Machine Learning-Based Prediction Models of Mortality for Intensive Care Unit Patients Using Nursing Records.

This study aimed to develop ICU mortality prediction models using a conceptual framework, focusing on nurses' concerns reflected in nursing records from the MIMIC IV database. We included 46,693 first-time ICU admissions of adults over 18 years with a minimum 24-hour stay, excluding those receiving hospice or palliative care. Predictors included demographics, clinical characteristics, and nursing documentation frequencies related to nurses' concerns. Four models were trained with 10-fold cross-validation after adjusting class imbalance. The random forest (RF) model was identified as the best-performing, with key predictors of mortality in this model being the frequency of vital signs, the frequency of nursing note documentation, and the frequency of monitoring-related nursing notes. This suggests that predictive models using nursing records, which reflect nurses' concerns as represented by the frequency of nursing documentation, may be integrated into clinical decision support tools, potentially enhancing patient outcomes in ICUs.

Development of a Mapping Table for Nursing Notes Based on Nurses' Concerns in ICU Patients.

This study aimed to develop a mapping table that connects nursing notes with standard terminology, focusing on nurses' concerns for ICU patients. After extracting nursing notes from a publicly accessible database, a research team, including a nursing informatics professor and researchers with ICU experience, developed a mapping table through a four-step process: initially reviewing literature on nurses' concerns, then extracting nursing notes from MIMIC IV and filtering the duplicate notes, subsequently defining and coding these concerns, and finally mapping them according to the CCC. Of 11,430,637 unstructured nursing notes from MIMIC IV, 265 unique notes remained after deduplication, with 208 notes reflecting nurses' concerns and categorized into 15 groups aligned with CCC. This mapping table will be a fundamental tool for predicting clinical deterioration in ICU patients by identifying important lexicons through natural language processing.

Patient Generated Health Data Among Older Adults with Cancer in Home Setting: Scoping Review.

This scoping review aimed to identify and synthesize the literature related to patient-generated health data (PGHD) among older adults with cancer in home setting. Of the 1,090 articles extracted through six databases searches, 53 were selected. Studies were published from 2007 to 2022 and the types of devices to generate PGHD included research-grade and consumer-grade wearable devices. PGHD was assessed for physical activity, vital signs, and sleep. PGHD utilization was categorized: 1) identification, monitoring, review, and analysis (100%); 2) feedback and information report (32.1%); 3) motivation (26.4%); and 4) education and coaching (17.0%). Our study reveals that various PGHDs from older adults with cancer are mainly collected passively, with limited use for interaction with healthcare providers. These results may provide valuable insights for healthcare providers into potential PGHD applications in geriatric cancer care.

Trajectory classes of depressive symptoms among middle-aged and older adults following the transition to living alone.

This study identified the trajectory classes of depressive symptoms after transitioning to living alone and the factors affecting these trajectories. Data from the initial to the seventh waves of the Korean Longitudinal Study of Aging were analyzed, focusing on four consecutive waves. We selected 369 participants aged ≥45 who transitioned to living alone between consecutive waves (T1 and T2). They were followed up for six years (T1 to T4) while living alone from T2 to T4. Using latent class growth modeling, three trajectories emerged: low-lessening, moderate-lessening, and high-stable. The highly stable class was associated with older age, more chronic diseases, lower satisfaction with health and financial status, and less frequent social interactions at T1 than the low-lessening class. These findings underscore the importance of community-based interventions for middle-aged and older adults with risk factors as they face an increased risk of depression over time after transitioning to living alone.

Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education.

OBJECTIVES: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development. METHODS: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions. RESULTS: This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting. CONCLUSIONS: To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.

Prediction of hospital-acquired influenza using machine learning algorithms: a comparative study.

