Studies in health technology and informatics
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Stud Health Technol Inform · Aug 2019
Understanding U.S. Adults' Zika Virus Risk Perceptions and Mitigation Behaviors to Improve Technology-Supported Risk Communication.
Understanding public risk perceptions, and how these affect behavior, is critical to public health's ability to leverage technology for risk communications. However, little is known about Zika virus risk perceptions. We addressed this gap by analyzing nationally representative (U. ⋯ Our results suggest that a minority of U. S. adults perceive Zika to be a major threat (13%), and only about 15% have taken protective actions. Our findings have implications for improving technology-supported risk communication.
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Stud Health Technol Inform · Aug 2019
Development and Progression in Danish eHealth Policies: Towards Evidence-Based Policy Making.
In order to realise the potential benefits of eHealth, governments develop eHealth policies to define and prioritise initiatives, the strategic goals and the resulting benefits. During the 23 years with eHealth policies in Denmark only a few status reports with a systematic and transparent evaluation have been made. ⋯ Interestingly, strategies for evaluating the devolopment of eHealth and eHealth policies were very sparcely noted in the policies. For the first time the de-emphasising of evaluations of eHealth policies in Denmark has been empirically demonstrated, thus undermining the objective of obtaining evidence-based eHealth policies.
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Stud Health Technol Inform · Aug 2019
Analysis and Measurement of China's Population Health Informatization Development Strategy.
This paper analyzes the development strategy of population health informatization in China, and summarizes the measurement direction and evaluation elements of population health informatization. Methods: Literature and field investigation, expert consultation and PEST analysis were used to determine the development level measurement and evaluation framework. ⋯ The information from hospitals and grass- roots medical and health institutions was also provided. From the perspective of the level of development, the framework of informatization evaluation is further refined.
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Stud Health Technol Inform · Aug 2019
The Barriers and Facilitators for Nurse Educators Using Telehealth for Education.
Telehealth is available world-wide and in addition to clinical uses, it can also be used to provide education for health professionals, supporting e-Networking. However, within New Zealand the uptake and widespread use of telehealth remains low, and why telehealth is not used more is not understood. This study describes nurse educators use of telehealth for education and identifies barriers and facilitators to increase the uptake of telehealth amongst nurse educators. ⋯ Equipment that was not user friendly and a lack of initial training were recognised as barriers to their uptake of telehealth. Telehealth training and support, and local champions were identified facilitators to increase the uptake of telehealth. Recommendations include the need for early adopting nurse educators to be recognised and encouraged, to role model good practice in telehealth, and mentor and support others.
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Stud Health Technol Inform · Jul 2019
Clinical Decision Support Systems: From the Perspective of Small and Imbalanced Data Set.
Clinical decision support systems are data analysis software that supports health professionals' decision - making the process to reach their ultimate outcome, taking into account patient information. However, the need for decision support systems cannot be denied because of most activities in the field of health care within the decision-making process. Decision support systems used for diagnosis are designed based on disease due to the complexity of diseases, symptoms, and disease-symptoms relationships. ⋯ Within the scope of the study, the publicly accessible data set, hepatitis was oversampled by distance-based data generation methods. The oversampled data sets were classified by using four different machine learning algorithms. Considering the classification scores of four different machine learning algorithms (Artificial Neural Networks, Support Vector Machines, Naive Bayes and Decision Tree), optimal synthetic data generation rate is recommended.