Dataset Information

Encouraging the scale-up of proven interventions: Infrastructure development for the "Evidence-to-Implementation" award.

ABSTRACT:

Background/objective

Although most research universities offer investigators help in obtaining patents for inventions, investigators generally have few resources for scaling up non-patentable innovations, such as health behavior change interventions. In 2017, the dissemination and implementation (D & I) team at the University of Wisconsin's Clinical and Translational Science Award (CTSA) created the Evidence-to-Implementation (E2I) award to encourage the scale-up of proven, non-patentable health interventions. The award was intended to give investigators financial support and business expertise to prepare evidence-based interventions for scale-up.

Methods

The D & I team adapted a set of criteria named Critical Factors Assessment, which has proven effective in predicting the success of entrepreneurial ventures outside the health care environment, to use as review criteria for the program. In March 2018 and February 2020, multidisciplinary panels assessed proposals using a review process loosely based on the one used by the NIH for grant proposals, replacing the traditional NIH scoring criteria with the eight predictive factors included in Critical Factors Assessment.

Results

two applications in 2018 and three applications in 2020 earned awards. Funding has ended for the first two awardees, and both innovations have advanced successfully.

Conclusion

Late-stage translation, though often overlooked by the academic community, is essential to maximizing the overall impact of the science generated by CTSAs. The Evidence-to-implementation award provides a working model for supporting late-stage translation within a CTSA environment.

SUBMITTER: Quanbeck A

PROVIDER: S-EPMC8427544 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BACKGROUND:Interventions that work must be effectively delivered at scale to achieve population level benefits. Researchers must choose among a vast array of implementation frameworks (>?60) that guide design and evaluation of implementation and scale-up processes. Therefore, we sought to recommend conceptual frameworks that can be used to design, inform, and evaluate implementation of physical activity (PA) and nutrition interventions at different stages of the program life cycle. We also sought to recommend a minimum data set of implementation outcome and determinant variables (indicators) as well as measures and tools deemed most relevant for PA and nutrition researchers. METHODS:We adopted a five-round modified Delphi methodology. For rounds 1, 2, and 3 we administered online surveys to PA and nutrition implementation scientists to generate a rank order list of most commonly used; i) implementation and scale-up frameworks, ii) implementation indicators, and iii) implementation and scale-up measures and tools. Measures and tools were excluded after round 2 as input from participants was very limited. For rounds 4 and 5, we conducted two in-person meetings with an expert group to create a shortlist of implementation and scale-up frameworks, identify a minimum data set of indicators and to discuss application and relevance of frameworks and indicators to the field of PA and nutrition. RESULTS:The two most commonly referenced implementation frameworks were the Framework for Effective Implementation and the Consolidated Framework for Implementation Research. We provide the 25 most highly ranked implementation indicators reported by those who participated in rounds 1-3 of the survey. From these, the expert group created a recommended minimum data set of implementation determinants (n?=?10) and implementation outcomes (n?=?5) and reconciled differences in commonly used terms and definitions. CONCLUSIONS:Researchers are confronted with myriad options when conducting implementation and scale-up evaluations. Thus, we identified and prioritized a list of frameworks and a minimum data set of indicators that have potential to improve the quality and consistency of evaluating implementation and scale-up of PA and nutrition interventions. Advancing our science is predicated upon increased efforts to develop a common 'language' and adaptable measures and tools.

Project description:BACKGROUND:Few efficacious physical activity interventions are successfully translated and sustained in practice. We propose a practical guide for researchers to increase the likelihood of successful implementation and scale up of physical activity interventions in practice contexts. The guide is based on two principles: (i) differences between the research and practice context can be addressed during intervention development and implementation planning by focusing on system, delivery personnel, and intervention characteristics; and (ii) early planning for implementation barriers and facilitators can improve subsequent translation into practice. METHODS:From the published literature, we identified evidence of strategies to improve research-practice translation, along with narrative descriptions of different approaches to addressing translational challenges. These, along with constructs taken from widely cited implementation outcome, process, and mechanistic models were collated and inform the guide. RESULTS:The resultant PRACTIS guide (PRACTical planning for Implementation and Scale-up) comprised the following four iterative steps: Step 1) Characterize the parameters of the implementation setting; Step 2) Identify and engage key stakeholders across multiple levels within the delivery system(s); Step 3) Identify contextual barriers and facilitators to implementation, and; Step 4) Address potential barriers to effective implementation. CONCLUSIONS:A lack of practical guidance for researchers on how to effectively plan implementation and scale up of physical activity interventions prevents us moving quickly from evidence to action. We recommend that intervention development and adaptation for broad and sustained implementation be prioritized early in intervention planning and include active engagement from delivery organizations and stakeholders. The PRACTIS guide is also relevant for clinical and public health researchers in other areas of prevention.

