Beyond the HIPAA Privacy Rule: Enhancing Privacy, Improving Health Through Research. Washington, DC: National Academies Press

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Split learning for health: Distributed deep learning without sharing raw patient data (2018, background): “Collaboration in health is heavily impeded by lack of trust, data sharing regulations such as HIPAA [3, 4, 5, 6, 7] and limited consent of patients.”
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks (2017, background): “This process limits timely opportunities to use data and may slow advances in biomedical knowledge and patient care (Gostin et al., 2009).”
No Peek: A Survey of private distributed deep learning (2018, background): “They also are unable to share it with external entities (data cannot physically leave the premises) due to limited consent of the patients, and regulations such as HIPAA [5,1,6,7,8] that prevent sharing many aspects of the data to external entities.”
Using Electronic Health Records for Population Health Research: A Review of Methods and Applications (2015, background): “Population health research that relies on protected health information (PHI) may risk violating individuals’ privacy rights (90).”
Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (2013, background): “Privacy is a highly important societal and personal value, but the current formulation and interpretation of this rule not only offer limited protection to patients, but also may impede the broader health research enterprise (IOM, 2009a).”