It takes 17 years to change healthcare

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Despite longstanding concerns about delays in getting research into practice, the literature on time lags seems surprisingly under-developed. 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. 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.

It would also provide the potential for evaluating the cost-effectiveness of translation interventions if their impact on lags can be measured. 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.

The first part of the paper explores conceptual models of the translation pipeline in order to provide context. The second part of the paper presents a review of the literature on time lags to present current estimates and issues. This leads to a discussion on the current state of understanding about time lags and considers the implications for future practice and policy.

For the first part of the study we identified literature that described conceptual models of translation. The models in the literature found by these methods were summarized into a simple conceptual model. For the second part of the study we reviewed the literature on time lags in health research. We found a formal search yielded few relevant papers so combined a number of approaches to increase our confidence that relevant papers had been identified.

We undertook backward and forward citation tracking to identify related work and used searches within targeted journals — e. Scientometrics and Journal of Translational Medicine. To analyse the lag data, we used a data extraction template with the following fields: start and end dates for measurement period, range, mean, median, dates used, topic, country of study.

In addition, the start point and endpoint of the time lag measured in each study were mapped onto specific stages in the conceptual model developed in the first part of the study. Understanding time lags requires a conceptual model of how research in science is converted to patient benefit so that the durations of activities and waits can be measured.

We have attempted to synthesize these models to identify key features of the translation process and to offer a tentative unified model. This was intended to help stakeholders agree a model which could be used to support future data gathering and better guide policy-making. We recognized that drug development, public health, devices and broader aspects of healthcare practice will vary in nature.

The translation process is summarized briefly in Figure 1. Clearly this model can be critiqued for being linear and we acknowledge the considerable literature that challenges this notion and accept that research translation is a messy, iterative and complex process see Balaconi et al. At the same time, we would argue that for the purposes of understanding and conceptualising time lags the model is appropriate in showing common steps found in the literature. A conceptual model of the journey of health biomedical research from research into benefit, as derived from the literature.

Some of these activities are repeated in different phases — grants and publications most particularly. Each activity involves a lag, either because the effort required for carrying out the task or as a result of non-value adding waits. Each of these is also associated with delays, although precisely what and where these gaps are, and how long they are, is again not consistent in the literature.

Policy measures to expedite the translation process typically focus on these gaps. Table 1 shows a summary of estimates derived from empirical studies of lags. Figure 2 shows these time-lag estimates by research phase. Chart showing the approximate range and average time lag reported in studies of time lags in health research.

As is shown in Table 1 , studies of time lags in translation of research to practice often measure different points in the process. For example, Decullier et al. Not surprisingly given they are measuring different lags, Figure 2 helps show that data are generally sparse and estimates vary. Measurement and reporting is often poor.

Ranges — or even interquartile ranges as large as years 38 — are seldom reported. Furthermore, where it was possible, further investigation of the average revealed wide variation; variation which is not highlighted or discussed in the papers. For example, Hopewell et al. Comparing the slowest negative publication with the fastest positive publication makes a potential difference of four years — half of the maximum lag. Some studies aggregate data from earlier studies without critical reflection or recognition of this.

Not surprisingly, studies also show variation in time lags by domain 38 and even intervention within a single domain. For example, examining research relating to advances in neonatal care, Grant et al. Atman et al. Content also appears to influence time lags. A common theme found in the literature concerns publication bias, and their implications for judging effectiveness. Studies also show that time lags are not stable over time. For example, Pulido noted a difference of 0.

Single papers raise issues that are not generally discussed but do seem relevant to measuring time lags from publications in particular. This paper aimed to synthesize existing knowledge to offer a conceptual model that can be used to standardize measurement and thus help to quantify lags in future.

The strengths of the study are that, to our knowledge, this provides the first attempt to review lags comprehensively, both in terms of using multiple approaches to find studies, but also in attempting to quantify time lags along the translation continuum. The review exposed a number of weaknesses in the literature and gaps in knowledge, which are not often discussed.

Despite our attempts to be comprehensive, however, we are aware that studies of time lags in health research are widely distributed and not easily identified using formal literature searches and we may have failed to capture relevant studies. We struggled to find research quantifying lags in basic research and the first translation gap in particular.

