Informatics Competencies of Students in a Doctor of Nursing Practice Program: A Descriptive Study.

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. METHODS: An online informatics course was offered to students enrolled in a Bachelor of Science in Nursing to DNP program, and their informatics competency, which includes three subscales, was evaluated. Online survey data were collected from Fall 2021 to Fall 2022 using the "Self-Assessment of Informatics Competency Scale for Health Professionals." RESULTS: An analysis of 127 student responses revealed that students demonstrated competence in overall informatics competency and in one subscale: "applied computer skills (clinical informatics)." They showed proficiency in the "basic computer skills" and the "role" subscales. However, they reported lower competency in managing data and integrating standard terminology into their practice. CONCLUSIONS: The findings offer detailed insights into the current informatics competencies of DNP students and can inform informatics educators on how to enhance their courses. As healthcare institutions increasingly depend on AI applications, it is imperative for informatics educators to include AI-related content in their curricula.

Artificial intelligence assisted telehealth for nursing: A scoping review.

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. OBJECTIVES: This scoping review focuses on finding users' satisfaction and perception of AI-assisted telehealth intervention, performances of AI algorithms, and the types of AI technology used. METHODS: A structured search was performed in six databases, PubMed, CINAHL, Web of Science, OVID, PsycINFO, and ProQuest, following the guidance of the Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews. The quality of the final reviewed studies was assessed using the Medical Education Research Study Quality Instrument. RESULTS: Eight of the 41 studies published between 2017 and 2022 were included in the final review. Six studies were conducted in the United States, one in Japan, and one in South Korea. Four studies collected data from participants (n = 3014). Two studies used image data (n = 1986), and two used sensor data from smart homes to detect patients' health events for nurses (n = 35). The quality of studies implied moderate to high-quality study (mean = 10.1, range = 7.7-13.7). Two studies reported high user satisfaction, three assessed user perception of AI in telehealth, and only one showed high AI acceptability. Two studies revealed the high performance of AI algorithms. Five studies used machine learning algorithms. CONCLUSIONS: AI-assisted telehealth interventions were efficient and promising and could be an effective care delivery method in nursing.

Faculty Driven Virtual Reality (VR) Scenarios and Students Perception of Immersive VR in Nursing Education: A Pilot Study.

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. We formed a focus group composed of nursing faculty (N=10) with expertise in the clinical setting and simulation. Faculty participants identified important topics and aspects of immersive VR scenarios during focus group discussions. We assessed nursing student participants' (N=11) perception of immersive VR in nursing education using a VR game (Anatomy Explorer 2020). Most student participants indicated that a VR game was immersive and realistic and recommended using immersive VR to learn clinical nursing skills. Realistic immersive VR clinical education scenarios could result in effective clinical nursing education.

Effectiveness of Immersive Virtual Reality in Nursing Education: Systematic Review.

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. METHODS: A structured search was performed in PubMed, Cumulative Index to Nursing and Allied Health Literature, EMBASE, PsycINFO, Web of Science, and ProQuest Nursing & Allied Health Database. The Medical Education Research Study Quality Instrument was used to assess the quality of studies. RESULTS: The final review was composed of 9 studies of moderate to high quality published between 2018 and 2020. The review indicated that immersive VR increased learning, cognitive, and psychomotor performance. Most participants of the studies preferred using immersive VR in education and with a variety of experiential scenarios. Barriers were associated with technological hardware and software (eg, simulation sickness, lack of visual comfort). CONCLUSION: The review supports the viability of immersive VR technology in nursing education.

Nursing Informatics Competency After Experiencing Simulated Electronic Health Records: Descriptive Study.

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. An assignment using DocuCare to learn core concepts of nursing informatics was developed for graduate students enrolled in a nursing informatics course. A validated standard informatics survey instrument was used to measure three individual domains (basic computer skills, roles, and advanced computer skills (clinical informatics)) as well as the combined informatics competency level. Overall, the nursing informatics competency level was between competent and proficient; basic computer skills and roles were above proficient; and advanced computer skills (clinical informatics) fell between competence and proficient levels.

Website for Women Who Abuse Opioids: What Are the Essential Components?

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.

Exploring Impact of Marijuana (Cannabis) Abuse on Adults Using Machine Learning.

