Using random assignment to measure court accessibility for low-income divorce seekers.

We conducted a field experiment in which 311 low-income individuals seeking a divorce were randomly assigned to receive access to a pro bono lawyer (versus minimal help) to assist with filing for divorce. Examining court records, we found that assignment to an attorney made a large difference in whether participants filed for and obtained a divorce. Three years after randomization, 46% of the treated group had terminated their marriages in the proper legal venue, compared to 9% of the control group.

Regulatory responses to medical machine learning.

Companies and healthcare providers are developing and implementing new applications of medical artificial intelligence, including the artificial intelligence sub-type of medical machine learning (MML). MML is based on the application of machine learning (ML) algorithms to automatically identify patterns and act on medical data to guide clinical decisions. MML poses challenges and raises important questions, including (1) How will regulators evaluate MML-based medical devices to ensure their safety and effectiveness? and (2) What additional MML considerations should be taken into account in the international context? To address these questions, we analyze the current regulatory approaches to MML in the USA and Europe.

Privacy Gaps for Digital Cardiology Data: Big Problems With Big Data.

Mr. M is a 55 year-old man who suffers an acute myocardial infarction (MI) and undergoes coronary stenting. Following hospitalization, he completes cardiac rehabilitation.

Analysis of e-waste recycling behavior based on survey at a Midwestern US University.

The United States lacks a set of unified electronic waste recycling laws, contributing in part to the observed low rate of e-waste recycling behaviors among consumers. Individual factors of consumers contributing to the low recycling rates are not well understood. The objective of this study was to evaluate consumer behaviors, including barriers, surrounding e-waste recycling at a large Midwestern university in the United States.

Regulatory oversight, causal inference, and safe and effective health care machine learning.

In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S.

Constitutional Challenges to Liver Transplant Policy.

Background: Liver transplant candidacy determination can be contentious. When transplantation is declined for reasons perceived as violating fundamental rights or discriminating against a protected class-for example, age, race, religion, nationality-the case may involve a constitutional claim. Judicial review of such cases may result in decisions with sweeping implications for transplant policy.

Shadow health records meet new data privacy laws.

Large sets of health data can enable innovation and quality measurement but can also create technical challenges and privacy risks. When entities such as health plans and health care providers handle personal health information, they are often subject to data privacy regulation. But amid a flood of new forms of health data, some third parties have figured out ways to avoid some data privacy laws, developing what we call “shadow health records”—collections of health data outside the health system that provide detailed pictures of individual health—that allow both innovative research and commercial targeting despite data privacy rules.

Ineffective Homeschooling in a Child with a Learning Disability.

Charles is a 10-year-old African-American male who presents to the Developmental Behavioral Pediatrics Clinic for evaluation of his learning. His primary care provider (PCP) was concerned that his developmental delays were negatively affecting his ability to engage in his homeschooling curriculum and also that his mother seemed unaware of the severity of his delays. Neuropsychological evaluation had been recommended by the PCP several times in the past, but the family declined.

Privacy in the age of medical big data.

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy.

Big data and black-box medical algorithms.

New machine-learning techniques entering medicine present challenges in validation, regulation, and integration into practice.

Medical immunity, international law and just war theory.

Under customary international law, the First Geneva Convention and Additional Protocol I, medical personnel are protected against intentional attack. In § 1 of this paper, we survey these legal norms and situate them within the broader international humanitarian law framework. In § 2, we explore the historical and philosophical basis of medical immunity, both of which have been underexplored in the academic literature.

Including ELSI research questions in newborn screening pilot studies.

Background: The evidence review processes for adding new conditions to state newborn screening (NBS) panels rely on data from pilot studies aimed at assessing the potential benefits and harms of screening. However, the consideration of ethical, legal, and social implications (ELSI) of screening within this research has been limited. This paper outlines important ELSI issues related to newborn screening policy and practices as a resource to help researchers integrate ELSI into NBS pilot studies.