The Assessment of Technology Adoption Interventions and Outcome Achievement Related to the Use of a Clinical Research Data Warehouse

Introduction: While funding for research has declined since 2004, the need for rapid, innovative, and lifesaving clinical and translational research has never been greater due to the rise in chronic health conditions, which have resulted in lower life expectancy and higher rates of mortality and adverse outcomes. Finding effective diagnostic and treatment methods to address the complex challenges in individual and population health will require a team science approach, creating the need for multidisciplinary collaboration among practitioners and researchers. To address this need, the National Institutes of Health (NIH) created the Clinical and Translational Science Awards (CTSA) program. The CTSA program distributes funds to a national network of medical research institutions, known as “hubs,” that work together to improve the translational research process. With this funding, each hub is required to achieve specific goals to support clinical and translational research teams by providing a variety of services, including cutting edge use of informatics technologies. As a result, the majority of CTSA recipients have implemented and maintain data warehouses, which combine disparate data types from a range of clinical and administrative sources, include data from multiple institutions, and support a variety of workflows. These data warehouses provide comprehensive sets of data that extend beyond the contents of a single EHR system and provide more valuable information for translational research. Although significant research has been conducted related to this technology, gaps exist regarding research team adoption of data warehouses. As a result, more information is needed to understand how data warehouses are adopted and what outcomes are achieved when using them. Specifically, this study focuses on three gaps: research team awareness of data warehouses, the outcomes of data warehouse training for research teams, and how to measure objectively outcomes achieved after training. By assessing and measuring data warehouse use, this study aims to provide a greater understanding of data warehouse adoption and the outcomes achieved. With this understanding, the most effective and efficient development, implementation, and maintenance strategies can be used to increase the return on investment for these resource-intensive technologies. In addition, technologies can be better designed to ensure they are meeting the needs of clinical and translational science in the 21st century and beyond. Methods: During the study period, presentations were held to raise awareness of data warehouse technology. In addition, training sessions were provided that focused on the use of data warehouses for research projects. To assess the impact of the presentations and training sessions, pre- and post-assessments gauged knowledge and likelihood to use the technology. As objective measurements, the number of data warehouse access and training requests were obtained, and audit trails were reviewed to assess trainee activities within the data warehouse. Finally, trainees completed a 30-day post-training assessment to provide information about barriers and benefits of the technology. Results: Key study findings suggest that the awareness presentations and training were successful in increasing research team knowledge of data warehouses and likelihood to use this technology, but did not result in a subsequent increase in access or training requests within the study period. In addition, 24% of trainees completed the associated data warehouse activities to achieve their intended outcomes within 30 days of training. The time needed for adopting the technology, the ease of use of data warehouses, the types of support available, and the data available within the data warehouse may all be factors influencing this completion rate. Conclusion: The key finding of this study is that data warehouse awareness presentations and training sessions are insufficient to result in research team adoption of the technology within a three-month study period. Several important implications can be drawn from this finding. First, the timeline for technology adoption requires further investigation, although it is likely longer than 90 days. Future assessments of technology adoption should include an individual’s timeline for pursuing the use of that technology. Second, this study provided a definition for outcome achievement, which was completion o

CREATe 2012-2016: Impact on society, industry and policy through research excellence and knowledge exchange

On the eve of the CREATe Festival May 2016, the Centre published this legacy report (edited by Kerry Patterson & Sukhpreet Singh with contributions from consortium researchers)

Innovative models for collaboration and student mobility in Europe

This report is based on new developments in higher education and international collaboration as collected by EADTU's Task Force and Peer Learning Activity on Virtual Mobility. The result is a report on three types of collaboration mobility: physical, blended and online. Main parameters for innovative education and mobility formats are defined as well as basic principles of international course and curriculum design. Examples illustrate the complete opportunity space between fully face to face and fully online collaboration. They relate to mobility within single courses, exchange mobility (classical Erasmus), networked programmes and mobility windows and joint programmes with embedded mobility. Mobility offers opportunities to institutions to strengthen their programmes and to students to enrich their study. They benefit from an international learning experience or following courses not provided by their own institution. The report shows concrete mobility schemes used in the membership (and beyond). It underpins policies for international networking and delivers tools to organise innovative education and mobility formats