BACKGROUND: Hospital-acquired influenza (HAI) is under-recognized despite its high morbidity and poor health outcomes. The early detection of HAI is crucial for curbing its transmission in hospital settings. AIM: This study aimed to investigate factors related to HAI, develop predictive models, and subsequently compare them to identify the best performing machine learning algorithm for predicting the occurrence of HAI. METHODS: This retrospective observational study was conducted in 2022 and included 111 HAI and 73,748 non-HAI patients from the 2011-2012 and 2019-2020 influenza seasons. General characteristics, comorbidities, vital signs, laboratory and chest X-ray results, and room information within the electronic medical record were analysed. Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGB), and Artificial Neural Network (ANN) techniques were used to construct the predictive models. Employing randomized allocation, 80% of the dataset constituted the training set, and the remaining 20% comprised the test set. The performance of the developed models was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), the count of false negatives (FN), and the determination of feature importance. RESULTS: Patients with HAI demonstrated notable differences in general characteristics, comorbidities, vital signs, laboratory findings, chest X-ray result, and room status compared to non-HAI patients. Among the developed models, the RF model demonstrated the best performance taking into account both the AUC (83.3%) and the occurrence of FN (four). The most influential factors for prediction were staying in double rooms, followed by vital signs and laboratory results. CONCLUSION: This study revealed the characteristics of patients with HAI and emphasized the role of ventilation in reducing influenza incidence. These findings can aid hospitals in devising infection prevention strategies, and the application of machine learning-based predictive models especially RF can enable early intervention to mitigate the spread of influenza in healthcare settings.

Path analysis of illness uncertainty and psychosocial adaptation of patients with Marfan syndrome.

AIMS: Marfan syndrome (MFS) is a genetic disorder that causes sudden or chronic cardiovascular problems, which can be fatal. Since MFS patients require regular close medical observation, it is important to understand the factors and pathways associated with psychosocial adaptation to the disease. This study aimed to identify the relationships among illness uncertainty, uncertainty appraisal, and psychosocial adaptation in MFS patients using path analysis. METHOD AND RESULTS: This descriptive cross-sectional survey study was conducted from October 2020 to March 2021, in compliance with STROBE guidelines. Using data from 179 participants aged older than 18 years, we constructed a hypothetical path model to identify determinants of illness uncertainty, uncertainty appraisal, and psychosocial adaptation. In the path analysis, disease severity, illness uncertainty, anxiety, and social support were significant factors influencing MFS patients' psychosocial adaptation. Disease severity and illness uncertainty exerted direct effects, while anxiety and social support exerted both direct and indirect (through illness uncertainty) effects. Finally, anxiety showed the greatest total effect. CONCLUSION: These findings are useful for enhancing MFS patients' psychosocial adaptation. Medical professionals should focus on managing disease severity, decreasing anxiety, and increasing social support.

Nursing diagnoses as factors associated with delirium among intensive care unit patients with sepsis: A retrospective correlational study.

AIMS: To examine whether nursing diagnoses were associated with delirium in patients with sepsis. BACKGROUND: Nursing diagnosis is a nurse's clinical judgement about clients' current or potential health conditions. Delirium is regarded as an important nurse-sensitive outcome. Nonetheless, nursing diagnoses associated with delirium have not yet been identified. DESIGN: Retrospective correlational study. METHODS: This study was carried out from December 2021 to January 2023. We analysed electronic health records of patients with sepsis admitted to the intensive care units (ICUs) of a tertiary hospital in Seoul, South Korea. Delirium was defined based on the Intensive Care Delirium Screening Checklist score. Nursing diagnoses established within 24 h of admission to the ICU were included and were based on the North American Nursing Diagnosis Association diagnostic classification. The data were analysed using logistic regression. Demographics, comorbidities, procedures and physiological measures were adjusted. Regression model was evaluated via receiver operating characteristic curve, Nagelkerke R2, accuracy and F1 score. RESULTS: The prevalence of delirium in patients with sepsis was 51.8%. Ineffective breathing patterns, decreased cardiac output and impaired skin integrity were significant nursing diagnoses related to delirium. Age ≥ 65 years, Acute Physiology and Chronic Health Evaluation II score, mechanical ventilation, continuous renal replacement therapy, physical restraint and comatose state were also associated with delirium in patients with sepsis. The area under the receiver operating characteristic curve was 0.806. CONCLUSION: Ineffective breathing patterns, decreased cardiac output and impaired skin integrity could manifest as prodromal symptoms of delirium among patients with sepsis. IMPACT: The prodromal symptoms of delirium revealed through nursing diagnoses can be efficiently used to identify high-risk groups for delirium. The use of nursing diagnosis system should be recommended in clinical practice caring for sepsis patients. REPORTING METHODS: STROBE checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public involvement.

Everyday Digital Literacy Questionnaire for Older Adults: Instrument Development and Validation Study.

