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.

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 4.0 (GPT-4.0), and Bio_Clinical Bidirectional Encoder Representations from Transformers (BERT) evaluated the generated statements for validity. The evaluation by GPT-4.0 and Bio_ClinicalBERT was conducted with and without contextual information and training. RESULTS: Of the generated statements, 2207 were deemed valid by expert reviewers. GPT-4.0 showed a zero-shot  AUC of 0.857, which aggravated with contextual information. Bio_ClinicalBERT, after training, significantly improved, reaching an AUC of 0.998. CONCLUSION: Bio_ClinicalBERT effectively validates auto-generated nursing statements, offering a promising solution to enhance and streamline healthcare documentation processes.

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. Therefore, future studies should verify the effectiveness of education.

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. Armband data filtering was performed using heart rate measures and minimum daily step counts as a proxy for adequate daily wear time. No substantial differences in the filtered armband datasets were observed by filtering methods. Significant gaps were observed between healthy walking amounts determined from armband data and through the health diary. Future studies need to explore more diverse data filtering methods and their impact on health outcome assessments.

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. Of these, 70.7% were physicians. While 65% had used mobile health apps and devices, only 12.8% were familiar with person-generated health data. Still, a promising 73.3% expressed interest in incorporating person-generated health data into patient care, particularly data on blood glucose and vital signs. The findings of the study also indicated that clinicians specializing in internal medicine (OR: 1.9, CI: 1.16-3.19), familiar with person-generated health data (OR: 2.6, CI: 1.58-4.29), with a positive view of information and communication technology adoption (OR: 2.6, CI: 1.65-4.13), and who see the value in person-generated health data (OR: 3.9, CI: 2.55-6.09) showed higher inclination to utilize it. However, those in outpatient settings (OR: 0.4, CI: 0.19-0.73) showed less enthusiasm. The findings of this study suggest that despite the willingness of clinicians to use person-generated health data, various barriers must be addressed first, including a lack of knowledge regarding its use, concerns about data reliability and quality, and a lack of provider incentives. Overcoming these challenges demands concerted organizational or policy support. This research underscores person-generated health data's untapped potential in healthcare and the pressing need for strategies that facilitate its clinical integration.

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.

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. OBJECTIVE: The aim of this study was to examine the extent of actualizing data-sharing intention to data-sharing action and to identify the factors that influence data-sharing intention and action. METHODS: A web-based survey of members of a university examined the data-sharing intention and issues of concern when making decisions on data sharing. The participants were asked to deposit their armband data for use in research at the end of the survey. A comparison of data-sharing intention and action in relation to the participants' characteristics was performed. Factors having a significant effect on data-sharing intention and action were identified using logistic regressions. RESULTS: Of 386 participants, 294 expressed willingness to share health data. However, only 73 participants deposited their armband data. The primary reason for refusal to deposit armband data was the inconvenience of the data transfer process (56.3%). Appropriate compensation had a significant effect on data-sharing intention (OR: 3.3, CI: 1.86-5.75) and action (OR: 2.8, CI: 1.14-8.21). The compensation for data sharing (OR:2.8, CI:1.14-8.21) and familiarity with data (OR:3.1, CI:1.36-8.21) were significant predictors of data sharing action, however, data-sharing intention was not (OR: 1.5, CI:0.65-3.72). CONCLUSION: Despite expressing willingness to share their health data, the participants' intention was not actualized to data-sharing behavior for depositing armband data. Implementation of a streamlined data transfer process and providing appropriate compensation might facilitate data-sharing. These findings could be useful in development of strategies to facilitate sharing and reuse of health data.

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.

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. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts. OBJECTIVE: The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON. METHODS: By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy. RESULTS: DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results. CONCLUSIONS: Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.

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. The analysis used data which generated 478 nurses who participated in the fifth Korean Working Conditions Survey (2017). This study built a complex samples logistic regression model and decision tree analysis. Presenteeism was significantly higher among those who experienced musculoskeletal pain, high emotional demands, discrimination, and psychological adverse social behavior at the workplace. Combined presence of psychological adverse social behavior and musculoskeletal pain was predicted presenteeism, and should be treated as groups with a high risk of presenteeism. Nurses should be aware of high-risk group for presenteeism and treat them as a priority group to manage.