Our aim to understand lags has been limited by the weaknesses of existing data. Limitations of the literature examined include the use of proxy measures. Much of the literature on lags focuses on dissemination and publication in peer-review journals in particular as these are the most measureable. If there are significant lags in, say, the grant or ethics process, this is less likely to be reflected in current total lag estimations.

Moreover, the variation in choice of proxy measures means that studies are almost never measuring the same thing, making valid aggregation and generalization difficult. There is a clear trend in the literature to seek a single answer to a single question through the calculation of an average.

The variation found in the literature suggests that this is not possible or even desirable , and variation matters. Thus any poor estimates are transferred forward into later analysis, and also hide a complexity which is highly relevant to research policy. There also appears to be a mismatch between conceptual models of the translation process, and the measuring of lags.

For example, the gap between guideline publication and translation into actual practice is often ignored, suggesting an under-estimation of the time lags in some cases. On the other hand, interventions may come into use before guidelines outlining them have been published — suggesting an over-estimation of time lags in other cases.

Using different endpoints, different domains and different approaches, Balas and Bohen 16 and Grant et al. Wratschko also suggested 17 years as the highest limit for the time taken from drug discovery to commercialization. Is this coincidence or not? One possible reason for the convergence is the difficulty of measuring longer lags — because of limitations of citation indexes, other records and recollections — which provides a ceiling to such estimates and leads to a convergence of average lags.

While not able to adequately quantify time lags in health research, this study provides lessons for future research policy and practice.

Concerns about lags are not new 14 but are unresolved. Based on the review, and our own work on lags, 13 , 17 , 19 , 30 , 51 we would argue that an essential step to being able to quantify time lags, and thereby make improvements, requires stakeholders to agree definitions, key stages and measures. It also perhaps requires stakeholders to develop a more nuanced understanding of when time lags are good or bad, linked to policy choices around ethics and governance for example, 52 or reflect workforce issues.

It seems to us that this provides an excellent framework to support future data gathering and analysis and thus provide a more informed base from which to develop policy to address time lags.

No attention is given to understanding distributions and variations. As noted in the introduction, some lags are necessary to ensure the safety and efficacy of implementing new research into practice. The discussion in the literature fails to consider what is necessary or desirable, tending to assume that all lags are unwelcome. A key question for policy is to identify which lags are beneficial and which are unnecessary, but to answer this question it is necessary to have an accurate and comparable estimate of the lags.

Translating scientific discoveries into patient benefit more quickly is a policy priority of many health research systems. Despite their policy salience, little is known about time lags and how they should be managed. This lack of knowledge puts those responsible for enabling translational research at a disadvantage.

An ambitious reason for being able to accurately measure lags is that it would be possible to look at their distribution to identify research that is both slow and fast in its translation. Further investigation of the characteristics of research at both ends of a distribution could help identify actionable policy interventions that could speed up the translation process, where appropriate, and thus increase the return on research investment. The views expressed are not necessarily those of the Department.

ZSM designed, conducted and analysed the literature review, and drafted and revised the paper; JG initiated the project, drafted and revised the paper, and has led a number of studies cited that attempted to measure lags; SW revised the paper. The authors thank NIHR for their support.

J R Soc Med. Author information Copyright and License information Disclaimer. Correspondence to: Jonathan Grant. Email: gro. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. See editorial " Knowledge, lost in translation " in volume on page Abstract This study aimed to review the literature describing and quantifying time lags in the health research translation process.

Methods For the first part of the study we identified literature that described conceptual models of translation. Findings Conceptualising translational research Understanding time lags requires a conceptual model of how research in science is converted to patient benefit so that the durations of activities and waits can be measured.

Open in a separate window. Figure 1. Estimating time lags in the translation process Table 1 shows a summary of estimates derived from empirical studies of lags. Table 1 Summary of studies of time lags in health research. With some expectations, most of this lag is generated after a trial has been completed. A weekly update of the most important issues driving the global agenda. You can unsubscribe at any time using the link in our emails. For more details, review our privacy policy. Our Impact.

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