Introduction
Nursing research is a pivotal driver of evidence-based practice since it contributes knowledge and practical insights to clinical settings. It allows for developing evidence-based, efficient, and safe solutions to clinical problems, prioritizing the reliability and credibility of research findings (White, 2019). However, it is essential to ensure that the research findings are relevant to practice and inform specifically the issues of critical importance. Moreover, once the solutions are proposed, they need to be tested for feasibility, which is the domain of translational and implementation science (Kraemer & Van Zutphen, 2019). This paper addresses the difference between translational science and implementation science to claim their essential role in shaping evidence-based practice.
Translational Science and Implementation Science
Both translational science and implementation science are the elements of medical science directed at improving the health care setting using evidence-based practice facilitation. The ultimate goal of these elements is to obtain maximum benefits for patients while utilizing organizational resources efficiently. Despite this similarity, these two types of nursing science have some differences. According to Kraemer and Van Zutphen (2019), translational science is a process of using factual knowledge obtained from clinical trials to methods and techniques that meet critical healthcare needs. Thus, translational science implies the coordination between the obtained research findings with their consecutive applicability to practice.
Unlike translational science, implementation science deals with applying the knowledge to practice via newly arranged interventions. It is defined as methods that encourage systematic usage of research findings into clinical practice in order to improve patient outcomes (Wensing & Grol, 2019). In other words, implementation science involves practical use of the knowledge transferred into the clinical setting via research. It relates to incorporating different stakeholders who enable the evidence to be integrated into clinical procedures.
In this regard, it is relevant to define the concept of knowledge translation. It is a process of transferring knowledge from the domain of implicitly known to the domain of explicitly known, thus making it available not only to separate individuals but to organizations and broader audiences (Jackson et al., 2020; White, 2019). Knowledge translation serves as an approach to disseminating knowledge and evidence across the involved stakeholders to drive change in the health care setting (Wensing & Grol, 2019). Thus, the overall difference between translation science and implementation science is that the former uses knowledge translation to disseminate the information and inform practical solutions. However, the latter is concerned with implementing the obtained evidence via interventions.
Importance of Translational and Implementation Science for the Health Care Setting
Both translational and implementation science are pivotal to the medical field due to the necessity of continuous improvement of care. Since these two types of science produce new knowledge and enable transforming evidence into practical solutions, their usefulness to the health care setting is difficult to overestimate (Jackson et al., 2020; White, 2019). Translational science and implementation science aim to find plausible and feasible solutions based on evidence. According to Brownson et al. (2017), implementation science can fill the implementation gap between the demand for a particular intervention and an offer within the scientific field. Thus, it is essential to utilize these two types of science in the nursing practice to contribute knowledge into practice and improve the overall quality of health care.
Translational Frameworks Applicable to the DNP Project
The DNP project is based on the attempt to investigate the influence of augmented reality tools’ use in open surgery on patient recovery effectiveness. The phenomenon that the DNP explores is post-surgery recovery, which is a multifaceted process of restoring healthy condition and functioning after an operative intrusion (Zhang et al., 2019). As a complex phenomenon, recovery is dependent on a variety of factors, one of which is the method of surgery (Zhang et al., 2019). Therefore, the use of computer-assisted surgery will be researched for its positive impact on recovery effectiveness.
In this regard, the most applicable translational frameworks would be the ones that address the coordination of multiple stakeholders while considering the technological premises of the issue at hand. Therefore, the two frameworks selected for the DNP project might be Roger’s Diffusion of Innovations Framework and Lomas’ Coordinated Implementation Framework (White, 2019). They allow for addressing the phenomenon of augmented reality at the center of the DNP project, which requires specific expertise for implementation. The selection of stakeholders is influenced by the phenomenon and should include organizational management, surgeons, technology specialists, and instructors.
Roger’s Diffusion of Innovations Model entails the introduction of an innovative solution to practice through diffusion. In its turn, diffusion means a social process that occurs among people in response to learning about an innovation such as a new evidence-based approach for extending or improving health care (Dearing & Cox, 2018). The framework integrates several levels of the research process for ultimate dissemination and exchange of evidence between purchasers, healthcare providers, organizations, patients, and policy-makers (White, 2019). As for Lomas’ Coordinated Implementation Framework, it entails a structured approach to organizing the research process, evidence dissemination, and implementation to the practice environment (White, 2019). It allows for conducting a multifaceted process of knowledge generation, testing, and improving before delivering care based on that knowledge to patients.
Conclusion
In summation, understanding the difference between translational and implementation science allows for adequate application of the relevant frameworks when solving particular clinical problems with the help of research. These two types of science complement each other and allow for generating new evidence, disseminating it across stakeholders, and implementing it via interventions. Within the proposed DNP project context, the use of Roger’s Diffusion of Innovations Framework and Lomas’ Coordinated Implementation Framework will be most applicable. They allow for integrating the technological aspect of the DNP with the multi-stakeholder nature of the project.
References
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Dearing, J. W., & Cox, J. G. (2018). Diffusion of innovations theory, principles, and practice. Health Affairs, 37(2).
Jackson, G. L., Cutrona, S. L., Kilbourne, A., White, B. S., Everett, C., & Damschroder, L. J. (2020). Implementation science: Helping healthcare systems improve. Journal of the American Academy of PAs, 33(1).
Kraemer, K., & Van Zutphen, K. G. (2019). Translational and implementation research to bridge evidence and implementation. Annals of Nutrition and Metabolism, 75(2).
Wensing, M., & Grol, R. (2019). Knowledge translation in health: how implementation science could contribute more. BMC Medicine, 17(1).
White, K. M. (2019). The science of translation and major frameworks. In Translation of evidence into nursing and healthcare. Springer Publishing.
Zhang, Y., Xin, Y., Sun, P., Cheng, D., Xu, M., Chen, J., Wang, J., & Jiang, J. (2019). Factors associated with failure of Enhanced Recovery After Surgery (ERAS) in colorectal and gastric surgery. Scandinavian Journal of Gastroenterology, 54(9).