BACKGROUND: The need for digital literacy in aging populations is increasing in the digitalizing society. Digital literacy involves the identification, evaluation, and communication of information through various digital devices or relevant programs. OBJECTIVE: The aims of this study were to develop an Everyday Digital Literacy Questionnaire (EDLQ), a digital literacy assessment scale, and subsequently evaluate its psychometric properties using a population of community-dwelling older adults in South Korea. METHODS: The EDLQ was developed using an instrument development design. A nationwide survey was conducted, and the study included 1016 community-dwelling older adults (age ≥60 years). To evaluate the psychometric properties, the participants were randomly divided into 2 groups (n=508 each), and the internal consistency (Cronbach α and McDonald ω), structural validity (exploratory factor analysis and confirmatory factor analysis), hypothesis-testing construct validity using the eHealth Literacy Scale (eHEALS), and measurement invariance were analyzed. RESULTS: Among the initial 30 items of the EDLQ, 22 items with a 3-factor solution had a total explained variance of 77%. The domains included "information and communication" (9 items), "content creation and management" (4 items), and "safety and security" (9 items). Confirmatory factor analysis was conducted with this 3-factor solution (χ2206=345.1; normed χ2206=1.7; comparative fit index=0.997; Tucker-Lewis index=0.997; root-mean-square error of approximation=0.036; standardized root-mean-square residual=0.050; composite reliability=0.903-0.959; average variance extracted=0.699-0.724; R2=0.616-0.773). Hypothesis-testing construct validity with the eHEALS revealed a strong correlation (r=0.75). Cronbach α and McDonald ω coefficients were .98 and 0.98, respectively. The fit indices for measurement invariance, including the configural, metric, and scalar invariance models, demonstrated a satisfactory fit to the data. Our findings suggest that the psychometric properties of the 22-item EDLQ are valid and reliable for assessing digital literacy among older Korean adults. CONCLUSIONS: In this study, we developed a digital literacy measure with strong psychometric properties that made it suitable for assessing the digital literacy of community-dwelling older adults in Korea. To broaden its applicability, however, further assessment of its feasibility for use with different languages and cultures is necessary. Moreover, more empirical research on digital literacy and related factors in older adults can facilitate the development of personalized digital health care services and educational interventions in the digital society.

Benefits of Information Technology in Healthcare: Artificial Intelligence, Internet of Things, and Personal Health Records.

OBJECTIVES: Systematic evaluations of the benefits of health information technology (HIT) play an essential role in enhancing healthcare quality by improving outcomes. However, there is limited empirical evidence regarding the benefits of IT adoption in healthcare settings. This study aimed to review the benefits of artificial intelligence (AI), the internet of things (IoT), and personal health records (PHR), based on scientific evidence. METHODS: The literature published in peer-reviewed journals between 2016 and 2022 was searched for systematic reviews and meta-analysis studies using the PubMed, Cochrane, and Embase databases. Manual searches were also performed using the reference lists of systematic reviews and eligible studies from major health informatics journals. The benefits of each HIT were assessed from multiple perspectives across four outcome domains. RESULTS: Twenty-four systematic review or meta-analysis studies on AI, IoT, and PHR were identified. The benefits of each HIT were assessed and summarized from a multifaceted perspective, focusing on four outcome domains: clinical, psycho-behavioral, managerial, and socioeconomic. The benefits varied depending on the nature of each type of HIT and the diseases to which they were applied. CONCLUSIONS: Overall, our review indicates that AI and PHR can positively impact clinical outcomes, while IoT holds potential for improving managerial efficiency. Despite ongoing research into the benefits of health IT in line with advances in healthcare, the existing evidence is limited in both volume and scope. The findings of our study can help identify areas for further investigation.

Mobile Internet Use and Life Satisfaction Among Older Adults: The Moderating Effect of Living Alone.

This study, informed by the principle of the Conservation of Resources (COR) theory, evaluated the relationships between mobile internet use frequency, living arrangement, and life satisfaction in older adults. We used data from the 2021 Digital Divide Survey in South Korea, conducting a secondary analysis on 2105 participants aged 65 or above who accessed the internet in the last month. Multiple linear regression models were performed. Compared to the older adults who lived with others, those living alone showed a stronger positive association between their use of the mobile internet for daily services, information production and sharing, social participation, and life satisfaction. These findings align with the principle of the COR theory, suggesting that older adults living alone due to a loss in resources are more likely to benefit from mobile internet use, indicating the potential effectiveness of promoting mobile internet use as a strategy to enhance their life satisfaction.

