Triggers and monitoring in intelligent personal health record

Machine and cognitive intelligence for human health: systematic review

Brain informatics is a novel interdisciplinary area that focuses on scientifically studying the mechanisms of human brain information processing by integrating experimental cognitive neuroscience with advanced Web intelligence-centered information technologies. Web intelligence, which aims to understand the computational, cognitive, physical, and social foundations of the future Web, has attracted increasing attention to facilitate the study of brain informatics to promote human health. A large number of articles created in the recent few years are proof of the investment in Web intelligence-assisted human health. This study systematically reviews academic studies regarding article trends, top journals, subjects, countries/regions, and institutions, study design, artificial intelligence technologies, clinical tasks, and performance evaluation. Results indicate that literature is especially welcomed in subjects such as medical informatics and health care sciences and service. There are several promising topics, for example, random forests, support vector machines, and conventional neural networks for disease detection and diagnosis, semantic Web, ontology mining, and topic modeling for clinical or biomedical text mining, artificial neural networks and logistic regression for prediction, and convolutional neural networks and support vector machines for monitoring and classification. Additionally, future research should focus on algorithm innovations, additional information use, functionality improvement, model and system generalization, scalability, evaluation, and automation, data acquirement and quality improvement, and allowing interaction. The findings of this study help better understand what and how Web intelligence can be applied to promote healthcare procedures and clinical outcomes. This provides important insights into the effective use of Web intelligence to support informatics-enabled brain studies.

Mobile PHRs compliance with Android and iOS usability guidelines

Mobile Personal Health Records (PHRs) have achieved a particularly strong market share since the appearance of more powerful mobile devices and popular worldwide mobile application markets such as Apple's App Store and Android's Google Play. However, Android and Apple have a set of recommendations on design and usability targeted towards developers who wish to publish apps in their stores: Android Design Guidelines and iOS Human Interface Guidelines. This paper aims to evaluate compliance with these guidelines by assessing the usability recommendations of a set of 24 selected mobile PHR applications. An analysis process based on a well-known Systematic Literature Review (SLR) protocol was used. The results show that the 24 mobile PHR applications studied are not suitably structured. 46 % of these applications do not use any of the recommended patterns, using instead lists or springboards, which are deprecated patterns for top-level menus. 70 % of the PHRs require a registration to be able to test the application when these interactions should be delayed. Our study will help both PHR users to select user-friendly mobile PHRs and PHR providers and developers to identify the good usability practices implemented by the applications with the highest scores.

Free Web-based personal health records: an analysis of functionality

This paper analyzes and assesses the functionality of free Web-based PHRs as regards health information, user actions and connection with other tools. A systematic literature review in Medline, ACM Digital Library, IEEE Digital Library and ScienceDirect was used to select 19 free Web-based PHRs from the 47 PHRs identified. The results show that none of the PHRs selected met 100% of the 28 functions presented in this paper. Two free Web-based PHRs target a particular public. Around 90 % of the PHRs identified allow users throughout the world to create their own profiles without any geographical restrictions. Only half of the PHRs selected provide physicians with user actions. Few PHRs can connect with other tools. There was considerable variability in the types of data included in free Web-based PHRs. Functionality may have implications for PHR use and adoption, particularly as regards patients with chronic illnesses or disabilities. Support for standard medical document formats and protocols are required to enable data to be exchanged with other stakeholders in the health care domain. The results of our study may assist users in selecting the PHR that best fits their needs, since no significant connection exists between the number of functions of the PHRs identified and their popularity.

Open issues in intelligent personal health record--an updated status report for 2012

To improve the capability and usability of the personal health record (PHR) as a tool to empower consumers in the management of their own health, we have proposed the concept of an intelligent PHR (iPHR) and built a prototype iPHR system with four functions. These four functions use various health knowledge and computer science techniques to automatically provide users with personalized healthcare information to facilitate their well-being. This paper discusses several open issues in iPHR, including two enhancements to an existing function and two potential new functions. The two enhancements are for automatically compiling relevant self-care activities for each health issue and automatically identifying contraindicated self-care activities, respectively. One potential new function is personalized search for individual healthcare providers. Another potential new function is personalized local search for health-related services to help maintain patients in their homes. We include some preliminary thoughts on how to address these open issues with the hope to stimulate future research work on iPHR.