Ethical dimensions of clinical data sharing by US healthcare organizations for purposes beyond direct patient care: Interviews with healthcare leaders.

Objectives: Empirically investigate current practices and analyze ethical dimensions of clinical data sharing by healthcare organizations for uses other than treatment, payment, and operations. Make recommendations to inform research and policy for healthcare organizations to protect patients' privacy and autonomy when sharing data with unrelated third parties.

Methods: Semi-structured interviews and surveys involving 24 informatics leaders from 22 US healthcare organizations, accompanied by thematic and ethical analyses.

What Do We Mean by Sharing of Patient Data? DaSH: A Data Sharing Hierarchy of Privacy and Ethical Challenges.

Background:  Clinical data sharing is common and necessary for patient care, research, public health, and innovation. However, the term "data sharing" is often ambiguous in its many facets and complexities-each of which involves ethical, legal, and social issues. To our knowledge, there is no extant hierarchy of data sharing that assesses these issues.

The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective.

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data.

Machine Learning-Based Critical Congenital Heart Disease Screening Using Dual-Site Pulse Oximetry Measurements.

Background: Oxygen saturation (Spo) screening has not led to earlier detection of critical congenital heart disease (CCHD). Adding pulse oximetry features (ie, perfusion data and radiofemoral pulse delay) may improve CCHD detection, especially coarctation of the aorta (CoA). We developed and tested a machine learning (ML) pulse oximetry algorithm to enhance CCHD detection.