Sleep Quality and Related Factors Among Liver Transplant Recipients in Korea: A Cross-Sectional Study.

BACKGROUND: Poor sleep quality is common after liver transplantation, which lowers health-related quality of life. However, the detection and management of sleeping difficulties in liver transplant recipients have been limited. PURPOSE: This study was designed to assess sleep quality in liver transplant recipients in Korea and associated factors, including symptom experience, depression, and family support, using the theory of unpleasant symptoms as a theoretical framework. METHODS: This cross-sectional study of 149 liver transplant recipients in a Korean tertiary hospital was conducted in 2019. Data were obtained using several structured self-report questionnaires, including the Pittsburgh Sleep Quality Index, Modified Transplant Symptom Occurrence and Symptom Distress Scale, Center for Epidemiologic Studies Depression Scale-Revised, and Family Support Scale. Multiple linear regression was used to determine the factors associated with sleep quality. RESULTS: The prevalence of sleep disturbance was 55% in the sample. Low sleep quality was associated with being female and with having a higher symptom experience, higher depressive symptoms, and lower family support. In the multiple linear regression analysis, depression and symptom experience was shown to significantly affect sleep quality. CONCLUSIONS/IMPLICATIONS FOR PRACTICE: The liver transplant recipients in this study reported poor sleep quality. The factors associated with the sleep quality included depression and symptom experience. To enhance sleep quality, interventions designed to reduce depression and symptoms related to liver transplantation should be provided. The results of this study may be used to develop nursing strategies to improve sleep quality in liver transplant recipients.

Development of the clinical reasoning competency scale for nurses.

BACKGROUND: Clinical reasoning is emphasized as an important component of nursing education, since nurses' lack of clinical reasoning leads to incorrect clinical decision-making. Therefore, a tool for measuring clinical reasoning competency needs to be developed. METHODS: This methodological study was conducted to develop the Clinical Reasoning Competency Scale (CRCS) and examine its psychometric properties. The attributes and preliminary items of the CRCS were developed based on a systematic literature review and in-depth interviews. The validity and reliability of the scale were evaluated among nurses. RESULTS: The exploratory factor analysis was conducted for the construct validation. The total explained variance of the CRCS was 52.62%. The CRCS consists of 8 items for plan setting, 11 items for intervention strategy regulation, and 3 items for self-instruction. The Cronbach's α of the CRCS was 0.92. Criterion validity was verified with the Nurse Clinical Reasoning Competence (NCRC). The correlation between the total NCRC and CRCS scores was 0.78, all of which were significant correlations. CONCLUSION: The CRCS is expected to provide raw scientific and empirical data for various intervention programs to develop and improve nurses' clinical reasoning competency.

Exploring nurses' multitasking in clinical settings using a multimethod study.

Nurses often multitask in the process of managing patient care and communicating with healthcare providers simultaneously within a limited time, which can negatively affect patient care and safety. In this multimethod research, we conducted a time and motion study to record nursing activities using eye trackers for 23 participants (9 nurses and 14 patients). The frequency and duration of single and multitasking activities were analyzed. Additionally, we conducted focus group interviews (FGIs) with 12 nurses (2-5 nurses per group) to further investigate their multitasking experience. The total duration of the eye tracker recordings was 3,399 min. Daily nursing activities comprised 23.7%, 21.1%, and 12.5% of scheduled medication, documentation, and monitoring and measurement, respectively. Among these activities, nurses mostly carry out scheduled medication, monitoring, and measurement together. Three themes emerged in the FGIs: "Being involved in every little task regarding patient care," "Getting swamped by the complexity of symptoms and problems of the patients at a given time," and "Getting interrupted at work too often." Nurses performed multiple activities while cooperating with other healthcare providers and providing care to patients. It is important to create an environment where nurses can focus on essential nursing activities to improve patient safety.

Association of Depressive Mood and Frailty With Mortality and Health Care Utilization: Korean National Cohort Study.

