ObjectiveTo evaluate the reliability and validity of Guideline Implementation Success Assessment Tool (A-GIST). MethodsWith the guideline for the prevention and treatment of type 2 diabetes mellitus in China (2020 edition) as the target guideline, health care providers and patients from different hospitals across the country were investigated by questionnaire using A-GIST. Spearman-Brown coefficient and Cronbach's α coefficient were used to evaluate the split-half reliability and internal consistency reliability, while the structural validity, convergent validity and discriminant validity were investigated by confirmatory factor analysis based on structural equation. ResultsThe internal consistency reliability and split half reliability coefficients of the whole tool and each dimension ranged from 0.650 to 0.986. The scale-level content validity index (S-CVI/Ave) of content validity was 0.846. Confirmatory factor analysis showed that, the χ2/df of two sections of the tool were 8.695 and 6.123, respectively. The root mean square residual (RMR), the standard root mean square residual (SRMR) and the root mean square error of approximation (RMSEA) were under or almost under the threshold. Besides, the goodness-of-fit index (GFI) of them were 0.901 and 0.822, the adjusted goodness-of-fit index (AGFI) were 0.836 and 0.787, and the parsimonious normed fit index (PNFI) were 0.545 and 0.788, respectively. ConclusionGuideline Implementation Success Assessment Tool (A-GIST) was proved to be valid and reliable, and it shows that it is necessary to optimize the items under the dimensions of maintenance and evaluation of diagnosis and treatment effect in the future.
With the encouragement of national policy on drug and medical device innovation, multi-center clinical trials and multi-regional clinical trials are facing an unprecedented opportunity in China. Trials with a multi-center design are far more common at present than before. However, it should be recognized there still exists shortcomings in current multi-center trials. In this paper, we summarize the problems and challenges and provide corresponding resolutions with the aim to reduce heterogeneity between study centers and avoid excessive center effects in treatment. It is urgent to develop design, implementation and reporting guidelines to improve the overall quality of multi-center clinical trials.
Evidence-based evidence translated into health care services requires three conversion processes, preclinical research-clinical research-implementation research, which might take a very long time. In order to speed up the process of research results translated into daily medical practice and health policy, an effectiveness-implementation hybrid design combining the effectiveness research of clinical and implementation research was emerging. This paper introduced the concept, classification and application of effectiveness-implementation hybrid design, to provide references for researchers in this area.
Implementation science is a new field that aims to scale up and sustain the application of evidence-based practice. It primarily studies the dissemination, adoption, evaluation, and improvement, etc. of the implementation process of evidence-based practice. In recent years, tens of implementation theories, frameworks and models have been developed. We systematically collected 41 of them and classified them into several categories. The first is the classical theories which are extended from the traditional social science and used as the predecessors of the implementation science. The second category is the implementation science frameworks/models that are derived from the first category, and can be divided into four groups: 1) the implementation models that explain the law of a certain aspect in the implementation; 2) the implementation determinants frameworks that summarize and classify the implementation determinants; 3) the process models that discuss how research translates into practice; 4) the evaluation models that evaluate the implementation and outcome. The implementation science theories can provide implementation researchers with valuable guidance for development of implementation strategies and evaluation of implementation outcomes.
The RE-AIM framework is the most commonly used framework for implementation research outcome evaluation, with the original paper cited 2 800 times. However, in the specific operationalization process of the framework, there are often situations of misuse or overuse of various dimensions and indicators. This article provides a detailed introduction to the main content, application scenarios, and characteristics of the RE-AIM framework. Taking the implementation research of Evidence-based Practice Guidelines for Integrated Traditional Chinese and Western Medicine Treatment of Stroke as an example, it introduces how to use the silent & anonymous feedback technology, which combines the improved Delphi method and nominal group technique, to construct an implementation outcome indicator evaluation system to achieve the operability of the RE-AIM framework. This will provide a reference for other researchers to use implementation science theories, models, and frameworks to develop implementation plans or evaluate implementation outcomes.
Implementation science is a relatively emergent and growing research area. Implementation research can assist to transform what is possible in theory to reality in practice and address the challenge of implementing proven interventions in the real world. Implementation research has a wide range of usages and complex research problems, so appropriate research methods, designs, and outcomes variables are required to address different research objectives. To better conduct implementation research, this paper systematically introduces the research designs, outcome variables, and reporting guideline of the implementation research in health care, based on the purposes and research questions of implementation research.
Effectiveness-implementation hybrid designs can test the effectiveness of interventions and the outcomes of implementation strategies concurrently and accelerate the transformation of research results into routine practice. This paper introduced three types of effectiveness-implementation hybrid designs and corresponding reporting guidelines, including standards for reporting implementation studies, cluster randomized design and CONSORT 2010, stepped-wedge cluster randomized design and extended version CONSORT 2018, qualitative research and COREQ reporting guideline, and provide references for domestic researchers to produce research reports on effectiveness-implementation hybrid design.
As evidence-based practice (EBP) continues to be valued, the guideline implementation has become an important field for research and practice in health care. In order to better guide the guideline implementation in the field of health care, this paper combines the EBP experience gained from several EBP programs carried out in our hospital those years, in aim of exploring and sharing the methodology of guideline implementation in domestic clinical settings.
ObjectiveTo analyze the problems in the construction of internal control in colleges and universities, and to explore the implementation path suitable for colleges and universities. MethodsThe relevant documents on the internal control construction of universities at home and abroad were retrieved, and the case study of the implementation path of the internal control system construction of C university was analyzed by using the methods of expert interviews, flow charts and walk-through tests. ResultsThe internal control system of colleges and universities takes digital intelligence as the technical support to explore the implementation path of "external standard rule riveting point, internal standard post entry process; data standardization, process progress visualization; speed of operation tracking, data backtracking to find deviation; business data is all smooth, performance evaluation to promote management", optimize the process and improve the system, real-time extraction of operation data for key projects to track, and shorten the business operation time by more than half. ConclusionThe case university carries out empirical research on the internal control system, based on the principle of effective implementation, makes full use of the digital intelligence technology to extract the whole process data of the business system across the system, and realizes the efficient collaboration, early warning and evaluation, and scientific decision-making of the "digital intelligence" platform.
Objective The ultimate goal of developing guidelines is for using them in clinical practice. In this study, an implementation evaluation tool was developed to promote the overall evaluation of guidelines and to improve their promotion and implementation. Methods The research group set up a team to formulate and establish a guideline implementation evaluation tool, through preliminary research, interviews, a systematic review of relevant literature, two expert consensus meetings and two Delphi expert consensus meetings to evaluate the guideline implementation tool. Experts were invited to give opinions and grades on the fields, items and overall implementation evaluation method of the tool. Results The evaluation tool for the implementation of guidelines included 5 fields, accessibility, communicability, performability, recognizability and applicability, with a total of 7 items. The scale-level CVIs in two rounds of Delphi expert consensus were 0.91 and 0.93. We collected opinions and suggestions and made some revisions and insertions without deleting any items based on the parameter that no items fulfilled the standard if mean <3.5, coefficient of variation >15% and I-CVI<0.78. Conclusion In this study, in order to provide a standard and method for the evaluation of guideline implementation, a guideline implementation evaluation tool has been developed and evaluated by clinically-related physicians and guideline formulation methodology experts. The guideline implementation evaluation tool presents satisfactory face and content validity. Empirical research is needed to verify the tool’s performance in evaluating guideline implementation.