Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: Methods of a decision-maker-researcher partnership systematic review

<p>Abstract</p> <p>Background</p> <p>Computerized clinical decision support systems are information technology-based systems designed to improve clinical decision-making. As with any healthcare intervention with claims to improve process of care or patient outcomes, decision support systems should be rigorously evaluated before widespread dissemination into clinical practice. Engaging healthcare providers and managers in the review process may facilitate knowledge translation and uptake. The objective of this research was to form a partnership of healthcare providers, managers, and researchers to review randomized controlled trials assessing the effects of computerized decision support for six clinical application areas: primary preventive care, therapeutic drug monitoring and dosing, drug prescribing, chronic disease management, diagnostic test ordering and interpretation, and acute care management; and to identify study characteristics that predict benefit.</p> <p>Methods</p> <p>The review was undertaken by the Health Information Research Unit, McMaster University, in partnership with Hamilton Health Sciences, the Hamilton, Niagara, Haldimand, and Brant Local Health Integration Network, and pertinent healthcare service teams. Following agreement on information needs and interests with decision-makers, our earlier systematic review was updated by searching Medline, EMBASE, EBM Review databases, and Inspec, and reviewing reference lists through 6 January 2010. Data extraction items were expanded according to input from decision-makers. Authors of primary studies were contacted to confirm data and to provide additional information. Eligible trials were organized according to clinical area of application. We included randomized controlled trials that evaluated the effect on practitioner performance or patient outcomes of patient care provided with a computerized clinical decision support system compared with patient care without such a system.</p> <p>Results</p> <p>Data will be summarized using descriptive summary measures, including proportions for categorical variables and means for continuous variables. Univariable and multivariable logistic regression models will be used to investigate associations between outcomes of interest and study specific covariates. When reporting results from individual studies, we will cite the measures of association and p-values reported in the studies. If appropriate for groups of studies with similar features, we will conduct meta-analyses.</p> <p>Conclusion</p> <p>A decision-maker-researcher partnership provides a model for systematic reviews that may foster knowledge translation and uptake.</p

Capturing high-level requirements of information dashboards' components through meta-modeling

[EN]Information dashboards are increasing their sophistication to match new necessities and adapt to the high quantities of generated data nowadays.These tools support visual analysis, knowledge generation, and thus, are crucial systems to assist decision-making processes.However, the design and development processes are complex, because several perspectives and components can be involved.Tailoringcapabilities are focused on providing individualized dashboards without affecting the time-to-market through the decrease of the development processes' time. Among the methods used to configure these tools, the software product lines paradigm and model-driven development can be found. These paradigms benefit from the study of the target domain and the abstraction of features, obtaining high-level models that can be instantiated into concrete models. This paper presents a dashboard meta-model that aims to be applicable to any dashboard. Through domain engineering, different features of these tools are identified and arranged into abstract structuresand relationships to gain a better understanding of the domain. The goal of the meta-model is to obtain a framework for instantiating any dashboard to adapt them to different contexts and user profiles.One of the contexts in which dashboards are gaining relevance is Learning Analytics, as learning dashboards are powerful tools for assisting teachers and students in their learning activities.To illustrate the instantiation process of the presented meta-model, a small example within this relevant context (Learning Analytics) is also provided

On-line Point-of-Click Web Usability Mining with PopEval_MB, WebEval_MB and the C-Assure Methodology