OBJECTIVES: To investigate the association of depressive mood and frailty with mortality and health care utilization (HCU) and identify the coexisting effect of depressive mood and frailty in older adults. DESIGN: A retrospective study using nationwide longitudinal cohort data. SETTING AND PARTICIPANTS: A total of 27,818 older adults age 66 years from the National Screening Program for Transitional Ages between 2007 and 2008, part of the National Health Insurance Service-Senior cohort. METHODS: Depressive mood and frailty were measured by the Geriatric Depression Scale and Timed Up and Go test, respectively. Outcomes were mortality and HCU, including long-term care services (LTCS), hospital admissions, and total length of stay (LOS) from the index date to December 31, 2015. Cox proportional hazards regression and zero-inflated negative binomial regression were performed to identify differences in outcomes by depressive mood and frailty. RESULTS: Participants with depressive mood and frailty represented 50.9% and 2.4%, respectively. The prevalence of mortality and LTCS use in the overall participants was 7.1% and 3.0%, respectively. More than 3 hospital admissions (36.7%) and total LOS above 15 days (53.2%) were the most common. Depressive mood was associated with LTCS use [hazard ratio (HR) 1.22, 95% confidence interval (CI) 1.05-1.42] and hospital admissions [incidence rate ratio (IRR) 1.05, 95% CI 1.02-1.08]. Frailty had associations with mortality risk (HR 1.96, 95% CI 1.44-2.68), LTCS use (HR 4.86, 95% CI 3.45-6.84), and LOS (IRR 1.30, 95% CI 1.06-1.60). The coexistence of depressive mood and frailty was associated with increased LOS (IRR 1.55, 95% CI 1.16-2.07). CONCLUSIONS AND IMPLICATIONS: Our findings highlight the need to focus on depressive mood and frailty to reduce mortality and HCU. Identifying combined problems in older adults may contribute to healthy aging by reducing adverse health outcomes and the burden of health care costs.

Development of an Ecological Momentary Assessment-Triggered Text Message Library for Peripheral Artery Disease.

This study developed an ecological momentary assessment (EMA)-triggered text message library based on the behavior change wheel framework to encourage exercise engagement in patients with peripheral artery disease (PAD). This study was conducted in three steps. In Step 1, semi-structured interviews were conducted with 15 patients with PAD. Their accounts of their exercise experiences revealed six barriers and three facilitators through thematic analysis. In Step 2, based on the findings from the prior step, the EMA survey was developed; its items included barriers to exercise, PAD-related leg symptoms, and general conditions. In Step 3, intervention functions to overcome exercise barriers were mapped, and 120 text messages were generated. Responses to the exercise barriers in the EMA survey triggered the automatic text messages. Additional studies are needed to examine the end-user acceptability of the text message library and the effectiveness of EMA-triggered text message interventions.

Diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients: A systematic review.

BACKGROUND: Nursing data consist of observations of patients' conditions and information on nurses' clinical judgment based on critically ill patients' behavior and physiological signs. Nursing data in electronic health records were recently emphasized as important predictors of patients' deterioration but have not been systematically reviewed. OBJECTIVE: We conducted a systematic review of prediction models using nursing data for clinical outcomes, such as prolonged hospital stay, readmission, and mortality in intensive care patients, compared to physiological data only. In addition, the type of nursing data used in prediction model developments was investigated. DESIGN: A systematic review. METHODS: PubMed, CINAHL, Cochrane CENTRAL, EMBASE, IEEE Xplore Digital Library, Web of Science, and Scopus were searched. Clinical outcome prediction models using nursing data for intensive care patients were included. Clinical outcomes were prolonged hospital stay, readmission, and mortality. Data were extracted from selected studies such as study design, data source, outcome definition, sample size, predictors, reference test, model development, model performance, and evaluation. The risk of bias and applicability was assessed using the Prediction model Risk of Bias Assessment Tool checklist. Descriptive summaries were produced based on paired forest plots and summary receiver operating characteristic curves. RESULTS: Sixteen studies were included in the systematic review. The data types of predictors used in prediction models were categorized as physiological data, nursing data, and clinical notes. The types of nursing data consisted of nursing notes, assessments, documentation frequency, and flowsheet comments. The studies using physiological data as a reference test showed higher predictive performance in combined data or nursing data than in physiological data. The overall risk of bias indicated that most of the included studies have a high risk. CONCLUSIONS: This study was conducted to identify and review the diagnostic accuracy of clinical outcome prediction using nursing data in intensive care patients. Most of the included studies developed models using nursing notes, and other studies used nursing assessments, documentation frequency, and flowsheet comments. Although the findings need careful interpretation due to the high risk of bias, the area under the curve scores of nursing data and combined data were higher than physiological data alone. It is necessary to establish a strategy in prediction modeling to utilize nursing data, clinical notes, and physiological data as predictors, considering the clinical context rather than physiological data alone. REGISTRATION: The protocol for this study is registered with PROSPERO (registration number: CRD42021273319).

