The advent of the digital revolution has contributed to the unprecedented wealth of scientific information currently produced in the biomedical field, providing avenues for the development of novel treatments, improved understanding of disease processes and their associated risk factors, as well as the formulation of effective prevention methods at the individual, community, and societal level. However, the accumulation of data, and the speed with which new data are generated, often results in the eclipse of relevant information of great value to the biomedical field.1
During the past decade, the foregoing observations emphasized the importance of identifying methods through which knowledge transfer between the scientific research community and physicians, healthcare providers, and other stakeholders is optimized to inform decisions about overall health, health care, and health policy.2 The term ”knowledge transfer” has been described as the process of exchanging and distributing knowledge in an organized and accessible manner, wherein ”knowledge transfer” acts as a necessary, although insufficient, component of successful ”knowledge translation” – more specifically, the successful dissemination of biomedical research into the clinical cognizance and the implementation of this knowledge to improve patient health outcomes.1, 3
Currently, 3 main sources for the transfer of specific biomedical information exist: medical journals, continuing medical education, and pharmaceutical representatives.1 However, these traditional forms of media are proving incapable of meeting the requirements of modern developments in knowledge creation, sharing, and capture.4 Consequently, investments in research endeavors are limited by the ineffectual communication of scientific knowledge and the gap between what is known about disparate health conditions and what is done to prevent and/or treat them.
This gap in the successful translation of knowledge from bench to bedside is consistently reported in clinical and health service research, indicating that the significant lag between advances in biomedical research and the elimination of preventable risks and iatrogenic complications divest patients of optimal treatment while simultaneously contributing to unnecessary healthcare system expenditures.1-3 Taken together, the scientific community’s clarion call for an effective means of navigating the mounting biomedical research that eliminates the constraints of integrating novel research findings into clinical practice requires the following: 1) targeted dissemination of relevant content to intended audiences; 2) exposure to contemporary research and their latest developments; 3) eliminating the time required to identify relevant research content; and 4) increasing the likelihood of content translation and accessibility to the general public.1-3
In response to the interminable accumulation of scholarly publications and the foregoing constraints, a Canadian start-up company, TrendMD (www.trendmd.com), developed a dissemination tool for scholarly content that effectively organizes and tailors this information to the appropriate audience by distributing and recommending scholarly content based on its audience’s reading habits, click behavior, and social media trends to cross-promote relevant material within and across journals/publishers as well as between separate fields of study.5, 6 TrendMD’s algorithmic approach to distributing peer-reviewed content also provides opportunities for researchers to not only amplify the impact of their research on clinical practices in their respective field, but also promote interdisciplinary innovation by expanding its readership to researchers in other fields with relevant interests and complementary objectives.5
This recommendation widget is free for publishers to install and is placed at the end of articles published on their website, allowing the widget to endorse links to related scholarly content within journals associated with the same publisher or a completely separate journal supported by a separate publisher. An example outlining the manner in which TrendMD’s recommendation widget functions is described step-wise below:6
- An article is published on novel treatments for bipolar depression in the British Medical Journal and the content providers are interested in promoting their work. A budget is set, and the article is disseminated using TrendMD’s current content provider network (upwards of 2500 journals and other scholarly sites).
- Presently, you are reading a separate article on bipolar depression in JAMA which is identified as a current content provider in TrendMD’s network by having its widget installed on their website and TrendMD recommends the content recently published in the British Medical Journal.
- The recommendation is successful in engaging your research interests and you click the link – an action that results in a cost deduction from the British Medical Journal‘s budget for content promotion via this content discovery platform and the proceeds are split between JAMA and TrendMD while the British Medical Journal adds site traffic and increases content exposure.
To date, TrendMD’s content recommendation widget has been installed across a network of upwards of 2500 science, medical, and technical journals and blogs, generating approximately 500 million scholarly recommendations to 30 million readers per month according to physician-entrepreneur co-founder, Paul Kudlow, MD.7
Technological innovations such as TrendMD capitalize on the fundamental shift in content exchange by drawing on social software that effectively personalizes individual health professionals’ needs in a less costly, cloud-based, and content-directed manner in a ubiquitous digital environment that is ever-changing. The introduction of TrendMD to the biomedical research landscape represents a new frontier for the actionable transfer of knowledge in pursuit of successful knowledge translation from biomedical research to clinical cognizance and real-world application to optimize patient health outcomes.1, 2
- Boissel JP, Amsallem E, Cucherat M, Nony P, Haugh MC. Bridging the gap between therapeutic research results and physician prescribing decisions: knowledge transfer, a prerequisite to knowledge translation. Eur J Clin Pharmacol. 2004;60(9):609-616.
- Lavis JN, Robertson D, Woodside JM, McLeod CB, Abelson J. How can research organizations more effectively transfer research knowledge to decision makers? Milbank Q. 2003;81(2):221-222.
- Grimshaw JM, Eccles MP, Lavis JN, Hill SJ, Squires JE. Knowledge translation of research findings. Implement Sci. 2012;7:50.
- von Krogh G. How does social software change knowledge management? Toward a strategic research agenda. J Strategic Information Sys. 2012;21:154-164.
- Cha DS. Close-up with Dr Paul Kudlow and TrendMD. IMS Magazine. March 19, 2015. Available at: http://www.imsmagazine.com/close-up-with-dr-paul-kudlow-and-trendmd/. Accessed April 22, 2016.
- Hall J. U of T student-entrepreneur cuts through scholarly information overload with TrendMD. University of Toronto U of T News. January 28, 2015. Available at: http://news.utoronto.ca/u-t-student-entrepreneur-cuts-through-scholarly-information-overload-trend-md. Accessed April 22, 2016.
- The Canadian Press. TrendMD designed to help medical research get read. CBC News . November 9, 2014. Available at: http://www.cbc.ca/news/canada/toronto/trendmd-designed-to-help-medical-research-get-read-1.2829378. Accessed April 22, 2016.