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. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility. METHODS: Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two. RESULTS: All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores. CONCLUSIONS: As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

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. Methods: A search of PubMed and Embase databases for relevant peer-reviewed articles on development of an XAI model using clinical data and evaluating explanation effectiveness published between January 1, 2011, and April 30, 2022, was conducted. All retrieved papers were screened independently by the two authors. Relevant papers were also reviewed for identification of the essential properties of XAI (e.g., stakeholders and objectives of XAI, quality of personalized explanations) and the measures of explanation effectiveness (e.g., mental model, user satisfaction, trust assessment, task performance, and correctability). Results: Six out of 882 articles met the criteria for eligibility. Artificial Intelligence (AI) users were the most frequently described stakeholders. XAI served various purposes, including evaluation, justification, improvement, and learning from AI. Evaluation of the quality of personalized explanations was based on fidelity, explanatory power, interpretability, and plausibility. User satisfaction was the most frequently used measure of explanation effectiveness, followed by trust assessment, correctability, and task performance. The methods of assessing these measures also varied. Conclusion: XAI research should address the lack of a comprehensive and agreed-upon framework for explaining XAI and standardized approaches for evaluating the effectiveness of the explanation that XAI provides to diverse AI stakeholders.

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. Workplace violence cases were identified based on violence-related mentions in nursing records. Workplace violence was predicted using various factors related to emergency department visit and stay. RESULTS: The dataset included 1215 workplace violence cases and 6044 nonviolence cases. Random Forest showed the best performance among the algorithms adopted in this study. Workplace violence was predicted with higher accuracy when both ED visit and ED stay factors were used as predictors (0.90, 95% confidence interval 0.898-0.912) than when only ED visit factors were used. When both ED visit and ED stay factors were included for prediction, the strongest predictor of risk of WPV was patient dissatisfaction, followed by high average daily length of stay, high daily number of patients, and symptoms of psychiatric disorders. DISCUSSION: This study showed that workplace violence could be predicted with previous data regarding ED visits and stays documented in electronic health records. Timely prediction and mitigation of workplace violence could improve the safety of emergency nurses and the quality of nursing care. To prevent workplace violence, emergency nurses must recognize and continuously observe the risk factors for workplace violence from admission to discharge.

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. RESULTS: The logistic regression (AUROC 0.85) models showed the best performance, followed by random forest (AUROC 0.83) and Naive Bayes (AUROC 0.83). The logistic regression model indicated that fewer consultations, the highest acuity level, need for an emergency operation or angiography, need for ICU admission, severe emergency disease and fewer diagnoses were the statistically significant predictors for Emergency Department Length of Stay of 6 h or less. CONCLUSIONS: The transferred critically ill patients analysed in this study who required immediate or specialized care tended to receive needed care on time at the study site. IMPLICATIONS FOR NURSING MANAGEMENT: Understanding the factors affecting the Emergency Department Length of Stay of transferred critically ill patients is crucial for developing strategies to manage the nursing resource of Emergency Department successfully.

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. Interventions for a novel system load-balancing approach included a data-driven patient flow tracking informatics system, adding medical specialists, point-of-care creatinine testing (when required before diagnostic imaging) with dedicated imaging test slots for emergency patients, and introducing patient flow managers. Records of adult patients visiting the emergency department from January 2016 to March 2020 were included. Outcomes were ED length of stay and ED bed occupancy. Regression discontinuity analysis of an interrupted time series was used adjusting for seasonality and the number of patients per staff. RESULTS: A total of 46,494 patients in the preintervention period and 151,802 patients in the postintervention period were included. After the intervention, ED length of stay decreased by 4.07 hours, whereas the slope indicated a return to preintervention levels over time. Monthly average ED bed occupancy decreased by 34.6%, and the slope remained consistent over time. DISCUSSION: The multimodal quality improvement intervention that included a patient flow manager was an effective intervention to reduce the ED length of stay and the ED bed occupancy at the study site. The change for length of stay may not sustain over time without further intervention.

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. To examine which parameters are critical in NP cytotoxicity studies, we have chosen to examine two NP types (liposomes and quantum dots) at different concentrations incubated with two primary vascular endothelial cells, HUVEC and HMVEC-C for a standard time of 24 h. We paid close attention to the effects of positive controls and cell association on interpretation of cytotoxicity data. Various cellular responses (ATP content, oxidative stress, mitochondrial toxicity, and phospholipidosis) were measured in parallel. Interestingly, cell association data varied significantly with the different image analyses. However, cytotoxicity responses could all be correlated with exposure concentration. Cell type did have an effect on cytotoxicity reports. Most significantly, NP cytotoxicity results varied with the inclusion or exclusion of positive controls. In the absence of positive controls, one tends to emphasize small changes in cell responses to NPs.