Developing an app-based self-management program for people living with HIV: a randomized controlled pilot study during the COVID-19 pandemic.

People living with human immunodeficiency virus (PLWH) in Korea demonstrate insufficient self-management behaviors. Especially during pandemics such as COVID-19, technology-based self-management programs are needed to overcome time and space limitations. The purpose of this study was to evaluate the effects of a self-management program using a mobile app (Health Manager) on self-management outcomes among PLWH in Korea. A randomized controlled pilot trial was performed and participants were enrolled in the infectious outpatient clinic of a single hospital. The intervention group used the mobile app for 4 weeks, while the control group received self-management education materials in a portable document format. The online self-report questionnaire assessed primary outcomes including self-efficacy for self-management, self-management behaviors, and medication adherence, and secondary outcomes including perceived health status, depression, and perceived stigma. Thirty-three participants were randomly assigned to the intervention (n = 17) or the control group (n = 16). In the intention-to-treat analysis, self-efficacy for self-management and self-management behaviors increased, while perceived stigma decreased. The app-based self-management program could be considered a helpful strategy to improve self-management outcomes among PLWH and reduce their perceived stigma during the pandemic. Further studies with larger samples and longer follow-ups are needed.Trial registration: Clinical Research Information Service, KCT0004696 [04/02/2020].

Development of a Standardized Curriculum for Nursing Informatics in Korea.

OBJECTIVES: This study explored the current status of nursing informatics education in South Korea and developed a standardized curriculum for it. METHODS: Data were collected in two stages: first, an online survey conducted from December 2020 to February 2021 among 60 nursing schools to analyze the current status of nursing informatics education; and second, a two-round Delphi survey with 15 experts from March to April 2021 to determine the mean and standard deviation of the demand for each learning objective in nursing informatics education. A standardized curriculum proposal was developed based on the results of the two-round Delphi survey. RESULTS: Nursing informatics was most commonly taught in the fourth year (34%), with two credits. The proportion of elective major subjects was high in undergraduate and graduate programs (77.4% and 78.6%, respectively), while the proportion of nursing informatics majors was low (21.4%). The curriculum developed included topics such as nursing information system-related concepts, definitions and components of healthcare information systems, electronic medical records, clinical decision support systems, mobile technology and health management, medical information standards, personal information protection and ethics, understanding of big data, use of information technology in evidence-based practice, use of information in community nursing, genome information usage, artificial intelligence clinical information systems, administrative management systems, and information technology nursing education. CONCLUSIONS: Nursing informatics professors should receive ongoing training to obtain recent medical information. Further review and modification of the nursing informatics curriculum should be performed to ensure that it remains up-to-date with recent developments.

Simulation Education Incorporating Academic Electronic Medical Records for Undergraduate Nursing Students: A Pilot Study.

OBJECTIVES: Academic electronic medical records (AEMRs) can be utilized for a variety of educational programs that can enhance nursing students' nursing informatics and clinical reasoning competencies. This study aimed to identify the applicability and effectiveness of simulation education incorporating AEMRs. METHODS: We developed simulation education scenarios incorporating AEMRs and evaluated them with 76 third- and fourth-year nursing students from five nursing schools using a mixed-methods design. We incorporated three simulation case scenarios involving preeclampsia, diabetes mellitus, and myocardial infarction into the AEMRs. After the simulation education, participants' feedback on the usability of the AEMR system and their self-efficacy for AEMR utilization were collected via self-reported surveys. Subsequently, the simulation education incorporating AEMRs was evaluated through a focus group interview. The survey data were examined using descriptive statistics, and thematic analysis was done for the focus group interview data. RESULTS: The average mean scores for the AEMR system's usability and participants' self-efficacy for AEMR utilization were 5.36 of 7 and 3.96 of 5, respectively. According to the focus group interviews, the participants were satisfied with the simulation education incorporating AEMRs and recognized their confidence in AEMR utilization. In addition, participants addressed challenges to simulation education incorporating AEMRs, including the need for pre-education and AEMR utilization difficulties. CONCLUSIONS: Nursing students were satisfied with and recognized the value of simulation education incorporating AEMRs. Although the actual application of simulation education incorporating AEMRs remains challenging, further research can help develop and implement this approach for nursing students.