Nurses are responsible to protect the confidentiality and security of patients' health information. In the critical care setting, these privacy and confidentiality issues may be even more poignant. If able to carry on with their normal lives after discharge, many of the patients that nurses treat will have some sequelae from their illnesses that could affect their careers, finances, and personal lives. This article reviews the current literature, presents a discussion of confidentiality and security as it applies to uniquely identifiable health information, and offers some "best practices" that can be used in daily practice. Furthermore, the author discusses the Health Insurance Portability and Accountability Act of 1996 and details some reasons why the act is not fully implemented a full 6 years after it was signed into law.
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http://dx.doi.org/10.1097/00044067-200308000-00005 | DOI Listing |
Surv Methodol
December 2024
Department of Statistical Science, 214a Old Chemistry Building, Duke University, Durham, NC 27708-0251.
When seeking to release public use files for confidential data, statistical agencies can generate fully synthetic data. We propose an approach for making fully synthetic data from surveys collected with complex sampling designs. Our approach adheres to the general strategy proposed by Rubin (1993).
View Article and Find Full Text PDFData Min Knowl Discov
January 2025
CWI, Amsterdam, The Netherlands.
Missing values arise routinely in real-world sequential (string) datasets due to: (1) imprecise data measurements; (2) flexible sequence modeling, such as binding profiles of molecular sequences; or (3) the existence of confidential information in a dataset which has been deleted deliberately for privacy protection. In order to analyze such datasets, it is often important to replace each missing value, with one or more letters, in an efficient and effective way. Here we formalize this task as a combinatorial optimization problem: the set of constraints includes the of the missing value (i.
View Article and Find Full Text PDFAJOG Glob Rep
February 2025
Division of Complex Family Planning, Department of Obstetrics Gynecology and Reproductive Sciences, University of California San Diego, La Jolla, CA (Meurice, Kully, Averbach and Mody).
Background: Telemedicine contraception services have increased since the COVID-19 pandemic. There may be unique equity implications and language barriers for patients who speak Spanish.
Objective: To identify the barriers and facilitators of telemedicine for contraception care among patients who speak Spanish using a community-based participatory research approach.
BMC Nurs
January 2025
Nursing Department, Hamad Medical Corporation, Doha, P.O. Box 3050, Qatar.
Background: Artificial Intelligence (AI) is increasingly applied in healthcare to boost productivity, reduce administrative workloads, and improve patient outcomes. In nursing, AI offers both opportunities and challenges. This study explores nurses' perspectives on implementing AI in nursing practice within the context of Jordan, focusing on the perceived benefits and concerns related to its integration.
View Article and Find Full Text PDFIn the context of Chinese clinical texts, this paper aims to propose a deep learning algorithm based on Bidirectional Encoder Representation from Transformers (BERT) to identify privacy information and to verify the feasibility of our method for privacy protection in the Chinese clinical context. We collected and double-annotated 33,017 discharge summaries from 151 medical institutions on a municipal regional health information platform, developed a BERT-based Bidirectional Long Short-Term Memory Model (BiLSTM) and Conditional Random Field (CRF) model, and tested the performance of privacy identification on the dataset. To explore the performance of different substructures of the neural network, we created five additional baseline models and evaluated the impact of different models on performance.
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