Publications by authors named "Jeeyae Choi"

Introduction: The rapid evolution of artificial intelligence (AI) technology has revolutionized healthcare, particularly through the integration of AI into health information systems. This transformation has significantly impacted the roles of nurses and nurse practitioners, prompting extensive research to assess the effectiveness of AI-integrated systems. This scoping review focuses on machine learning (ML) used in nursing, specifically investigating ML algorithms, model evaluation methods, areas of focus related to nursing, and the most effective ML algorithms.

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Nurses must excel in using Artificial Intelligence (AI) - applied hospital systems, making their informatics competency crucial. ChatGPT has been trained with extensive amounts of informatics- and technology-related health data and has gained popularity. Nurses could have the opportunity to enhance their informatics competency through the knowledge generated by ChatGPT.

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The advancement of technology and Artificial Intelligence applied health information systems demand high informatics competencies from nurses. To prepare nursing students to meet this demand, informatics courses are designed to increase informatics competencies. We offered an online informatics course to graduate students in a Nurse Educator program and assessed their informatics competency, including subdomains.

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Objectives: Health systems that apply artificial intelligence (AI) are transforming the roles of healthcare providers, including those of Doctor of Nursing Practice (DNP) providers. These professionals are required to utilize informatics knowledge and skills to deliver quality care, necessitating a high level of informatics competencies, which should be developed through well-structured courses. The purpose of this study is to assess the informatics competency scale scores of DNP students and to provide recommendations for enhancing the informatics curriculum.

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Innovative nursing education methods became essential due to the COVID-19 pandemic. Immersive Virtual Reality (VR) education offers nursing students authentic patient encounters in a realistic simulated environment. A pilot study was conducted to identify nursing education clinical scenarios that should be developed for immersive VR and to assess students' perception of immersive VR in education.

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Background: Due to the COVID-19 pandemic, telehealth resurfaced as a convenient efficient healthcare delivery method. Researchers indicate that Artificial Intelligence (AI) could further facilitate delivering quality care in telehealth. It is essential to find supporting evidence to use AI-assisted telehealth interventions in nursing.

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Studies indicate opioid prevention programs should be designed differently for men and women. Since a website is an effective means of providing behavioral interventions, tailoring one to meet the specific needs of women is a logical step in assisting them to overcome opioid misuse. We identified essential components for inclusion in a website following a community engagement project.

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Nursing has embraced flexible online advanced education as a means of increasing the supply of nurse professionals. Virtual simulation and other adaptive learning technologies enhance learning efficiency and student outcomes. A simulated electronic health record (DocuCare) was adopted in an online graduate nursing informatics course.

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Background: Digital education using immersive virtual reality (VR) technology is available in nursing. Evidence of its effectiveness is essential.

Purpose: This review analyzed the effectiveness of and barriers to using immersive VR in nursing education.

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Marijuana is the most common illicit substance globally. The rate of marijuana use is increasing in young adults in the US. The current environment of legalizing marijuana use is further contributing to an increase of users.

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Objectives: Nursing has embraced online education to increase its workforce while providing flexible advanced education to nurse professionals. Faculty use virtual simulation and other adaptive learning technologies to enhance learning efficiency and student outcomes in online courses. The purpose of this study was to assess the impact of simulated Electronic Health Records (EHRs) on informatics competency in a graduate online informatics course.

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Health-related mobile apps have the potential to allow patients and providers to proactively and responsibly manage pain together. However, there is a gap between the science of pain and current mobile apps. To develop a prototype science-based pain assessment mobile app (PainSmart) for Android smartphones, pain assessment tasks were extracted from a clinical guideline.

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Aim: To determine the relationship between knowledge, attitude and the practice of hand hygiene by nursing and midwifery students in Cambodia.

Background: Hand hygiene is the most cost-effective means to prevent hospital-acquired infections. Techniques of hand hygiene are simple; however, many researchers have found hand hygiene knowledge, attitude and practice to be poor in many healthcare settings worldwide, especially in developing countries.

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The study demonstrated an application of machine learning techniques in building a depression prediction model. We used the NSHAP II data (3,377 subjects and 261 variables) and built the models using a logistic regression with and without L1 regularization. Depression prediction rates ranged 58.

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Due to the advance of mobile technology, use of mobile devices such as smartphones has been growing rapidly in the U.S. and worldwide.

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Objective: to understand the prenatal genetic testing decision-making processes among pregnant Korean-American women.

Design: a qualitative, descriptive research design.

Participants: referrals and snowball sampling techniques were used to recruit 10 Korean-American women who had been recommended for amniocentesis during pregnancy in the United States (U.

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We explored the feasibility of representing nursing research data with the Observational Medical Outcomes Partners (OMOP) Common Data Model (CDM) to understand the challenges and opportunities in representing various types of health data not limited to diseases and drug treatments. We collected 1,431 unique data items from 256 nursing articles and mapped them to the OMOP CDM. A deeper level of mapping was explored by simulating 10 data search use cases.

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As a first step of pursuing the vision of "Big Data science in nursing," we described the characteristics of nursing research data reported in 194 published nursing studies. We also explored how completely the Version 1 metadata specification of biomedical and healthCAre Data Discovery Index Ecosystem (bioCADDIE) represents these metadata. The metadata items of the nursing studies were all related to one or more of the bioCADDIE metadata entities.

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To enhance patient safety from falls, many hospital information systems have been implemented to collect clinical data from the bedside and have used the information to improve fall prevention care. However, most of them use administrative data not clinical nursing data. This necessitated the development of a web-based Nursing Practice and Research Information Management System (NPRIMS) that processes clinical nursing data to measure nurses' delivery of fall prevention care and its impact on patient outcomes.

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Many hospital information systems have been developed and implemented to collect clinical data from the bedside and have used the information to improve patient care. Because of a growing awareness that the use of clinical information improves quality of care and patient outcomes, measuring tools (electronic and paper based) have been developed, but most of them require multiple steps of data collection and analysis. This necessitated the development of a Web-based Nursing Practice and Research Information Management System that processes clinical nursing data to measure nurses' delivery of care and its impact on patient outcomes and provides useful information to clinicians, administrators, researchers, and policy makers at the point of care.

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In recent years, Decision Support Systems (DSSs) have been developed and used to achieve "meaningful use". One approach to developing DSSs is to translate clinical guidelines into a computer-interpretable format. However, there is no specific guideline modeling approach to translate nursing guidelines to computer-interpretable guidelines.

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Accurate assessment and documentation of skin conditions facilitate communication among care providers and are critical to effective prevention and mitigation of pressure ulcer. We report developing a prototype mobile system called SAPPIRE (Skin Assessment for Pressure Ulcer Prevention, an Integrated Recording Environment) for an android device to assist nurses with skin assessment and documentation at bedside. SAPPIRE demonstrates (1) data documentation conforming to the relevant terminology standards, (2) data exchange using Continuity of Care Records (CCR) standard and (3) smart display of patient data relevant to risk parameters to promote accurate pressure ulcer risk assessment with the Braden scale.

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To provide best recommendations at the point of care, guidelines have been implemented in computer systems. As a prerequisite, guidelines are translated into a computer-interpretable guideline format. Since there are no specific tools to translate nursing guidelines, only a few nursing guidelines are translated and implemented in computer systems.

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