Project description:BACKGROUND:The objectives of South Africa's electronic health (eHealth) strategy recognize the value proposition that telemedicine practices hold for rural and urban referrals, but a lack of accepted and formalized scale-up has impeded realization of benefits. While both synchronous and asynchronous teledermatology exist, these remain localized and not scaled-up. Skin pathology is often the first sign of an HIV/AIDS infection, which remains a major cause of morbidity and mortality in South Africa. It is essential to replace the current inefficient dermatology referral process with a swift, organized, and efficacious one. OBJECTIVE:The objective of this study is to present an evidenced-based teledermatology scale-up framework (TDSF) and implementation roadmap (TDSF-IR). METHODS:A qualitative method with a design science research process model was used which consisted of 5 phases: (1) Awareness, which confirmed the need for an evidence-based TDSF and supporting TDSF-IR; (2) Suggestion, where a proposal was delivered on how to develop a TDSF and TDSF-IR; (3) Development, where we identified recommended design requirements and used these to identify and critique existing teledermatology or related scale-up frameworks; (4) Evaluation and validation, where we assessed outputs of the development phase against the design requirements and validated by confirming the veracity of the TDSF and TDSF-IR (validation involved 4 key senior teledermatology stakeholders using a questionnaire with a 5-point Likert scale); and (5) Conclusion, where validation results were used to finalize and communicate the TDSF and TDSF-IR to users. RESULTS:The study identified 5 TDSF components: eHealth building blocks, eHealth strategic objectives and budget, scale-up continuum periods, scale-up drivers, and scale-up phases. In addition, 36 subcomponents were identified. Each was further characterized and described to enable design of the final evidence-based TDSF. An implementation roadmap (TDSF-IR) was also prepared as a guide for an implementer with step-by-step instructions for application of the TDSF. For the validation study of the TDSF and supporting TDSF-IR, 4 purposively selected key senior teledermatology management stakeholders were asked if they found it useful as a guide to assist the South African public health system with teledermatology scale-up. The mean (SD) of Likert-scale rating was 4.0 (0.53) where 4=Agree and 33 of 36 responses were either agree or strongly agree. CONCLUSIONS:This study developed a TDSF and supporting roadmap (TDSF-IR) that are evidence-based. The proposed approach and described tools could be adapted to assist with ensuring scale-up and sustainability for other eHealth practices in other locations.

Project description:Breast cancer exhibits significant molecular, pathological, and clinical heterogeneity. Current clinicopathological evaluation is imperfect for predicting outcome, which results in overtreatment for many patients, and for others, leads to death from recurrent disease. Therefore, additional criteria are needed to better personalize care and maximize treatment effectiveness and survival. To address these challenges, the Sweden Cancerome Analysis Network - Breast (SCAN-B) consortium was initiated in 2010 as a multicenter prospective study with longsighted aims to 1) analyze breast cancers with next-generation genomic technologies for translational research in a population-based manner and integrated with healthcare; 2) decipher fundamental tumor biology from these analyses; 3) utilize genomic data to develop and validate new clinically-actionable biomarker assays; and 4) build the infrastructure for real-time clinical implementation of molecular diagnostic, prognostic, and predictive tests. In the first phase, we focus on molecular profiling by next-generation RNA-sequencing on the Illumina platform. In the three years from August 30, 2010 through August 31, 2013, we have consented and enrolled 3,979 patients with primary breast cancer at the seven hospital sites in South Sweden, representing approximately 85% of eligible patients in the catchment area. Pre-operative blood samples have been collected for 3,942 (99%) patients and primary tumor specimens collected for 2,929 (74%) patients. Herein we describe the study infrastructure and present initial proof of concept results from prospective RNA-sequencing including tumor molecular subtyping and detection of driver gene mutations. We demonstrate that large-scale population-based collection and RNA-sequencing analysis of breast cancer is feasible. The SCAN-B Initiative should significantly reduce the time to discovery, validation, and clinical implementation of novel molecular diagnostic and predictive tests. We welcome the participation of additional comprehensive cancer treatment centers.