Exploring the tradeoff between data privacy and utility with a clinical data analysis use case.

Background: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward.

Essential properties and explanation effectiveness of explainable artificial intelligence in healthcare: A systematic review.

Background: Significant advancements in the field of information technology have influenced the creation of trustworthy explainable artificial intelligence (XAI) in healthcare. Despite improved performance of XAI, XAI techniques have not yet been integrated into real-time patient care.

Objective: The aim of this systematic review is to understand the trends and gaps in research on XAI through an assessment of the essential properties of XAI and an evaluation of explanation effectiveness in the healthcare field.

Predicting Workplace Violence in the Emergency Department Based on Electronic Health Record Data.

Introduction: Emergency departments are extremely vulnerable to workplace violence, and emergency nurses are frequently exposed to workplace violence. We developed workplace violence prediction models using machine learning methods based on data from electronic health records.

Methods: This study was conducted using electronic health record data collected between January 1, 2016 and December 31, 2021.

Factors affecting the length of stay in the emergency department for critically Ill patients transferred to regional emergency medical center.

Aim: To identify the factors affecting Emergency Department Length of Stay for transferred critically ill patients.

Background: The Length of Stay of the transferred patients is an important indicator of Emergency Department service quality; thus, understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is essential.

Methods: Using the electronic medical records of 968 transferred critically ill Emergency Department patients of a tertiary hospital in Korea, prediction models for Emergency Department Length of Stay were built using various machine learning algorithms.

Multimodal Quality Improvement Intervention With Dedicated Patient Flow Manager to Reduce Emergency Department Length of Stay and Occupancy: Interrupted Time Series Analysis.

Introduction: Crowding in the emergency department is a problem worldwide that can affect patient safety and clinical outcomes. The aim of this project was to evaluate a multimodal quality improvement intervention with a new patient flow manager to reduce ED length of stay and ED bed occupancy.

Methods: This single-site interrupted time-series analysis study was conducted in a tertiary hospital emergency department in South Korea.

Understanding and improving assays for cytotoxicity of nanoparticles: what really matters?

Despite years of excellent individual studies, the impact of nanoparticle (NP) cytotoxicity studies remains limited by inconsistent data collection and analysis. It is often unclear how exposure conditions can be used to determine cytotoxicity quantitatively. Discrepancies due to using different measurement conditions, readouts and controls to characterize NP interactions with cells lead to further challenges.