Evaluation of mobile health education applications for health professionals and patients

Paper presented at 8th International conference on e-Health (EH 2016), 1-3 July 2016, Funchal, Madeira, Portugal. ABSTRACT Mobile applications for health education are commonly utilized to support patients and health professionals. A critical evaluation framework is required to ensure the usability and reliability of mobile health education applications in order to facilitate the saving of time and effort for the various user groups; thus, the aim of this paper is to describe a framework for evaluating mobile applications for health education. The intended outcome of this framework is to meet the needs and requirements of the different user categories and to improve the development of mobile health education applications with software engineering approaches, by creating new and more effective techniques to evaluate such software. This paper first highlights the importance of mobile health education apps, then explains the need to establish an evaluation framework for these apps. The paper provides a description of the evaluation framework, along with some specific evaluation metrics: an efficient hybrid of selected heuristic evaluation (HE) and usability evaluation (UE) factors to enable the determination of the usefulness and usability of health education mobile apps. Finally, an explanation of the initial results for the framework was obtained using a Medscape mobile app. The proposed framework - An Evaluation Framework for Mobile Health Education Apps – is a hybrid of five metrics selected from a larger set in heuristic and usability evaluation, filtered based on interviews from patients and health professionals. These five metrics correspond to specific facets of usability identified through a requirements analysis of typical users of mobile health apps. These metrics were decomposed into 21 specific questionnaire questions, which are available on request from first author

Reviews

Apolog

The Grammar of Interactive Explanatory Model Analysis

The growing need for in-depth analysis of predictive models leads to a series of new methods for explaining their local and global properties. Which of these methods is the best? It turns out that this is an ill-posed question. One cannot sufficiently explain a black-box machine learning model using a single method that gives only one perspective. Isolated explanations are prone to misunderstanding, which inevitably leads to wrong or simplistic reasoning. This problem is known as the Rashomon effect and refers to diverse, even contradictory interpretations of the same phenomenon. Surprisingly, the majority of methods developed for explainable machine learning focus on a single aspect of the model behavior. In contrast, we showcase the problem of explainability as an interactive and sequential analysis of a model. This paper presents how different Explanatory Model Analysis (EMA) methods complement each other and why it is essential to juxtapose them together. The introduced process of Interactive EMA (IEMA) derives from the algorithmic side of explainable machine learning and aims to embrace ideas developed in cognitive sciences. We formalize the grammar of IEMA to describe potential human-model dialogues. IEMA is implemented in the human-centered framework that adopts interactivity, customizability and automation as its main traits. Combined, these methods enhance the responsible approach to predictive modeling.Comment: 17 pages, 10 figures, 3 table

Using Shared Workspaces in Higher Education

We evaluate the use of BSCW shared workspaces in higher education by means of a comparison of seven courses in which this environment was used. We identify a number of different functions for which the BSCW environment has been used and discuss the relative success of these functions across the cases. In addition, we evaluate the cases with the 4E model of Collis et al. (2000) which predicts the chances of acceptance of ICT in an educational setting. Effectiveness for the given task appears to be a prime success factor for using ICT. But an effective tool may fail due to other factors like ease of use and organisational, socialcultural or technological obstacles. The particular strength of a shared workspace, for which BSCW is most effective and efficient, is providing a repository for objects of collaborative work. Other types of usage showed mixed results. In the future we expect that learning takes place in an integrated, open ICT environment in which different kinds of tools are available for different purposes and users can switch between tools as appropriate. We could observe this in several of the case studies, where non-use of BSCW did not mean that a particular task was not performed, but, on the contrary, a more efficient solution for the same function was available. Shared workspaces have proven to be highly useful, but it seems advisable that their purpose be limited to what they were originally designed for

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Systematic evaluation of design choices for software development tools

[Abstract]: Most design and evaluation of software tools is based on the intuition and experience of the designers. Software tool designers consider themselves typical users of the tools that they build and tend to subjectively evaluate their products rather than objectively evaluate them using established usability methods. This subjective approach is inadequate if the quality of software tools is to improve and the use of more systematic methods is advocated. This paper summarises a sequence of studies that show how user interface design choices for software development tools can be evaluated using established usability engineering techniques. The techniques used included guideline review, predictive modelling and experimental studies with users

A safety analysis approach to clinical workflows : application and evaluation

Clinical workflows are safety critical workflows as they have the potential to cause harm or death to patients. Their safety needs to be considered as early as possible in the development process. Effective safety analysis methods are required to ensure the safety of these high-risk workflows, because errors that may happen through routine workflow could propagate within the workflow to result in harmful failures of the system’s output. This paper shows how to apply an approach for safety analysis of clinic al workflows to analyse the safety of the workflow within a radiology department and evaluates the approach in terms of usability and benefits. The outcomes of using this approach include identification of the root causes of hazardous workflow failures that may put patients’ lives at risk. We show that the approach is applicable to this area of healthcare and is able to present added value through the detailed information on possible failures, of both their causes and effects; therefore, it has the potential to improve the safety of radiology and other clinical workflows