By Jayson Marwaha and Tiffany Tuedor
On April 5, 2021, a quiet, but potentially transformative shift regarding patient access to data occurred.
Health systems are now required to provide patients with timely access to their own medical records, upon request. (Shockingly, this hasn’t been a requirement in the past.)
By Wendy Netter Epstein and Charlotte Tschider
This May, Google announced a new partnership with national hospital chain HCA Healthcare to consolidate HCA’s digital health data from electronic medical records and medical devices and store it in Google Cloud.
This move is the just the latest of a growing trend — in the first half of this year alone, there have been at least 38 partnerships announced between providers and big tech. Health systems are hoping to leverage the know-how of tech titans to unlock the potential of their treasure troves of data.
Health systems have faltered in achieving this on their own, facing, on the one hand, technical and practical challenges, and, on the other, political and ethical concerns.
By Rachele Hendricks-Sturrup
On April 21, 2021, the European Commission released a “first-ever” legal framework on artificial intelligence (AI) in an attempt to address societal risks associated with AI implementation.
The EU has now effectively set a global stage for AI regulation, being the first nation of member states to create a legal framework with specific intent to address or mitigate potentially harmful effects of broad AI implementation.
Within the proposed framework, the Commission touched on a variety of considerations and “high-risk” AI system scenarios. The Commission defined high-risk AI systems as those that pose significant (material or immaterial) risks to the health and safety or fundamental rights of persons.
This post outlines four key considerations in the proposal with regard to health: 1) prioritizing emergency health care; 2) law enforcement profiling as a social determinant of health; 3) immigrant health risk screening; and 4) AI regulatory sandboxes and a health data space to support AI product commercialization and public health innovation.
By Jenna Becker
Hospitals use artificial intelligence and machine learning (AI/ML) not only in clinical decision-making, but also to allocate scarce resources.
These resource allocation algorithms have received less regulatory attention than clinical decision-making algorithms, but nevertheless pose similar concerns, particularly with respect to their potential for bias.
Without regulatory oversight, the risks associated with resource allocation algorithms are significant. Health systems must take particular care when implementing these solutions.
By Jenna Becker
A data privacy lawsuit against the University of Chicago Medical Center and Google was recently dismissed, demonstrating the difficulty of pursuing claims against hospitals that share patient data with tech companies.
Patient data sharing between health systems and large software companies is becoming increasingly common as these organizations chase the potential of artificial intelligence and machine learning in healthcare. However, many tech firms also own troves of consumer data, and these companies may be able to match up “de-identified” patient records with a patient’s identity.
Scholars, privacy advocates, and lawmakers have argued that HIPAA is inadequate in the current landscape. Dinerstein v. Google is a clear reminder that both HIPAA and contract law are insufficient for handling these types of privacy violations. Patients are left seemingly defenseless against their most personal information being shared without their meaningful consent.
A recent US lawsuit highlights crucial challenges at the interface of data utility, patient privacy & data misuse
By Timo Minssen (CeBIL, UCPH), Sara Gerke & Carmel Shachar
Earlier, we discussed the new suit filed against Google, the University of Chicago (UC), and UChicago Medicine, focusing on the disclosure of patient data from UC to Google. This piece goes beyond the background to consider the potential impact of this lawsuit, in the U.S., as well as placing the lawsuit in the context of other trends in data privacy and security.
By Timo Minssen (CeBIL, UCPH), Sara Gerke & Carmel Shachar
A recent US lawsuit highlights crucial challenges at the interface of data utility, patient privacy & data misuse
The huge prospects of artificial intelligence and machine learning (ML), as well as the increasing trend toward public-private partnerships in biomedical innovation, stress the importance of an effective governance and regulation of data sharing in the health and life sciences. Cutting-edge biomedical research strongly demands high-quality data to ensure safe and effective health products. It is often argued that greater access to individual patient data collections stored in hospitals’ medical records systems may considerably advance medical science and improve patient care. However, as public and private actors attempt to gain access to such high-quality data to train their advanced algorithms, a number of sensitive ethical and legal aspects also need to be carefully considered. Besides giving rise to safety, antitrust, trade secrets, and intellectual property issues, such practices have resulted in serious concerns with regard to patient privacy, confidentiality, and the commitments made to patients via appropriate informed consent processes.
By Oliver Kim
May marks the annual Asian American and Pacific Islander Heritage Month, which recognizes the history and contributions of this diverse population in the United States. Accounting for that diversity though is one of the challenges facing the Asian American-Pacific Islander (AAPI) community: for example, the Library of Congress commemorative website recognizes that AAPI is a “rather broad term” that can include
all of the Asian continent and the Pacific islands of Melanesia (New Guinea, New Caledonia, Vanuatu, Fiji and the Solomon Islands), Micronesia (Marianas, Guam, Wake Island, Palau, Marshall Islands, Kiribati, Nauru and the Federated States of Micronesia) and Polynesia (New Zealand, Hawaiian Islands, Rotuma, Midway Islands, Samoa, American Samoa, Tonga, Tuvalu, Cook Islands, French Polynesia and Easter Island).
Understanding that diversity has huge policy and political implications, particularly in health policy. Read More