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. The data were analysed using the R statistical package. Network graphs were used to analyse the relationships among turnover predictors, and meta-analysis was performed to determine the effect sizes of the correlations. RESULTS: This review analysed common predictors identified in previous studies. Burnout (0.541), emotional exhaustion (0.511), job stress (0.390) and career plateau (0.386) showed positive effect sizes, while organizational commitment (-0.540), person-organizational fit (-0.521), career commitment (-0.508), work engagement (-0.503), job satisfaction (-0.491) and job embeddedness (-0.483) showed negative effect sizes.

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. METHODS: This was a secondary analysis of Korean National Health and Nutrition Examination Survey data (2013-2017), targeting 7347 older adults (≥65 years). Socioeconomic position, defined as income, was the primary indicator. The outcome was binary for predicted cardiovascular risk (<90 vs ≥90 percentile). Disparities were measured using relative index of inequality (RII). The contributions of material, behavioral, psychological, and social-relational factors were estimated by calculating percentage reduction in RII when adjusted for these factors. RESULTS: Among men aged 65 to 74 years and women 75 years or older, the largest reductions in RII were achieved after adjusting for social-relational factors. Among men 75 years or older and women aged 65 to 74 years, adjusting for material factors resulted in the largest reductions in RII. Adjustments for behavioral factors also reduced RII for both genders aged 65 to 74 years. CONCLUSIONS: Improving the social, material, and behavioral circumstances of lower-income older adults may help address income-related disparities in cardiovascular risk in old age.

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., breast cancer is a genetic disease), Asian Americans are known as a high-risk group within breast cancer survivors. However, healthcare providers are challenged to promote women's self-efficacy while considering their cultural beliefs and attitudes. In this study, the efficacy of a technology-based information and coaching/support program was examined in improving self-efficacy for coping with breast cancer among Asian American survivors. A randomized repeated measures control group study was conducted with 67 Asian American breast cancer survivors. The questions on background characteristics, the Personal Resource Questionnaire, the Perceived Isolation Scale, the Supportive Care Needs Survey Short Form 34, and the Cancer Behavior Inventory were used. The data were analyzed using repeated measurement analyses, χ2 tests, and decision tree analyses. There were significant increases in the self-efficacy scores of both control and intervention groups over time (p = .017). However, the increase in the control group's self-efficacy scores was only up to post 1 month, and there was a decrease in the scores by post 3 months. When the participants were divided into high and low-change groups based on the changes in their self-efficacy scores for 3 months, the intervention group had more participants who belonged to the high-change group (p = .036). The technology-based intervention was effective in improving self-efficacy for coping with breast cancer among Asian American breast cancer survivors.

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). Socioeconomic position, defined as income and use of welfare benefits, was the primary indicator. The outcome was binary for predicted CVD risk (<90th vs. ≥ 90th). The Slope Index of Inequality (SII) and Relative Index of Inequality (RII) were used to assess trends in disparities. RESULTS: The percentage of older adults with a predicted CVD risk of 90% or more declined over time, but this was due to a decrease among the more affluent. Disparities have persisted since 2012, with a worsening trend seen for Medicaid recipients. We found significant absolute and relative disparities among men over 75 years of age in recent years (SII > 0.19, RII > 7). CONCLUSIONS: These results may inform and improve policies regarding income disparity reduction and cardiovascular health.

Trends in Female Workers' Childbearing Intentions in South Korea.

This study aimed to analyze trends of South Korean working women's childbearing intentions to provide directions for strategies to increase South Korea's birth rate. This study used the data generated by the Korean Longitudinal Panel Survey of Women and Families in South Korea from 2007 to 2016, and included 2,341 working women. This study showed that female workers' intention to bear children is decreasing. In 2007, age and the number of children were considered in predicting the characteristics of those with childbearing intentions. In 2016, the provision of maternity leave at work, job satisfaction regarding relationships and communication, and work-family conflicts were added. When identifying the factors by category, the impact level of occupational factors increased, although the impact level of individual factors decreased. There should be a balance between work and family roles, and employers should provide ample maternity leave and promote an organizational culture that supports job satisfaction.