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. The purpose of this study was to explore the characteristics of adults who abuse marijuana (20-49 years old) and analyze behavior and social relation variables related to depression and suicide risk using machine-learning algorithms. A total of 698 participants were identified from the 2019 National Survey on Drug Use and Health survey as marijuana dependent in the previous year. Principal Component Analysis and Chi-square were used to select features (variables) and mean imputation method was applied for missing data. Logistic regression, Random Forest, and K-Nearest Neighbor machine-learning algorithms were used to build depression and suicide risk prediction models. The results showed unique characteristics of the group and well-performing prediction models with influential risk variables. Identified risk variables were aligned with previous studies and suggested the development of marijuana abuse prevention programs targeting 20-29 year olds with a regular depression and suicide screening. Further study is suggested for identifying specific barriers to receiving timely treatment for depression and suicide risk.

Machine Learning-Based Nicotine Addiction Prediction Models for Youth E-Cigarette and Waterpipe (Hookah) Users.

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage and Selection Operator (LASSO). ReliefF identified important predictor variables, and the Davies-Bouldin clustering evaluation index selected the optimal number of predictors for Random Forest. A total of 193 predictor variables were included in the final analysis. Performance of prediction models was measured by Root Mean Square Error (RMSE) and Confusion Matrix. The results suggested high performance of prediction. Identified predictor variables were aligned with previous research. The noble predictors found, such as 'witnessed e-cigarette use in their household' and 'perception of their tobacco use', could be used in public awareness or targeted e-cigarette and hookah youth education and for policymakers.

Impact of Simulated Electronic Health Records on Informatics Competency of Students in Informatics Course.

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. METHODS: A two-group independent measures study design was adopted to assess students' perception of a simulated EHR while comparing differences in informatics competencies between an intervention group and a control group. A simulated EHR assignment was provided to students in the intervention group, and a paper assignment was provided to those in the control group. The informatics competency of the students was measured using the Self-Assessment of Informatics Competency Scale for Health Professionals (SICS). RESULTS: Students who were enrolled in a family nurse practitioner program in fall of 2019 participated in this study (n = 39). The students expressed positive perceptions of a simulated EHR experience. The SICS results indicated that students in the intervention (simulated EHR) group showed higher informatics competency than those in the control group. CONCLUSIONS: The positive results of this study support incorporating simulated EHR exercises in online courses. Higher informatics competency in the intervention group implies that the use of simulated EHR facilitated learning of complicated informatics concepts.

Steps to Develop a Mobile App for Pain Assessment of Cancer Patients: A Usability Study.

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. These tasks were then converted to activity diagrams and became the logic of PainSmart. Five participants diagnosed with breast cancer evaluated usability of PainSmart with the System Usability Scale. Patient experience was recorded using Camtasia Studio Version 9 software. The five participants were able to explore the pain app after only 20 minutes of training. Using the System Usability Scale with comments, participant mean usability score was 77.5; above 68 is considered an above average system. A prototype of a pain assessment mobile app for cancer patients demonstrated high usability and will be refined based on participant feedback.

Hand hygiene of nursing and midwifery students in Cambodia.

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. Cambodia is a developing country in Southeast Asia and data regarding hand hygiene are limited. METHODS: A cross-sectional descriptive study was conducted to assess the level of knowledge, attitude and practice of hand hygiene. Students in nursing and midwifery programmes were the target population (n = 300). Survey data were collected from January to May 2017. Descriptive statistics, t-tests and correlation coefficients were calculated to assess relationships between student knowledge, attitude and hand hygiene practice. RESULT: The level of knowledge, attitude and practice of hand hygiene in nursing and midwifery students was moderate. A majority of students had received hand hygiene training. There was no significant difference between nursing and midwifery students in knowledge, attitude and practice of hand hygiene. However, a Pearson correlation of attitude and practice had a weak positive relationship. CONCLUSION: Both nursing and midwifery students demonstrated moderate levels of knowledge, attitudes and practice of hand hygiene. IMPLICATION FOR NURSING AND HEALTH POLICY: This study identified a need for hand hygiene training. Developing training programmes to improve attitudes about hand hygiene is strongly recommended. Effective training may contribute to change behaviours of hand hygiene (attitude), improve practice and ultimately reduce hospital-acquired infections.

A Framework Facilitates Development of a Mobile App.