In this paper we describe a new tool for planning, creating and conducting wide-ranging usability data acquisition throughout the system life cycle from inception to replacement. This aids sub-culturally targetable, on-line mass-consultation applicable to usability studies and change management. Usability and web data intelligence mining is made possible by the system capturing data from users, asynchronously on a distributed network, with minimised annoyance and judgement distortion effects. Previous research has shown that human judgements, particularly retrospective as distinct from real-time evaluations of painful experiences, are fundamentally flawed when superseded by other experiences. In evaluation studies as in any knowledge elicitation exercise (whether for requirements specification, expert systems prototyping or IS impact analysis) it is vital that unarticulated or poorly articulated data is captured as completely as possible whilst minimising distortion bias effects and annoyance of the user. A monolithic data elicitation method often proves inadequate for requirements acquisition or usability data whereas a dynamic planning framework can provide the execution monitoring and contextually-aware control of the enquiry process, as prescribed in Llemex_rb (Badii 1986/87/88). Such meta-level reasoning needs meta-methodological knowledge of the situated applicability of methods so as to choose suitable techniques to capture user data. Such data can range from simple static IDs to highly dynamic data on underlying patterns of multi-modal user behaviour; with various life-cycle models, sub-languages, semiotics and dispositional attitudes (Badii 1986, 1999a,b,c). PopEval_MB as an unobtrusive, on-line, mass-personalisation tool replaces the traditional paper-based survey methods, which suffer from problems of usability data distortion and acquisition management. It serves an enquiry methodology, which is contextually sensitive to the capture problems of the particular data type(s) being targeted at any time thus guiding the selection of suitable elicitation techniques along the way (Badii 1986/96). The enquiry methodology itself is a sub-system within a meta-methodology of frameworks and tools for IS/IT impact analysis and IS cultural compatibility management. This meta-methodology is referred to as Cultural Accommodation Analysis with Sensitised Systems for User/Usability Relationships and Reachabilities Evaluation (C-Assure); under a research programme directed by P3i at UCN (Badii et al 1996, 1999a,b,c). This paper describes the motivation for C-Assure in researching applicable meta-models that minimise the risks in IS development and adoption. It gives an overview of the tools that C-Assure seeks to incorporate into an integrated IS Planning, Development and Diffusion Support Environment (IPDSE) of which the tools for usability evaluation, mass-personalisation and web intelligence ie PopEval_MB and Web_Eval_AB are the focus of this work. The paper explains the theoretical foundations and the hypotheses to be tested in terms of the human Judgement and Decision Making, and, the Pleasure and Pain Recall, or (J/DM)-PPR theoretic effects which also motivated the design of PopEval_MB. Our results support the recent findings from cognitive psychology studies in applying the research on Pleasure and Pain Recall (PPR) to Human Computer Interaction (HCI). In this context we have validated the influence of factors modifying J/DM; specifically the effects of \u27duration neglect\u27 and \u27peak-and-end evaluations’. Thus the empirical studies, as performed here, have provided the first supportive evidence for the J/DM and PPR results from earlier research in psychology as can be applied to the fields of software engineering; in particular to Web Mediated Systems (WMS) for on-line shopping as an exemplar. We maintain that more expressive causal models of usability are needed for the increasingly more volatile user environments of emergent interactive systems such as WMS. We propose a new definition and a process model for dynamic usability, distinguishing instantaneous and steady state usability. The results indicate that PopEval_MB and WebEval_AB deliver their intended functionality with minimal user annoyance and distortion bias. We show how PopEval_MB can be used to by-pass, interpret and exploit natural J/DM-PPR biases; to enable the elicitation of least-distorted usability data intelligence; to reveal the precise root causes of, and the routes to, perceived user (dis)satisfaction. This study also confirms the validity of our new dynamic usability process model, which exploits the natural J/DM–PPR saliencyrecency effects and is thus more relevant to the emergent click-happy WMS user environments. The results can be exploited in interpretivist-iterative approaches to IS deployment, diffusion and change management, enterprise health analysis, marketing, design of WebAds and culturally inter-operable systems generall

Enhancing geographical information systems capabilities with multi-criteria evaluation functions

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A meta-analysis of relationships between organizational characteristics and IT innovation adoption in organizations

This is the post-print version of the final paper published in Information & Management. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.Adoption of IT in organizations is influenced by a wide range of factors in technology, organization, environment, and individuals. Researchers have identified several factors that either facilitate or hinder innovation adoption. Studies have produced inconsistent and contradictory outcomes. We performed a meta-analysis of ten organizational factors to determine their relative impact and strength. We aggregated their findings to determine the magnitude and direction of the relationship between organizational factors and IT innovation adoption. We found organizational readiness to be the most significant attribute and also found a moderately significant relationship between IT adoption and IS department size. Our study found weak significance of IS infrastructure, top management support, IT expertise, resources, and organizational size on IT adoption of technology while formalization, centralization, and product champion were found to be insignificant attributes. We also examined stage of innovation, type of innovation, type of organization, and size of organization as moderator conditions affecting the relationship between the organizational variables and IT adoption

Team Learning: A Theoretical Integration and Review

With the increasing emphasis on work teams as the primary architecture of organizational structure, scholars have begun to focus attention on team learning, the processes that support it, and the important outcomes that depend on it. Although the literature addressing learning in teams is broad, it is also messy and fraught with conceptual confusion. This chapter presents a theoretical integration and review. The goal is to organize theory and research on team learning, identify actionable frameworks and findings, and emphasize promising targets for future research. We emphasize three theoretical foci in our examination of team learning, treating it as multilevel (individual and team, not individual or team), dynamic (iterative and progressive; a process not an outcome), and emergent (outcomes of team learning can manifest in different ways over time). The integrative theoretical heuristic distinguishes team learning process theories, supporting emergent states, team knowledge representations, and respective influences on team performance and effectiveness. Promising directions for theory development and research are discussed

A design recording framework to facilitate knowledge sharing in collaborative software engineering

This paper describes an environment that allows a development team to share knowledge about software artefacts by recording decisions and rationales as well as supporting the team in formulating and maintaining design constraints. It explores the use of multi-dimensional design spaces for capturing various issues arising during development and presenting this meta-information using a network of views. It describes a framework to underlie the collaborative environment and shows the supporting architecture and its implementation. It addresses how the artefacts and their meta-information are captured in a non-invasive way and shows how an artefact repository is embedded to store and manage the artefacts