Assessment, Usability, and Sociocultural Impacts of DataONE

DataONE, funded from 2009-2019 by the U.S. National Science Foundation, is an early example of a large-scale project that built both a cyberinfrastructure and culture of data discovery, sharing, and reuse. DataONE used a Working Group model, where a diverse group of participants collaborated on targeted research and development activities to achieve broader project goals. This article summarizes the work carried out by two of DataONE’s working groups: Usability & Assessment (2009-2019) and Sociocultural Issues (2009-2014). The activities of these working groups provide a unique longitudinal look at how scientists, librarians, and other key stakeholders engaged in convergence research to identify and analyze practices around research data management through the development of boundary objects, an iterative assessment program, and reflection. Members of the working groups disseminated their findings widely in papers, presentations, and datasets, reaching international audiences through publications in 25 different journals and presentations to over 5,000 people at interdisciplinary venues. The working groups helped inform the DataONE cyberinfrastructure and influenced the evolving data management landscape. By studying working groups over time, the paper also presents lessons learned about the working group model for global large-scale projects that bring together participants from multiple disciplines and communities in convergence research

Method Usefulness for Quality Improvement in Care

Complexity in care arises from several concurrent sources, such as siloed care organisations and complex care processes handled by several medical specialities. Due to factors including the ongoing development of personalised care and increasingly older populations suffering from multi-sickness, care complexity can only be expected to increase. Simultaneously, organisational efficiency needs to be increased alongside this growing complexity. To address these challenges, there is a need to understand care complexity in order to drive care improvement.Quality improvement (QI) aims to develop health and social care. Methods are central for QI by describing the care and thereby support i) planning for future care, ii) acquisition of knowledge and understanding of the current practice, and iii) prediction of the future of care from historical data. Methods for QI generally display data in a simple, graphic way, so that they are easy for practitioners to understand; however, this strong focus on simplicity may limit the understanding of care complexity and thereby reduce the support provided for QI. As QI research with a focus on methods describing care complexity is scarce, the purpose of this thesis is to explore the usefulness of methods describing care complexity for QI in care.To fulfil this purpose, two research questions guided the analysis of the five appended papers. The first research question (What usefulness can visual methods describing care process complexity have for QI?) addresses the need to identify new methods describing care complexity where current methods are lacking. Two methods are chosen, guided by visual analytics theory: Lexis diagram and process mining. Two case studies and a literature review explore the usefulness of Lexis diagrams and process mining through visualisation of process variations at a patient and a population level, across groups and over time. The second research question (What usefulness can methods describing care organisation complexity have for QI in public procurement?) expands and explores the use of current methods describing complexity into the public care procurement context. First, the current state of QI in public care procurement is explored through an archival study, and next, a case study is conducted to explore the use of business excellence models to support QI in public care procurement. The thesis is guided by a pragmatic approach, leading to a mixed-methods approach and domain expert collaboration.This thesis makes three main contributions. First, each method’s properties are connected to a set of evaluative and organisational benefits, revealing the possibility of and need for matching methods to the local contextual conditions and needs for QI. Subsequently, a framework for this task is presented. Second, the results on the explored methods describing care complexity yield additional understanding of variations and care systems across stakeholders compared to traditional methods used for QI in each context. Methods describing care complexity may, therefore, be useful to support QI efforts. Third, when methods describe care complexity, stakeholders might be supported in driving local QI efforts, and as the new perspectives seem to challenge their mental models, they also seem to develop their understanding of QI.The findings and conclusions of this thesis primarily contribute to the QI research field but can also inform other research on Lexis diagrams, process mining, and public care procurement

