Privacy Concerns Persist, Could Be EHR Hurdle

2006 ◽  
Vol 39 (4) ◽  
pp. 68-69
Author(s):  
NELLIE BRISTOL
Keyword(s):  
Author(s):  
P. Sudheer ◽  
T. Lakshmi Surekha

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been to secure the cloud storage. Data content privacy. A semi anonymous privilege control scheme AnonyControl to address not only the data privacy. But also the user identity privacy. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semi anonymity. The  Anonymity –F which fully prevent the identity leakage and achieve the full anonymity.


MIS Quarterly ◽  
2013 ◽  
Vol 37 (1) ◽  
pp. 275-298 ◽  
Author(s):  
Weiyin Hong ◽  
◽  
James Y. L. Thong ◽  
◽  

2015 ◽  
Vol 8 (1) ◽  
Author(s):  
Himanshu Rajput

Social networking sites (SNSs) have become popular in India with the proliferation of Internet. SNSs have gained the interests of academicians and researchers. The current study is an endeavor to understand the continuance of social networking sites in India. The study applies an extended version of theory of planned behavior. Additional factors privacy concerns and habits were incorporated into the standard theory of planned behaviour. A survey was conducted in a Central University in India. Overall, data was collected from 150 respondents. PLS-SEM was used to test the proposed model. All the hypotheses except the moderating role of habits between intentions and continued use of social networking sites, were supported by the results. Habits were found to affect continued use of social networking sites indirectly through continued intentions.


2020 ◽  
Author(s):  
Rina Kagawa ◽  
Yukino Baba ◽  
Hideo Tsurushima

BACKGROUND Sharing progress notes as a common social capital is essential in research and education, but the content of progress notes is sensitive and needs to be kept confidential. Publishing actual progress notes are difficult due to privacy concerns. OBJECTIVE This study aims to generate a large repository of pseudo-progress notes of authentic quality. We focused on two requirements for authentic quality: the validity and consistency of the data, from the perspective of medical practice, and the empirical and semantic characteristics of progress notes, such as shorthand styles used for reporting changes in a patient's physical status, long narrative sentences detailing patient anxiety, and interprofessional communications. METHODS We proposed a practical framework that consists of a simulation of the notes and evaluation of the simulated notes. The framework utilized two human cognitive traits: (1) the ability to use imitation to simulate objects with diverse characteristics without background knowledge and (2) the use of comparison as a strategy for deep thinking. This enabled crowd workers to generate a large number of progress notes. Our framework involved three steps. In step 1, crowd workers imitated actual progress notes decomposed into subject data (S), object data (O), and assessment and plan (A/P). These imitated texts were then shuffled and recomposed in S, O, and A/P in order to create simulated progress notes. In step 2, crowd workers identified the characteristics of actual progress notes based on comparisons between actual and dummy progress notes. These characteristics were clustered based on their similarities. Each cluster exhibited the empirical and semantic characteristics of the actual progress notes. Finally, in step 3, the texts from step 1 that exhibited the identified characteristics from step 2 were evaluated as quality-guaranteed progress notes that met the two requirements. All data were preprocessed to protect patient privacy. RESULTS Step 1: By recomposing the 700 imitated texts, 9,856 simulated progress notes were generated. Step 2: 3,938 differences between actual progress notes and dummy progress notes were identified. After clustering, 166 characteristics were evaluated to be appropriate as empirical and semantic characteristics of the actual progress notes. Step 3: 500 crowd workers demonstrated that 83.0% of the simulated progress notes satisfied at least one of the characteristics obtained in step 2. The crowd workers' artificially-reproduced progress notes were evaluated to determine the most realistic, based on four metrics: disease, morpheme, readability, and reality. CONCLUSIONS Our results demonstrated that crowd workers could generate and evaluate highly professional documents. We have made our large repository of high-quality crowdsourced progress notes publicly available, and we encourage their use in the development of medical education and research.


2020 ◽  
Author(s):  
Yuanyuan Dang ◽  
Shanshan Guo ◽  
Xitong Guo

BACKGROUND The mobile health (mHealth) provides a new opportunity for patients’ disease prediction and health self-management. At the same time, privacy problems in mHealth have brought forth significant attention concerning patients' online health information disclosure and hindered mHealth development. OBJECTIVE Privacy calculus theory (PCT) has been widely used to understand personal information disclosure behaviors with the basic assumption of a national and linear decision-making process. However, people’s cognitive behavior processes are complex and mutual. In attempting to close this knowledge gap, we further optimize the information disclosure model of patients based on PCT by identifying the mutual relationship between costs (privacy concerns) and benefits. Social support, which has been proved to be a distinct and significant disclosure benefit of mHealth, was chosen to be the representative benefit of information disclosure in mHealth. METHODS From an individual perspective, a structural equation model with privacy concerns, health information disclosure intention in mHealth, and social support from mHealth has been examined. RESULTS 253 randomly selected participants provided validated questionnaire. The result indicated that perceived health information sensitivity positively enhances the privacy concern (0.505, p<0.01), and higher privacy concern levels will decrease the health information disclosure intention (-0.338, p<0.01). Various aspects of individual characters influence perceived health information sensitivity in different ways. The informational support has a negatively moderate on reduce the positive effect between perceived health information sensitivity and privacy concerns (-0.171, p<0.1) and will decrease the negative effect between privacy concerns and health information disclosure intention(-0.105, p<0.1). However, emotional support has no directly moderate effect on both privacy concerns and health information disclosure intention. CONCLUSIONS The results indicate that social support can be regarded as a disutility reducer, that is, on the one hand, it reduces the privacy concerns of patients; on the other hand, it also reduces the negative impact of privacy concerns on information disclosure intention. Moreover, the moderate effect of social support is partially supported. Informational support, one demission of social support, is significant, while the other demission, emotional support, is not significant in mHealth. Furthermore, the results are different among patients with different individual characteristics. This study also provides specific theoretical and practical implications to enhance the development of mHealth.


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