An investigation into the application of Claims Analysis to evaluate usability of a digital library interface

There is a need for tools that help developers evaluate the usability of digital library interfaces. The potential for using Claims Analysis to help developers in this way has been investigated in three linked case studies. The first explored the design rationale of an existing design with its developers. This showed that they had considered positive consequences for novice uses but that they found it difficult to identify negative effects. The second study explored the detailed design of an add-on feature. A scenario and sample claims were introduced to evaluate exploratory use within an action cycle of planning, execution and evaluation. This framework provided an effective stimulus to enable the developers to evaluate the design and explore opportunities for redesign. Finally, some novice users explored the digital library and the findings from this were used to validate a user scenario and claims

Sentiment analysis on social media for identifying public awareness of type 2 diabetes

2020-November956-96

Conversational agents in healthcare: a scoping review and conceptual analysis

Background: Conversational agents also known as chatbots are computer programs designed to simulate human text or verbal conversations. They are increasingly used in a range of fields, including healthcare. By enabling better accessibility, personalization and efficiency, conversational agents have the potential to improve patient care. Objectives: To review the current applications, gaps and challenges in the literature on conversational agents in healthcare and provide recommendations for their future research, design and application. Methods: We performed a scoping review. A broad literature search was done in Medline (Ovid), EMBASE (Ovid), PubMed, Scopus and Cochrane central with the search terms “conversational agents”, “conversational AI”, “chatbots” and associated synonyms. We also searched grey literature using sources such as OCLC World Cat database and Research Gate in April 2019. Reference lists of relevant articles were checked for further articles. Screening and data extraction were performed in parallel by two review authors. The included evidence was analyzed narratively employing the principles of thematic analysis. Results: The literature search yielded 47 study reports (45 articles and two ongoing clinical trials) which matched the inclusion criteria. The identified conversational agents were largely smartphone applications-delivered (n=23) and used free text only as the main input (n=19) and output (n=30) modality. Case-studies describing chatbot development (n=18) were most prevalent and only 11 RCTs were identified. Three most commonly reported conversational agent applications in the literature were treatment and monitoring, healthcare service support, and patient education. Conclusions: The literature on conversational agents in healthcare is largely descriptive and aimed at treatment and monitoring and health service support. It mostly reports on text-based, AI-driven and mobile application-delivered conversational agents. There is an urgent need for robust evaluation of diverse healthcare conversational agents’ formats focusing on their acceptability, safety and effectiveness

A 21st century approach to tackling dengue: Crowdsourced surveillance, predictive mapping and tailored communication

This paper describes a social media system to prevent dengue in Sri Lanka and potentially in the rest of the South and Southeast Asia regions. The system integrates three concepts of public health prevention that have thus far been implemented only in silos. First, the predictive surveillance component uses a computer simulation to forewarn health authorities and the general public about impending disease outbreaks. The civic engagement component allows the general public to use social media tools to interact and engage with health authorities by aiding them in surveillance efforts by reporting symptoms, mosquito bites and breeding sites using smartphone technologies. The health communication component utilizes citizen data gathered from the first two components to disseminate customized health awareness messages to enhance knowledge and increase preventive behaviors among citizens. The system, known as "Mo-Buzz," will be made available on a host of digital platforms like simple mobile phones, smart phones and a website. We present challenges and lessons learnt including content validation, stakeholder collaborations and applied trans-disciplinary research. (C) 2013 Elsevier B.V. All rights reserved

Top-Down and Bottom-up Approaches to Identify the Users, the Services and the Interface of a 2.0 Digital Library

International audienc