Bmc Med Inform Decis
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Bmc Med Inform Decis · Jun 2017
The challenges of emerging HISs in bridging the communication gaps among physicians and nurses in China: an interview study.
To explore the current situation, existing problems and possible causes of said problems with regards to physician-nurse communication under an environment of increasingly widespread usage of Hospital Information Systems and to seek out new potential strategies in information technology to improve physician-nurse communication. ⋯ There are objective risks in physician-nurse communication in Chinese hospitals, and clinical information systems lack solutions to the relevant problems. Developing a dedicated, mobile, quick and convenient module for physician-nurse communication within existing hospital information system with automatic reminders for important information that segregates between synchronous and asynchronous communication according to the different types of information could help improve physician-nurse communication.
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Bmc Med Inform Decis · May 2017
Modeling long-term human activeness using recurrent neural networks for biometric data.
With the invention of fitness trackers, it has been possible to continuously monitor a user's biometric data such as heart rates, number of footsteps taken, and amount of calories burned. This paper names the time series of these three types of biometric data, the user's "activeness", and investigates the feasibility in modeling and predicting the long-term activeness of the user. ⋯ This paper defines and investigates the notion of a user's "activeness", and shows that forecasting the long-term activeness of the user is indeed possible. Such information can be utilized by a health-related application to proactively recommend suitable events or services to the user.
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Bmc Med Inform Decis · Apr 2017
Social media engagement analysis of U.S. Federal health agencies on Facebook.
It is becoming increasingly common for individuals and organizations to use social media platforms such as Facebook. These are being used for a wide variety of purposes including disseminating, discussing and seeking health related information. U.S. Federal health agencies are leveraging these platforms to 'engage' social media users to read, spread, promote and encourage health related discussions. However, different agencies and their communications get varying levels of engagement. In this study we use statistical models to identify factors that associate with engagement. ⋯ We present the first comprehensive analyses of engagement with U.S. Federal health agencies on Facebook. In addition, we briefly compare and contrast findings from this study to our earlier study with similar focus but on Twitter to show the robustness of our methods.
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Bmc Med Inform Decis · Apr 2017
Automatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire data.
Feature selection methods are commonly used to identify subsets of relevant features to facilitate the construction of models for classification, yet little is known about how feature selection methods perform in diffusion tensor images (DTIs). In this study, feature selection and machine learning classification methods were tested for the purpose of automating diagnosis of migraines using both DTIs and questionnaire answers related to emotion and cognition - factors that influence of pain perceptions. ⋯ The proposed feature selection committee method improved the performance of migraine diagnosis classifiers compared to individual feature selection methods, producing a robust system that achieved over 90% accuracy in all classifiers. The results suggest that the proposed methods can be used to support specialists in the classification of migraines in patients undergoing magnetic resonance imaging.
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Bmc Med Inform Decis · Feb 2017
Smokers' and drinkers' choice of smartphone applications and expectations of engagement: a think aloud and interview study.
Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement. ⋯ Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.