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7,460 results on '"Imran, Muhammad"'

1. Visual Analytic Tools and Techniques in Population Health and Health Services Research: Scoping Review

Author

Chishtie, Jawad Ahmed, Marchand, Jean-Sebastien, Turcotte, Luke A, Bielska, Iwona Anna, Babineau, Jessica, Cepoiu-Martin, Monica, Irvine, Michael, Munce, Sarah, Abudiab, Sally, Bjelica, Marko, Hossain, Saima, Imran, Muhammad, Jeji, Tara, and Jaglal, Susan

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundVisual analytics (VA) promotes the understanding of data with visual, interactive techniques, using analytic and visual engines. The analytic engine includes automated techniques, whereas common visual outputs include flow maps and spatiotemporal hot spots. ObjectiveThis scoping review aims to address a gap in the literature, with the specific objective to synthesize literature on the use of VA tools, techniques, and frameworks in interrelated health care areas of population health and health services research (HSR). MethodsUsing the 2018 PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines, the review focuses on peer-reviewed journal articles and full conference papers from 2005 to March 2019. Two researchers were involved at each step, and another researcher arbitrated disagreements. A comprehensive abstraction platform captured data from diverse bodies of the literature, primarily from the computer and health sciences. ResultsAfter screening 11,310 articles, findings from 55 articles were synthesized under the major headings of visual and analytic engines, visual presentation characteristics, tools used and their capabilities, application to health care areas, data types and sources, VA frameworks, frameworks used for VA applications, availability and innovation, and co-design initiatives. We found extensive application of VA methods used in areas of epidemiology, surveillance and modeling, health services access, use, and cost analyses. All articles included a distinct analytic and visualization engine, with varying levels of detail provided. Most tools were prototypes, with 5 in use at the time of publication. Seven articles presented methodological frameworks. Toward consistent reporting, we present a checklist, with an expanded definition for VA applications in health care, to assist researchers in sharing research for greater replicability. We summarized the results in a Tableau dashboard. ConclusionsWith the increasing availability and generation of big health care data, VA is a fast-growing method applied to complex health care data. What makes VA innovative is its capability to process multiple, varied data sources to demonstrate trends and patterns for exploratory analysis, leading to knowledge generation and decision support. This is the first review to bridge a critical gap in the literature on VA methods applied to the areas of population health and HSR, which further indicates possible avenues for the adoption of these methods in the future. This review is especially important in the wake of COVID-19 surveillance and response initiatives, where many VA products have taken center stage. International Registered Report Identifier (IRRID)RR2-10.2196/14019

Published
2020
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42. When Technology-Based Learning Is the Only Option: Evaluating Perceived Usefulness of Social Media

Author

Hanif, Aamer and Imran, Muhammad

Abstract

During unusual times involving discontinued face to face sessions in formal education settings, mobile learning (m-learning) involving social networking sites has become a popular alternative since students are always in possession of handheld electronic devices. When connection through technology was the only option due to social distancing in current pandemic, students who were already active extensive users of social networks found online learning as a new way of getting formal education. The objective of this study was to explore how the state of student's behavioral intention for social media based online learning is driven by external factors like subjective norm and self-efficacy. To fulfill this aim, this study uses a quantitative approach to study the factors that mediate the decision behavior of students towards social media employed as a learning platform and use of m-learning involving social networks. A sample of management science students (n= 255) from four universities participated in the research. Analysis of data suggested that subjective norm and self-efficacy were significant predictors for student participation in e-learning initiatives involving social media and networks. The proposed serial mediation model revealed that self-efficacy and perceived usefulness in that order were playing a positive significant role in student use of social networking for learning. No significant differences were observed between either gender when self-efficacy, perceived usefulness, and use of social media in education were considered.

Published
2022

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