Research and innovation 2019

Research and innovation are two pillars that come together when universities are at stake. The expansion of the frontiers of human knowledge, in all areas and disciplines, is an irrefutable commitment of higher education institutions. Together with public and private entities, they are also committed to promoting knowledge transfer to society and the economy, in the form of new ideas, new products and new processes. Universities are supposed to transform ideas into value for society. To achieve these goals, higher education institutions have to assure their human resources are highly qualified, that they have an adequate atmosphere, that research is of high quality, and finally that adequate interactions take place. At UMinho we have a clear strategy to be an open and permanent space for knowledge production and furtherance of nationally and internationally relevant innovation across different social and economic sectors. For many years, UMinho has adopted the principles of open access and open science. We aim at carrying out our scientific activity and the dissemination of the corresponding results transparently and collaboratively; this implies that researchers, citizens, policymakers, state agencies, companies, and third sector organizations work in close cooperation facing research and innovation processes. We believe this is the shorter way to trigger smart and sustainable growth and qualified job creation. At UMinho, we encourage the coupling between research and education. Our goal is to expand research opportunities and to give our students occasions to experience vibrant research environments, ensuring that learning goes beyond the “common” routines. Joining research and learning processes provides both undergraduate and postgraduate students with opportunities to own their learning process. We believe that research experience has a role to play in improving students’ motivation for learning, in the pursuit of their interests. Doing better science occurs when we make it both more sensitive to the needs of society and also more efficient in what concerns the allocated resources. It is also a question of accountability. This is fundamental for reinforcing society awareness about our contributions to human and social development. Following the 2018 publication, we present here the 2019 edition of Research and Innovation, a series that draws on the outcomes of the activity of the UMinho research and innovation ecosystem. This comprehensive volume gives particular emphasis to the Research Units outcomes, namely in terms of funding, research projects, papers, and the most important achievements; the activity of the Interface Units and Collaborative Laboratories in which UMinho participates is also reported, through their activities and institutional projects, making evident their importance for the continuous growth of our Institution, our region, and our country. Rui Vieira de Castro RectorPublishe

A Smart Products Lifecycle Management (sPLM) Framework - Modeling for Conceptualization, Interoperability, and Modularity

Autonomy and intelligence have been built into many of today’s mechatronic products, taking advantage of low-cost sensors and advanced data analytics technologies. Design of product intelligence (enabled by analytics capabilities) is no longer a trivial or additional option for the product development. The objective of this research is aimed at addressing the challenges raised by the new data-driven design paradigm for smart products development, in which the product itself and the smartness require to be carefully co-constructed. A smart product can be seen as specific compositions and configurations of its physical components to form the body, its analytics models to implement the intelligence, evolving along its lifecycle stages. Based on this view, the contribution of this research is to expand the “Product Lifecycle Management (PLM)” concept traditionally for physical products to data-based products. As a result, a Smart Products Lifecycle Management (sPLM) framework is conceptualized based on a high-dimensional Smart Product Hypercube (sPH) representation and decomposition. First, the sPLM addresses the interoperability issues by developing a Smart Component data model to uniformly represent and compose physical component models created by engineers and analytics models created by data scientists. Second, the sPLM implements an NPD3 process model that incorporates formal data analytics process into the new product development (NPD) process model, in order to support the transdisciplinary information flows and team interactions between engineers and data scientists. Third, the sPLM addresses the issues related to product definition, modular design, product configuration, and lifecycle management of analytics models, by adapting the theoretical frameworks and methods for traditional product design and development. An sPLM proof-of-concept platform had been implemented for validation of the concepts and methodologies developed throughout the research work. The sPLM platform provides a shared data repository to manage the product-, process-, and configuration-related knowledge for smart products development. It also provides a collaborative environment to facilitate transdisciplinary collaboration between product engineers and data scientists

ORED Communicator - February 2015

The February 2015 issue of the Office of Research and Economic Development newsletter.https://digitalcommons.fiu.edu/research_newsletter/1006/thumbnail.jp