The Development of a Point of Care Clinical Guidelines Mobile Application Following a User-Centred Design Approach

This paper describes the development of a point of care clinical guidelines mobile application. A user-centred design approach was utilised to inform the design of a smartphone application, this included: Observations; a survey; focus groups and an analysis of popular apps utilised by clinicians in a UK NHS Trust. Usability testing was conducted to inform iterations of the application, which presents clinicians with a variety of integrated tools to aid in decision making and information retrieval. The study found that clinicians use a mixture of technology to retrieve information, which is often inefficient or has poor usability. It also shows that smartphone application development for use in UK hospitals needs to consider the variety of users and their clinical knowledge and work pattern. This study highlights the need for applying user-centred design methods in the design of information presented to clinicians and the need for clinical information delivery that is efficient and easy to use at the bedside

Research of a m-health app design for information management of MDTMs

\u3cp\u3eThe m-Health apps have been adopted broadly in both medical and family environment. They hold potential to support the work of medical staff and provide help in individual health care. However, the emphasis on the benefits of mobility and the functionality is not enough. Relatively little empirical research guides for the app development. The m-Health apps should be developed for specific purposes with the consideration of the specific users and using contexts. This paper introduces a research for a m-Health app design in support of information management for multidisciplinary medical team meetings (MDTMs) in order to enhancing meeting efficiency. The contextual design methods were used as the guideline for the design. The app design based on tablet was developed and tested by medical teams in this study. The results indicated that the most medical staffs held positive and supportive attitudes to the m-Health app as an intervention in their medical meetings. The recommended app helped medical staffs including oncologists and nurses etc. to improve their meeting efficiency through information management such as setting up meeting schedule, making records for meetings, updating the patients’ information, etc. The results also revealed that the choices of different mobile platforms should be taken into account when developing m-Health apps since it would greatly influence user experience in utility and usability in the specific contexts. Design recommendations were summarized for future design.\u3c/p\u3

An Integrated Octree-RANSAC Technique for Automated LiDAR Building Data Segmentation for Decorative Buildings

The 12th International Symposium on Visual Computing (ISVC 2016), Las Vegas, United States of America, 12-14 December 2016This paper introduces a new method for the automated segmentation of laser scanning data for decorative urban buildings. The method combines octree indexing and RANSAC - two previously established but heretofore not integrated techniques. The approach was successfully applied to terrestrial point clouds of the facades of five highly decorative urban structures for which existing approaches could not provide an automated pipeline. The segmentation technique was relatively efficient and wholly scalable requiring only 1 second per 1,000 points, regardless of the façade’s level of ornamentation or non-recti-linearity. While the technique struggled with shallow protrusions, its ability to process a wide range of building types and opening shapes with data densities as low as 400 pts/m2 demonstrate its inherent potential as part of a large and more sophisticated processing approach.European Research Counci

Lessons Learned with Laser Scanning Point Cloud Management in Hadoop HBase

While big data technologies are growing rapidly and benefit a wide range of science and engineering domains, many barriers remain for the remote sensing community to fully exploit the benefits provided by these emerging powerful technologies. To overcome these barriers, this paper presents the in-depth experience gained when adopting a distributed computing framework – Hadoop HBase – for storage, indexing, and integration of large scale, high resolution laser scanning point cloud data. Four data models were conceptualized, implemented, and rigorously investigated to explore the advantageous features of distributed, key-value database systems. In addition, the comparison of the four models facilitated the reassessment of several well-known point cloud management techniques founded in traditional computing environments in the new context of the distributed, key-value database. The four models were derived from two row-key designs and two columns structures, thereby demonstrating various considerations during the development of a data solution for high-resolution, city-scale aerial laser scan for a portion of Dublin, Ireland. This paper presents lessons learned from the data model design and its implementation for spatial data management in a distributed computing framework. The study is a step towards full exploitation of powerful emerging computing assets for dense spatio-temporal data.The Hadoop cluster used for the work presented in this paper was provided by allocation TG-CIE170036 - Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-154856