Improving Patient Safety Through Formal and Informal Learning Networks

This paper explores a novel perspective on patient safety improvements, which draws on
contemporary social network and learning theories. A case study was conducted at a Portuguese
acute university hospital. Data collection followed a staged approach, whereby 46 interviews
were conducted involving 49 respondents from a broad array of departments and professional
backgrounds. This case study highlights the importance of two major interlinked factors in
contributing to patient safety improvements. The first of these is the crucial role of formal and
informal, internal and external social networks. The second is the importance and the possible
advantage of combining formal and informal learning. The analysis suggests that initiatives
rooted in formal learning approaches alone do not necessarily lead to the creation of long-term
grounded internal safety networks, and that patient safety improvements can crucially depend on
bottom-up initiatives of communities of practice and informal learning. Traditional research on
patient safety places a strong emphasis on top-down and managerialist approaches and is often
based on the assumption that „safety? learning is primarily formal and context-independent. This
paper suggests that bottom-up initiatives and a combination of formal and informal learning can
make a major contribute to patient safety improvements.

Development of small and medium-sized enterprise horizontal innovation networks: UK agri-food sector study

Within the management literature, there is an emergent discourse on horizontal collaboration among small and medium-sized enterprises (SMEs), whereby individual rivalries are overcome by the need for more resources and innovation, leading to increased competitiveness through joint product development. In particular, a number of these horizontal collaborations between SMEs have occurred within the agri-food sector. As a consequence, this article aims to explore the longitudinal development of horizontal innovation networks within an artisan bakers’ network as part of the UK SME agri-food sector. An interpretivist research approach was used, whereby the development and evolution of an artisan bakers’ horizontal network was studied over a 27-month period. The findings, as summarised in conceptual models which draw upon knowledge-based open innovation and social network constructs, illustrate that a complex three-stage life cycle development occurred within the bakers’ horizontal network.

Location-Awareness for Radios and Networks: Part I

The papers in this special issue focus on the topic of location awareness for radio and networks. Localization-awareness using radio signals stands to revolutionize the fields of navigation and communication engineering. It can be utilized to great effect in the next generation of cellular networks, mining applications, health-care monitoring, transportation and intelligent highways, multi-robot applications, first responders operations, military applications, factory automation, building and environmental controls, cognitive wireless networks, commercial and social network applications, and smart spaces. A multitude of technologies can be used in location-aware radios and networks, including GNSS, RFID, cellular, UWB, WLAN, Bluetooth, cooperative localization, indoor GPS, device-free localization, IR, Radar, and UHF. The performances of these technologies are measured by their accuracy, precision, complexity, robustness, scalability, and cost. Given the many application scenarios across different disciplines, there is a clear need for a broad, up-to-date and cogent treatment of radio-based location awareness. This special issue aims to provide a comprehensive overview of the state-of-the-art in technology, regulation, and theory. It also presents a holistic view of research challenges and opportunities in the emerging areas of localization.

Finding Relevant Tweets

When a user of a microblogging site authors a microblog
post or browses through a microblog post, it provides cues as to what
topic she is interested in at that point in time. Example-based search
that retrieves similar tweets given one exemplary tweet, such as the one
just authored, can help provide the user with relevant content. We investigate
various components of microblog posts, such as the associated
timestamp, author’s social network, and the content of the post, and
develop approaches that harness such factors in finding relevant tweets
given a query tweet. An empirical analysis of such techniques on real
world twitter-data is then presented to quantify the utility of the various
factors in assessing tweet relevance. We observe that content-wise similar
tweets that also contain extra information not already present in the
query, are perceived as useful. We then develop a composite technique
that combines the various approaches by scoring tweets using a dynamic
query-specific linear combination of separate techniques. An empirical
evaluation establishes the effectiveness of the composite technique, and
that it outperforms each of its constituents.

Inferring and Exploiting Categories for Next Location Prediction

Predicting the next location of a user based on their previous visiting pattern is one of the primary tasks over data from location based social networks (LBSNs) such as Foursquare. Many different aspects of these so-called “check-in” profiles of a user have been made use of in this task, including spatial and temporal information of check-ins as well as the social network information of the user. Building more sophisticated prediction models by enriching these check-in data by combining them with information from other sources is challenging due to the limited data that these LBSNs expose due to privacy concerns. In this paper, we propose a framework to use the location data from LBSNs, combine it with the data from maps for associating a set of venue categories with these locations. For example, if the user is found to be checking in at a mall that has cafes, cinemas and restaurants according to the map, all these information is associated. This category information is then leveraged to predict the next checkin location by the user. Our experiments with publicly available check-in dataset show that this approach improves on the state-of-the-art methods for location prediction.

A compendium of modelling techniques

This Integration Insight provides a brief overview of the most popular modelling techniques used to analyse complex real-world problems, as well as some less popular but highly relevant techniques. The modelling methods are divided into three categories, with each encompassing a number of methods, as follows: 1) Qualitative Aggregate Models (Soft Systems Methodology, Concept Maps and Mind Mapping, Scenario Planning, Causal (Loop) Diagrams), 2) Quantitative Aggregate Models (Function fitting and Regression, Bayesian Nets, System of differential equations / Dynamical systems, System Dynamics, Evolutionary Algorithms) and 3) Individual Oriented Models (Cellular Automata, Microsimulation, Agent Based Models, Discrete Event Simulation, Social Network
Analysis). Each technique is broadly described with example uses, key attributes and reference material.