Complexity and the science of implementation in health IT — knowledge gaps and future visions

Objectives The intent of this paper is in the examination of health IT implementation processes – the barriers to and facilitators of successful implementation, identification of a beginning set of implementation best practices, the identification of gaps in the health IT implementation body of knowledge, and recommendations for future study and application. Methods A literature review resulted in the identification of six health IT related implementation best practices which were subsequently debated and clarified by participants attending the NI2012 Research Post Conference held in Montreal in the summer of 2012. Using the framework for implementation research (CFIR) to guide their application, the six best practices were applied to two distinct health IT implementation studies to assess their applicability. Results Assessing the implementation processes from two markedly diverse settings illustrated both the challenges and potentials of using standardized implementation processes. In support of what was discovered in the review of the literature, “one size fits all” in health IT implementation is a fallacy, particularly when global diversity is added into the mix. At the same time, several frameworks show promise for use as “scaffolding” to begin to assess best practices, their distinct dimensions, and their applicability for use. Conclusions Health IT innovations, regardless of the implementation setting, requires a close assessment of many dimensions. While there is no “one size fits all”, there are commonalities and best practices that can be blended, adapted, and utilized to improve the process of implementation. This paper examines health IT implementation processes and identifies a beginning set of implementation best practices, which could begin to address gaps in the health IT implementation body of knowledge.

Measuring complexity for building projects : a Delphi study

Purpose---The aim of this study is to identify complexity measures for building projects in the People’s Republic of China (PRC). Design/Methodology/Approach---A three-round of Delphi questionnaire survey was conducted to identify the key parameters that measure the degree of project complexity. A complexity index (CI) was developed based on the identified measures and their relative importance. Findings---Six key measures of project complexity have been identified, which include, namely (1) building structure & function; (2) construction method; (3) the urgency of the project schedule; (4) project size/scale; (5) geological condition; and (6) neighboring environment. Practical implications---These complexity measures help stakeholders assess degrees of project complexity and better manage the potential risks that might be induced to different levels of project complexity. Originality/Value---The findings provide insightful perspectives to define and understand project complexity. For stakeholders, understanding and addressing the complexity help to improve project planning and implementation.

The incorporation of web 2.0 into learning design : an implementation model from the perspectives of teachers and students

This paper summarises some of the recent studies on various types of learning approaches that have utilised some form of Web 2.0 services in curriculum design to enhance learning. A generic implementation model of this integration will then be presented to illustrate the overall learning implementation process. Recently, the integration of Web 2.0 technologies into learning curriculum has begun to get a wide acceptance among teaching instructors across various higher learning institutions. This is evidenced by numerous studies which indicate the implementation of a range of Web 2.0 technologies into their learning design to improve learning delivery. Moreover, recent studies also have shown that the ability of current students to embrace Web 2.0 technologies is better than students using existing learning technology. Despite various attempts made by teachers in relation to the integration, researchers have noted a lack of integration standard to help in curriculum design. The absence of this standard will restrict the capacity of Web 2.0 adaptation into learning and adding more the complexity to provide meaningful learning. Therefore, this paper will attempt to draw a conceptual integration model which is being generated to reflect how learning activities with some facilitation of Web 2.0 is currently being implemented. The design of this model is based on shared experiences by many scholars as well as feedback gathered from two separate surveys conducted on teachers and a group of 180 students. Furthermore, this paper also recognizes some key components that generally engage in the design of a Web 2.0 teaching and learning which need to be addressed accordingly. Overall, the content of this paper will be organised as follows. The first part of the paper will introduce the importance of Web 2.0 implementation in teaching and learning from the perspective of higher education institutions and those challenges surrounding this area. The second part summarizes related works done in this field and brings forward the concept of designing learning with the incorporation of Web 2.0 technology. The next part presents the results of analysis derived from the two student and teachers surveys on using Web 2.0 during learning activities. This paper concludes by presenting a model that reflects several key entities that may be involved during the learning design.

On Ordinal VC-Dimension and Some Notions of Complexity

We generalize the classical notion of Vapnik–Chernovenkis (VC) dimension to ordinal VC-dimension, in the context of logical learning paradigms. Logical learning paradigms encompass the numerical learning paradigms commonly studied in Inductive Inference. A logical learning paradigm is defined as a set W of structures over some vocabulary, and a set D of first-order formulas that represent data. The sets of models of ϕ in W, where ϕ varies over D, generate a natural topology W over W. We show that if D is closed under boolean operators, then the notion of ordinal VC-dimension offers a perfect characterization for the problem of predicting the truth of the members of D in a member of W, with an ordinal bound on the number of mistakes. This shows that the notion of VC-dimension has a natural interpretation in Inductive Inference, when cast into a logical setting. We also study the relationships between predictive complexity, selective complexity—a variation on predictive complexity—and mind change complexity. The assumptions that D is closed under boolean operators and that W is compact often play a crucial role to establish connections between these concepts. We then consider a computable setting with effective versions of the complexity measures, and show that the equivalence between ordinal VC-dimension and predictive complexity fails. More precisely, we prove that the effective ordinal VC-dimension of a paradigm can be defined when all other effective notions of complexity are undefined. On a better note, when W is compact, all effective notions of complexity are defined, though they are not related as in the noncomputable version of the framework.