Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion

MIS Quarterly ◽  
2009 ◽  
Vol 33 (2) ◽  
pp. 339 ◽  
Author(s):  
Angst ◽  
Agarwal
Keyword(s):  
Electronic Health Records ◽  
Elaboration Likelihood Model ◽  
Elaboration Likelihood ◽  
Health Records ◽  
Privacy Concerns ◽  
Likelihood Model ◽  
Electronic Health

Privacy concerns for mobile app download: An elaboration likelihood model perspective

2017 ◽  
Vol 94 ◽  
pp. 19-28 ◽  
Author(s):  
Jie Gu ◽  
Yunjie (Calvin) Xu ◽  
Heng Xu ◽  
Cheng Zhang ◽  
Hong Ling
Keyword(s):  
Elaboration Likelihood Model ◽  
Mobile App ◽  
Elaboration Likelihood ◽  
Privacy Concerns ◽  
Likelihood Model

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2015 ◽  
pp. 251-264
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

Privacy-Friendly Management of Electronic Health Records in the eHealth Context

2017 ◽  
pp. 528-542
Author(s):  
Milica Milutinovic ◽  
Bart De Decker
Keyword(s):  
Electronic Health Records ◽  
Large Scale ◽  
Patient Data ◽  
Health Records ◽  
Privacy Concerns ◽  
Ubiquitous Technology ◽  
Emergency Situations ◽  
User Centric ◽  
Electronic Health

Electronic Health Records (EHRs) are becoming the ubiquitous technology for managing patients' records in many countries. They allow for easier transfer and analysis of patient data on a large scale. However, privacy concerns linked to this technology are emerging. Namely, patients rarely fully understand how EHRs are managed. Additionally, the records are not necessarily stored within the organization where the patient is receiving her healthcare. This service may be delegated to a remote provider, and it is not always clear which health-provisioning entities have access to this data. Therefore, in this chapter the authors propose an alternative where users can keep and manage their records in their existing eHealth systems. The approach is user-centric and enables the patients to have better control over their data while still allowing for special measures to be taken in case of emergency situations with the goal of providing the required care to the patient.

scholarly journals Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ruowang Li ◽  
Rui Duan ◽  
Xinyuan Zhang ◽  
Thomas Lumley ◽  
Sarah Pendergrass ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Genetic Data ◽  
Test Statistics ◽  
Health Records ◽  
Privacy Concerns ◽  
Individual Level ◽  
Multiple Datasets ◽  
Level Data ◽  
Electronic Health ◽  
Identical Test

AbstractIncreasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients’ data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.

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