Implementation science (IS) aims to systematically analyze and address the real-world gaps from evidence to practice and the influencing factors of the context. It is necessary to carry out qualitative research to gather relevant implementation outcomes. Nevertheless, traditional qualitative analysis has issues such as consuming a great deal of time and energy, and it is unable to promptly provide the crucial data required for implementation science research. The Rapid Qualitative Analysis (RQA) method, through semi-structured interviews and the adoption of techniques such as immediate data condensation and matrix analysis, can effectively shorten the cycle of qualitative data collection and data processing. RQA can promptly identify social determinants of health such as structural barriers, facilitators, and the behavioral characteristics of target groups. It provides a real-time basis for public health decision-making, the interpretation of complex social phenomena, and the process and effectiveness evaluation of research projects. Although RQA is difficult to conduct in-depth theoretical analysis based on grounded theory, its efficiency and flexibility make it the preferred tool for large-scale and time-sensitive research. Thus, it has been widely applied in implementation science research. This paper sorts out the core concepts and commonly used technical methods of RQA, as well as the differences between RQA and traditional qualitative analysis. It also explores the applications of RQA in intervention optimization, process evaluation, and implementation outcome evaluation. By integrating specific cases, this paper clarifies its application value in the field of implementation science. In the future, it is advisable to explore the integration of RQA with technologies such as artificial intelligence and big data, in order to bridge the gap between the transformation of scientific research achievements into practice. Under circumstances of limited resources or tight time constraints, RQA can be used to efficiently conduct implementation science research, providing convenient and scientific methodological and technical support for accelerating evidence-based practice.

Prediabetes is a condition where blood glucose levels have deviated from the normal range but have not yet reached the diagnostic criteria for diabetes. It represents a transitional phase between health and diabetes. Currently, there are no global guidelines for managing prediabetes. However, the use of metformin in prediabetes is largely based on clinical experience, and there is a lack of high-quality evidence-based medicine. There are uncertainties regarding its dosage and unknown adverse reactions, and its exact efficacy and safety still require further study. This article systematically reviews the current state of metformin in the treatment of prediabetic, analyzing differences in efficacy across different dosages, the guideline recommendations, clinical application experiences, potential adverse effects and irrational drug use. We aim to provide scientific basis and clinical practice guidance for the rational use of metformin in the management of prediabetes.