One of the greatest challenges in biomedical and health research is ensuring that research findings are translated effectively and without undue delay from ‘bench to bedside’. As previous analysis has made clear, the time that elapses between discoveries in medical research and adoption in practice is important. Longer time lags mean a lower rate of return on the research investment, which makes it less attractive to do the research in the first place. Longer time lags also mean patients lose out during the delay, but some lags may be beneficial, for example, to ensure effectiveness and safety.
The length of the total ‘gap’ between start of research and the result entering into practice is uncertain. Some research suggests 17 years is typical, but how gaps are defined and measured differs significantly across approaches, producing results that are hard to compare.
The UK Medical Research Council (MRC) has awarded a grant jointly to OHE, Brunel University’s Heath Economics Research Group (HERG) and RAND Europe for a project intended to fit analyses of lags into a common framework. OHE and its partners will use a case study approach that develops the process marker model proposed by Trochim and colleagues. This approach uses multiple specific milestones or events as process markers, which are clearly defined to enable comparability. The current project will attempt to identify dates for each marker to allow time lags to be calculated and assessed.
The project will be based on six case studies in the fields of cardiovascular and mental health research, which were the focus of the team’s earlier study on economic returns from medical research. Included will be 'backward'- and 'forward'-tracing case studies. The former begin with the application of a new therapy or procedure and identify the research behind the innovation; the latter start with specific research and follow developments to when a new practice is adopted. The project will cover a range of types of medical interventions – for example, a new drug, a service delivery such as behavioural therapy, or a screening programme for specific diseases. Both the markers and the primary factors responsible for time lags are likely to differ across types of interventions.
If the method proves successful, additional research could produce more in-depth analyses of the sources of time lags that would suggest, for example, whether lags are desirable (a delay that increases safety, for example, may well be desirable) and how they differ across types of intervention. This would allow the development of evidence-based policy recommendations, building on preliminary policy-relevant findings from the pilot project.
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