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.

Evaluating the validity of the nursing statements algorithmically generated based on the International Classifications of Nursing Practice for respiratory nursing care using large language models.

Objective: This study aims to facilitate the creation of quality standardized nursing statements in South Korea's hospitals using algorithmic generation based on the International Classifications of Nursing Practice (ICNP) and evaluation through Large Language Models.

Materials And Methods: We algorithmically generated 15 972 statements related to acute respiratory care using 117 concepts and concept composition models of ICNP. Human reviewers, Generative Pre-trained Transformers 4.

Quality of Person-Generated Healthy Walking Data: An Explorative Analysis.

Despite the potential benefits of Person Generated Health Data (PGHD), data quality issues impede its use. This study examined the effect of different methods for filtering armband data on determining the amount of healthy walking and the consistency between healthy walking captured using armbands and health diaries. Four weeks of armband and health diary data were acquired from 103 college students.

Educational Needs Assessment for Family Caregivers of Dementia Patients.

People living with dementia are highly dependent on caregivers. We conducted an online survey with regard to caregivers' educational experiences, needs, and expectations. We found that most of the participants lacked educational experiences and expected updated methods through metaverse in virtual reality.

Attitudes and perspectives of nurses and physicians in South Korea towards the clinical use of person-generated health data.

This study aimed to explore the adoption of person-generated health data in clinical settings and discern the factors influencing clinicians' willingness to use it. A web-based survey containing 48 questions was developed based on prior research and the Unified Theory of Acceptance and Use of Technology 2 model. The survey was administered to a convenience sample of 486 nurses and physicians in South Korea recruited through an online community and snowball sampling.

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.

From intention to action: The factors affecting health data sharing intention and action.

Introduction: Effective prevention and treatment of diseases requires utilization of health-related lifestyle data, which has thus become increasingly important. According to some studies, participants were willing to share their health data for use in medical care and research. Although intention does not always accurately reflect action, few studies have examined the question of whether data-sharing intention leads to data-sharing action.

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.

The Contribution of Material, Behavioral, Psychological, and Social-Relational Factors to Income-Related Disparities in Cardiovascular Risk Among Older Adults.

Background: Understanding the factors underlying health disparities is vital to developing strategies to improve health equity in old age. Such efforts should be encouraged in Korea.

Objective: This study explored how material, behavioral, psychological, and social-relational factors contribute to income-related disparities in cardiovascular risk among Korean adults 65 years and older.

Developing a Dietary Lifestyle Ontology to Improve the Interoperability of Dietary Data: Proof-of-Concept Study.

Background: Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data.

Health-Related Symptoms and Working Conditions on Vulnerability to Presenteeism Among Nurses in South Korea.

Presenteeism among nurses is reported to be higher than that of other professional workers, and affects one's health and the safety of their patients. Therefore, study on correlation between complex working condition and presenteeism is needed among nurses. This study aimed to predict characteristics of high-risk groups for presenteeism among nurses.

A meta-analysis on predictors of turnover intention of hospital nurses in South Korea (2000-2020).

Aims: To identify the predictors of Registered Nurses' turnover intention and analyse the effect sizes.

Design: A systematic review and meta-analysis of previous research, conducted to comprehensively identify the predictors of turnover intention.

Methods: In total, 417 studies from 1 January 2000 to 30 April 2020 that investigated predictors of turnover intention of South Korean nurses were reviewed.

A 10-year trend in income disparity of cardiovascular health among older adults in South Korea.

Objectives: Although cardiovascular disease (CVD) risk has lessened in Korea, it is unclear whether older adults in all socioeconomic strata have benefited equally. This study explored trends in income disparities in CVD risk among older adults in Korea.

Methods: This was a secondary analysis of Korean National Health and Nutrition Examination Survey data (2008-2017), targeting 14,836 older adults (≥65 years).

A technology-based information and coaching/support program and self-efficacy of Asian American breast cancer survivors.

Women's self-efficacy for coping with breast cancer is one of the key factors that lead to successful breast cancer survivorship. Due to the cultural stigma linked to breast cancer (e.g.

Factors Related to Presenteeism among South Korean Workers Exposed to Workplace Psychological Adverse Social Behavior.

Presenteeism negatively affects both individuals and society. This study identified factors of presenteeism among workers in South Korea, especially in relation to exposure to adverse social behaviors. Here, an adverse social behavior refers to any forms of workplace violence or intimidation.

Ambiguity in Communicating Intensity of Physical Activity: Survey Study.

Background: Communicating physical activity information with sufficient details, such as activity type, frequency, duration, and intensity, is vital to accurately delineate the attributes of physical activity that bring positive health impact. Unlike frequency and duration, intensity is a subjective concept that can be interpreted differently by people depending on demographics, health status, physical fitness, and exercise habits. However, activity intensity is often communicated using general degree modifiers, degree of physical exertion, and physical activity examples, which are the expressions that people may interpret differently.

Optimization of Nursing-Specific Flu Alerts.

Clinical decision support interventions, such as alerts and reminders, can improve clinician compliance with practice guidelines and patient outcomes. Alerts that trigger at inappropriate times are often dismissed by clinicians, reducing desired actions rather than increasing them. A set of nursing-specific alerts related to influenza screening and vaccination were optimized so that they would "trigger" less often but function adequately to maintain institutional flu vaccination compliance.

Representing Nursing Data With Fast Healthcare Interoperability Resources: Early Lessons Learned With a Use Case Scenario on Home-Based Pressure Ulcer Care.

Healthcare communities are rapidly embracing Health Level 7's Fast Healthcare Interoperability Resources standard as the next-generation messaging protocol to facilitate data interoperability. Implementation-friendly formats for data representation and compliance to widely adopted industry standards are among the strengths of Fast Healthcare Interoperability Resources that are accelerating its wide adoption. Research confirms the advantages of Fast Healthcare Interoperability Resources in increasing data interoperability in mortality reporting, genetic test sharing, and patient-generated data.

Patient Perspectives About Decisions to Share Medical Data and Biospecimens for Research.

Importance: Patients increasingly demand transparency in and control of how their medical records and biospecimens are shared for research. How much they are willing to share and what factors influence their sharing preferences remain understudied in real settings.

Objectives: To examine whether and how various presentations of consent forms are associated with differences in electronic health record and biospecimen sharing rates and whether these rates vary according to user interface design, data recipients, data and biospecimen items, and patient characteristics.

Developing a Physical Activity Ontology to Support the Interoperability of Physical Activity Data.

Background: Physical activity data provides important information on disease onset, progression, and treatment outcomes. Although analyzing physical activity data in conjunction with other clinical and microbiological data will lead to new insights crucial for improving human health, it has been hampered partly because of the large variations in the way the data are collected and presented.

Objective: The aim of this study was to develop a Physical Activity Ontology (PACO) to support structuring and standardizing heterogeneous descriptions of physical activities.

Selecting Optimal Subset to release under Differentially Private M-estimators from Hybrid Datasets.

Privacy concern in data sharing especially for health data gains particularly increasing attention nowadays. Now some patients agree to open their information for research use, which gives rise to a new question of how to effectively use the public information to better understand the private dataset without breaching privacy. In this paper, we specialize this question as selecting an optimal subset of the public dataset for M-estimators in the framework of differential privacy (DP) in [1].

Data discovery with DATS: exemplar adoptions and lessons learned.

The DAta Tag Suite (DATS) is a model supporting dataset description, indexing, and discovery. It is available as an annotated serialization with schema.org, a vocabulary used by major search engines, thus making the datasets discoverable on the web.