Due to the advance of mobile technology, use of mobile devices such as smartphones has been growing rapidly in the U.S. and worldwide. Many health related mobile apps have been developed for care professionals and patients as well. However, studies indicate that there is a gap between science and commercially developed mobile apps. In addition to this, there is no framework to guide devlopment of a mobile app to overcome many challenges and obstacles. Through the development of a pain management mobile app for cancer patients, a framework was developed. A proposed framework was based on the technology acceptance model, ensuring usefulness and ease of use for end users. The first two steps in the framework enforce accurate knowledge translation and communication to build a science based mobile app. Iterative decisions points between steps enhance accomplishment of each step.

Predicting Depression Among Community Residing Older Adults: A Use of Machine Learning Approch.

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 58.33% to 90.48% and 83.33% to 90.44% in the model with and without L1 regularization, respectively. The moderate to high prediction rates imply that the machine learning algorithms built the prediction models successfully.

Decision-making about prenatal genetic testing among pregnant Korean-American women.

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.S.). All participants were born in Korea and had immigrated to the U.S. The number of years living in the U.S. ranged from 4 to 11 (M=5.7). SETTING: various regional areas of the U.S. MEASUREMENTS AND FINDINGS: the researchers conducted face-to-face or phone interviews using semi-structured interview guides. The interviews were conducted in the Korean language and lasted approximately 50-100minutes. The interview guides focused on the decision-making process and experiences with prenatal genetic testing, as well as reflections on the decisions. Four core themes emerged related to the participants' decision-making processes, according to their descriptions. These themes are (1) facing the challenges of decision-making, (2) seeking support, (3) determining one's preferred role in the decision-making process, and (4) feeling uncomfortable with the degree of patient autonomy in U.S. health care. KEY CONCLUSION: researchers concluded that many distinctive factors influence the decision-making processes used by pregnant Korean-American women. The results have the potential to improve shared decision-making practices regarding prenatal genetic testing. IMPLICATIONS FOR PRACTICE: clinicians need to understand the sociocultural underpinnings of pregnant Korean-American immigrants regarding prenatal genetic screening and testing as an initial step to engage these patients in shared decision-making.

Explorative Analyses of Nursing Research Data.

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. However, values of many metadata items of the nursing studies were not sufficiently represented through the bioCADDIE metadata. This was partly due to the differences in the scope of the content that the bioCADDIE metadata are designed to represent. The 194 nursing studies reported a total of 1,181 unique data items, the majority of which take non-numeric values. This indicates the importance of data standardization to enable the integrative analyses of these data to support Big Data science in nursing.

Enhancing Patient Safety Using Clinical Nursing Data: A Pilot Study.

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. This pilot study developed computer algorithms based on a falls prevention protocol and programmed the prototype NPRIMS. It successfully measured the performance of nursing care delivered and its impact on patient outcomes using clinical nursing data from the study site. Results of the study revealed that NPRIMS has the potential to pinpoint components of nursing processes that are in need of improvement for preventing patient from falls.

Feasibility of Representing Data from Published Nursing Research Using the OMOP Common Data Model.

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. Although the majority of the data could be represented in the OMOP CDM, potential information loss was identified in contents related to patient reported outcomes, socio-economic information, and locally developed nursing intervention protocols. These areas will be further investigated in a follow up study. We will use lessons learned in this study to inform the metadata development efforts for data discovery.

Developing a Web-Based Nursing Practice and Research Information Management System: A Pilot Study.

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. This pilot study developed a computer algorithm based on a falls prevention protocol and programmed the prototype Web-based Nursing Practice and Research Information Management System. It successfully measured performance of nursing care delivered and its impact on patient outcomes successfully using clinical nursing data from the study site. Although Nursing Practice and Research Information Management System was tested with small data sets, results of study revealed that it has the potential to measure nurses' delivery of care and its impact on patient outcomes, while pinpointing components of nursing process in need of improvement.

Modeling a Nursing Guideline with Standard Terminology and Unified Modeling Language for a Nursing Decision Support System: A Case Study.

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. This results in limited use of DSSs in nursing. Unified modeling language (UML) is a software writing language known to accurately represent the end-users' perspective, due to its expressive characteristics. Furthermore, standard terminology enabled DSSs have been shown to smoothly integrate into existing health information systems. In order to facilitate development of nursing DSSs, the UML was used to represent a guideline for medication management for older adults encode with the International Classification for Nursing Practice (ICNP®). The UML was found to be a useful and sufficient tool to model a nursing guideline for a DSS.