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. This study used the data from 23,164 full-time salaried employees, who participated in the fifth Korean Working Conditions Survey. This study attempted to predict presenteeism based on the exposure to adverse social behaviors and working conditions using logistic regression. Presenteeism was reported in 15.9% of the sample. Presenteeism was significantly higher among workers with the following characteristics: females, aged 40 years or older; middle school graduates; over 40 working hours a week; shift workers; no job-related safety information received; exposure to adverse social behavior and discrimination; and those with a high demand for quantitative work, low job autonomy, high emotional demands, and high job stress. The workers exposed to adverse social behavior showed a higher prevalence of presenteeism (41.2%), and low job autonomy was the most significant predictor of presenteeism. The findings of this study suggest that allowing enough autonomy in job-related roles may help alleviate presenteeism among those who have experienced adverse social behavior at work.

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. Lack of clarity in communicating the intensity level of physical activity is a potential barrier to an accurate assessment of exercise effect and effective imparting of exercise recommendations. OBJECTIVE: This study aimed to assess the variations in people's perceptions and interpretations of commonly used intensity descriptions of physical activities and to identify factors that may contribute to these variations. METHODS: A Web-based survey with a 25-item questionnaire was conducted using Amazon Mechanical Turk, targeting adults residing in the United States. The questionnaire included questions on participants' demographics, exercise habits, overall perceived health status, and perceived intensity of 10 physical activity examples. The survey responses were analyzed using the R statistical package. RESULTS: The analyses included 498 responses. The majority of respondents were females (276/498, 55.4%) and whites (399/498, 79.9%). Numeric ratings of physical exertion after exercise were relatively well associated with the 3 general degree descriptors of exercise intensity: light, moderate, and vigorous. However, there was no clear association between the intensity expressed with those degree descriptors and the degree of physical exertion the participants reported to have experienced after exercise. Intensity ratings of various examples of physical activity differed significantly according to respondents' characteristics. Regression analyses showed that those who reported good health or considered regular exercise was important for their health tended to rate the intensity levels of the activity examples significantly higher than their counterparts. The respondents' age and race (white vs nonwhite) were not significant predictors of the intensity rating. CONCLUSIONS: This survey showed significant variations in how people perceive and interpret the intensity levels of physical activities described with general severity modifiers, degrees of physical exertion, and physical activity examples. Considering that these are among the most widely used methods of communicating physical activity intensity in current practice, a possible miscommunication in assessing and promoting physical activity seems to be a real concern. We need to adopt a method that represents activity intensity in a quantifiable manner to avoid unintended miscommunication.

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. We analyzed the current triggering criteria for six flu vaccine-related alerts and asked nurse end users for suggestions to increase specificity. Using the "five rights" (of clinical decision support) as a framework, alerts were redesigned to address user needs. New alerts were tested and implemented and their activity compared in two different flu seasons, preoptimization and postoptimization. The redesigned alerts resulted in fewer alerts per encounter (P < .0001), less dismissals of alerts (P < .0001), and a 2.8% point improvement in compliance rates for flu vaccine screening, documentation, and administration. A focus group confirmed that the redesign improved workflow, but some nurses thought they still triggered too often. The five rights model can support improvements in alert design and outcomes.

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. However, few studies have investigated the application of Fast Healthcare Interoperability Resources in nursing-specific domains. In this study, a Fast Healthcare Interoperability Resources document was generated for a use case scenario in a home-based, pressure ulcer care setting. Study goals were to describe the step-by-step process of generating a Fast Healthcare Interoperability Resources artifact and to inform nursing communities about the advantages and challenges in representing nursing data with Fast Healthcare Interoperability Resources. Overall, Fast Healthcare Interoperability Resources effectively represented the majority of the data included in the use case scenario. A few challenges that could potentially cause information loss were noted such as the lack of standardized concept codes for value encoding and the difficulty directly connecting an observation to a related condition. Continuous evaluations in diverse nursing domains are needed in order to gain a more thorough insight on potential challenges that Fast Healthcare Interoperability Resources holds in representing nursing data.