Electronic Health Records: Cure-all or Chronic Condition?

Computer-based information systems feature in almost every aspect of our lives, and yet most of us receive handwritten prescriptions when we visit our doctors and rely on paper-based medical records in our healthcare. Although electronic health record (EHR) systems have long been promoted as a cost-effective and efficient alternative to this situation, clear-cut evidence of their success has not been forthcoming. An examination of some of the underlying problems that prevent EHR systems from delivering the benefits that their proponents tout identifies four broad objectives - reducing cost, reducing errors, improving coordination and improving adherence to standards - and shows that they are not always met. The three possible causes for this failure to deliver involve problems with the codification of knowledge, group and tacit knowledge, and coordination and communication. There is, however, reason to be optimistic that EHR systems can fulfil a healthy part, if not all, of their potential

Real-Time Classification of Twitter Trends

Social media users give rise to social trends as they share about common interests, which can be triggered by different reasons. In this work, we explore the types of triggers that spark trends on Twitter, introducing a typology with following four types: 'news', 'ongoing events', 'memes', and 'commemoratives'. While previous research has analyzed trending topics in a long term, we look at the earliest tweets that produce a trend, with the aim of categorizing trends early on. This would allow to provide a filtered subset of trends to end users. We analyze and experiment with a set of straightforward language-independent features based on the social spread of trends to categorize them into the introduced typology. Our method provides an efficient way to accurately categorize trending topics without need of external data, enabling news organizations to discover breaking news in real-time, or to quickly identify viral memes that might enrich marketing decisions, among others. The analysis of social features also reveals patterns associated with each type of trend, such as tweets about ongoing events being shorter as many were likely sent from mobile devices, or memes having more retweets originating from a few trend-setters.Comment: Pre-print of article accepted for publication in Journal of the American Society for Information Science and Technology copyright @ 2013 (American Society for Information Science and Technology