The health care sector can benefit considerably from developments in digital technology. Consequently, eHealth applications are rapidly increasing in number and sophistication. For successful development and implementation of eHealth, it is paramount to guarantee the privacy and safety of patients and their collected data. At the same time, anonymized data that are collected through eHealth could be used in the development of innovative and personalized diagnostic, prognostic, and treatment tools. To address the needs of researchers, health care providers, and eHealth developers for more information and practical tools to handle privacy and legal matters in eHealth, the Dutch national Digital Society Research Programme organized the "Mind Your Data: Privacy and Legal Matters in eHealth" conference. In this paper, we share the key take home messages from the conference based on the following five tradeoffs: (1) privacy versus independence, (2) informed consent versus convenience, (3) clinical research versus clinical routine data, (4) responsibility and standardization, and (5) privacy versus solidarity.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075039PMC
http://dx.doi.org/10.2196/17456DOI Listing

Publication Analysis

Top Keywords

privacy legal
12
legal matters
12
data privacy
8
matters ehealth
8
health care
8
privacy versus
8
privacy
6
ehealth
6
mind data
4
ehealth health
4

Similar Publications

Recent discoveries indicating that the brain retains its ability to adapt and change throughout life have sparked interest in cognitive training (CT) as a possible means to postpone the development of dementia. Despite this, most research has focused on confirming the efficacy of training outcomes, with few studies examining the correlation between performance and results across various stages of training. In particular, the relationship between initial performance and the extent of improvement, the rate of learning, and the asymptotic performance level throughout the learning curve remains ambiguous.

View Article and Find Full Text PDF

Social Media Recruitment as a Potential Trigger for Vulnerability: Multistakeholder Interview Study.

JMIR Hum Factors

December 2024

Institute of History and Ethics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Ismaningerstr. 22, Munich, 81675, Germany, 49 89 4140 4041.

Background: More clinical studies use social media to increase recruitment accrual. However, empirical analyses focusing on the ethical aspects pertinent when targeting patients with vulnerable characteristics are lacking.

Objective: This study aims to explore expert and patient perspectives on vulnerability in the context of social media recruitment and seeks to explore how social media can reduce or amplify vulnerabilities.

View Article and Find Full Text PDF

Background: Newborn screening is a public health system designed to identify infants at risk for conditions early in life to facilitate timely intervention and treatment to prevent or mitigate adverse health outcomes. Newborn screening programs use tandem mass spectrometry as a platform to detect several treatable inborn errors of metabolism, and the T-cell receptor excision circle assay to detect some inborn errors of the immune system. Recent advancements in DNA sequencing have decreased the cost of sequencing and allow us to consider DNA sequencing as an additional platform to complement other newborn screening methods.

View Article and Find Full Text PDF

Healthcare workers safety: a cohort study using healthcare utilisation databases on vaccination and vaccine timeliness impact against SARS-CoV-2 infection.

Sci Rep

January 2025

Center of Epidemiology, Biostatistics and Medical Information Technology, Department of Biomedical Sciences and Public Health, Università Politecnica delle Marche, Ancona, 60126, Italy.

Healthcare Workers (HCWs) are at ongoing risk of SARS-CoV-2 infection, potentially contributing to its transmission. This study assessed full vaccination and vaccination timeliness impact on SARS-CoV-2 infections among HCWs in Italy's Marche Region, using Healthcare Utilization Databases. We evaluated vaccination coverage and its associated factors.

View Article and Find Full Text PDF

Preserving privacy in healthcare: A systematic review of deep learning approaches for synthetic data generation.

Comput Methods Programs Biomed

December 2024

Data Science and Artificial Intelligence Lab, Singapore General Hospital, Singapore. Electronic address:

Background: Data sharing in healthcare is vital for advancing research and personalized medicine. However, the process is hindered by privacy, ethical, and legal challenges associated with patient data. Synthetic data generation emerges as a promising solution, replicating statistical properties of real data while enhancing privacy protection.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!