The medical profession is steeped in traditions that guide its practice. These traditions were developed to preserve the well-being of patients. Transformations in science, technology, and society, while maintaining a self-governance structure that drives the goal of care provision, have remained hallmarks of the profession. The purpose of this paper is to examine ethical challenges in health care as it relates to Big Data, Accountable Care Organizations, and Health Care Predictive Analytics using the principles of biomedical ethics laid out by Beauchamp and Childress (autonomy, beneficence, non-maleficence, and justice). Among these are the use of Electronic Health Records within stipulations of the Health Insurance Portability and Accountability Act. Clinicians are well-positioned to impact health policy development to address ethical issues associated with the use of Big Data, Accountable Care, and Health Care Predictive Analytics as we work to transform the doctor-patient relationship towards improving population health outcomes and creating a healthier society.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1007/s10730-019-09377-5 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Cross-Country Inequalities in Disease Burden and Quality of Care of Stroke, 1990-2021: A Systematic Analysis of the Global Burden of Disease Study 2021.
Eur J Neurol
February 2025
Nuffield Department of Women's and Reproductive Health, Medical Science Division, University of Oxford, Oxford, UK.
Background And Purpose: This study aims to assess the disease burden and care quality along with cross-country inequalities for stroke at global, regional, and national levels from 1990 to 2021.
Methods: Data on stroke were extracted from the Global Burden of Disease (GBD) study 2021 for the globe, five sociodemographic index (SDI) regions, 21 GBD regions, and 204 countries/territories. The disease burden was quantified using the age-standardized disability-adjusted life years rate (ASDR).
Augmenting Circadian Biology Research With Data Science.
J Biol Rhythms
January 2025
Shiu Chien-Gene Lay Department of Bioengineering, University of California, San Diego, La Jolla, California.
The nature of biological research is changing, driven by the emergence of big data, and new computational models to parse out the information therein. Traditional methods remain the core of biological research but are increasingly either augmented or sometimes replaced by emerging data science tools. This presents a profound opportunity for those circadian researchers interested in incorporating big data and related analyses into their plans.
View Article and Find Full Text PDFRecognizing drivers' sleep onset by detecting slow eye movement using a parallel multimodal one-dimensional convolutional neural network.
Comput Methods Biomech Biomed Engin
January 2025
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, Changzhou University, Changzhou, P.R. China.
Slow eye movements (SEMs) are a reliable physiological marker of drivers' sleep onset, often accompanied by EEG alpha wave attenuation. A parallel multimodal 1D convolutional neural network (PM-1D-CNN) model is proposed to classify SEMs. The model uses two parallel 1D-CNN blocks to extract features from EOG and EEG signals, which are then fused and fed into fully connected layers for classification.
View Article and Find Full Text PDFRisk of all-cause mortality by various cigarette smoking indices: A longitudinal study using the Korea National Health Examination Baseline Cohort in South Korea.
Tob Induc Dis
January 2025
Institute of Health and Environment, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.
Introduction: Smoking behaviors can be quantified using various indices. Previous studies have shown that these indices measure and predict health risks differently. Additionally, the choice of measure differs depending on the health outcome of interest.
View Article and Find Full Text PDFPrediction model of middle school student performance based on MBSO and MDBO-BP-Adaboost method.
Front Big Data
January 2025
School of Information Science and Technology, Shihezi University, Xinjiang, China.
Predictions of student performance are important to the education system as a whole, helping students to know how their learning is changing and adjusting teachers' and school policymakers' plans for their future growth. However, selecting meaningful features from the huge amount of educational data is challenging, so the dimensionality of student achievement features needs to be reduced. Based on this motivation, this paper proposes an improved Binary Snake Optimizer (MBSO) as a wrapped feature selection model, taking the Mat and Por student achievement data in the UCI database as an example, and comparing the MBSO feature selection model with other feature methods, the MBSO is able to select features with strong correlation to the students and the average number of student features selected reaches a minimum of 7.
View Article and Find Full Text PDFWant 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!