of the literature on time lags to present current estimates and issues.
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The first part of the paper explores conceptual models of the translation pipeline in order to provide context. The aim was to overlay empirical lag data onto the conceptual model of translational research to provide an overview of estimated time lags and where they occur. It would also provide the potential for evaluating the cost-effectiveness of translation interventions if their impact on lags can be measured. This would allow efforts to reduce lags to be focused on areas of particular concern or value, or on areas where interventions might be expected to have best effect.
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To help address this gap, this paper aims to synthesize existing knowledge and to offer a conceptual model that can be used to standardize measurement and thus help to quantify lags in future. 13ĭespite longstanding concerns about delays in getting research into practice, the literature on time lags seems surprisingly under-developed. Such convergence around an ‘average’ time lag of 17 years hides complexities that are relevant to policy and practice which would benefit from greater understanding. 1, 3, 15 Balas and Bohen, 16 Grant 17 and Wratschko 18 all estimated a time lag of 17 years measuring different points of the process. It is frequently stated that it takes an average of 17 years for research evidence to reach clinical practice. What is notable is that all the above calculations depended upon an estimated time lag estimated because, despite longstanding concerns about them, 14 time lags in health research are little understood. Varying the lag time from 10 to 25 years produced rates of return of 13% and 6%, respectively, illustrating that shortening the lag between bench and bedside improves the overall benefit of cardiovascular research. (The remaining 30% arise from ‘spillovers’ benefiting the wider economy.) This level of benefit was calculated using an estimated lag of 17 years. Of this, 9% was attributable to the benefit from health improvements, which is the focus of this paper. 13 In other words, a £1.00 investment in public/charitable CVD research produced a stream of benefits equivalent to earning £0.39 per year in perpetuity. One recent study (of which JG and SW were co-authors) estimating the economic benefit of cardiovascular disease (CVD) research in the UK between 19, found an internal rate of return (IRR) of CVD research of 39%. 12 Although some lag will be necessary to ensure the safety and efficacy of new interventions or advances, in essence we should aim to optimize lags. Delays are seen as a waste of scarce resources and a sacrifice of potential patient benefit. 3– 11īoth literature and policy tend to assume that speedy translation of research into practice is a good thing. 1, 2 Policy interventions to improve translation respond to a vast empirical literature on the difficulties of getting research across research phases and into practice. Timely realization of the benefits of expensive medical research is an international concern attracting considerable policy effort around ‘translation’.