INTRODUCTION:The U.S. Department of Veterans Affairs (VA) is a leader in providing innovative wellness programming to its employees. However, like many organizations, VA faces ongoing challenges fostering employee engagement. This national, formative evaluation aimed to identify barriers and facilitators to the implementation of employee wellness programming at multiple sites. MATERIALS AND METHODS:We used a novel quality improvement approach to uncover barriers and facilitators to employee engagement with employee wellness programs. We conducted qualitative, semi-structured interviews with key partners at 8 independent VA sites. Identified barriers and facilitators were coded and analyzed using a quality improvement framework for the cause-and-effect analysis. RESULTS:Based on the cause-and-effect analysis, we generated recommendations to enhance employee engagement. Recommendations included incorporating wellness into orientation, diversifying communication, hiring devoted leaders, developing whole health-oriented mission statements, constructing comprehensive whole person wellness measures, and promoting a holistic wellness culture. CONCLUSIONS:This multisite evaluation generated collaborative insights from key partners with diverse perspectives of the employee wellness program implementation process. Organizations can adopt this evaluation model to assess and refine their own employee wellness initiatives, identifying both challenges and successes to drive engagement. VA has invested significant resources to support employees via the Employee Whole Health Program. To improve employee engagement with the wellness programming, we recommend promoting employee input, agency, and participation in shaping the programs mission.

Background/introduction Variability in student and faculty experiences with quality improvement and system change at an Upper Midwestern university led to inconsistent planning of doctor of nursing practice (DNP) projects. Purpose and significance The purpose of the pilot for the DNP project practicum formative learning activities was to support the DNP project planning process by incorporating the ethical use of generative artificial intelligence (GenAI). Methods The faculty developed two formative assignments, underpinned by self-directed learning theory and Byrne's (2025) Five Rights of Teaching and Learning with Artificial Intelligence, for a DNP asynchronous project practicum. The assignments required students to use generative artificial intelligence (GenAI) to create DNP project goals, SMART (specific, measurable, achievable, relevant, and time-bound) objectives, tasks, a timeline table, and a measurement and evaluation plan table. Results Using GenAI to guide DNP project planning generated ideas and improved efficiency. Students acknowledged GenAI's imperfections and the ethical risks of overreliance. Faculty noted GenAI offers utility while engaging students with their project planning. Limitations The rapid evolution of GenAI requires regular faculty review and updates to guided prompts. Conclusions/implications for practice The focused formative learning activities with GenAI facilitated students' self-directed learning and enabled them to develop GenAI literacy by leveraging technology and analyzing its limitations.

A rapidly expanding array of Artificial Intelligence (AI) tools, with continually evolving features and functionalities, offers unprecedented opportunities to streamline literature reviews, expediting the screening, extraction, and synthesis phases. We present preliminary findings of evaluating various AI tools' strengths and limitations.

Consumer involvement in the co-design of diabetes self-management smartphone apps is vital. This scoping review explored how consumers are involved in the co-design processes and methods and approaches guiding this research. Our review was guided by Arksey and O'Malley's five-stage framework, PRISMA-ScR guidelines, and Witteman and colleagues' 11-item user-centered design (UCD-11) framework. We searched literature across five databases and examined types of consumer involvement in co-design and frequency of methods and approaches (i.e., co-design approaches, behavioral theories, and other frameworks), synthesizing findings in SPSS and Excel. Of the 14,206 initial items, 283 articles were included. Most studies were conducted in Asia (33.2%) and focused on type 2 diabetes (43.1%). All articles addressed at least one UCD principle, and prototype evaluation (UCD-3) was the most frequent (82.3%); 85.2% addressed iterative responsiveness (factor 2). Most articles (66.8%) did not report a particular method or approach; 20.5% used design-related approaches, with user-centered design being the most common (7.4%). Few articles (3.9%) utilized social cognitive theory. Overall, co-design activities were isolated by phase. Consumers were primarily involved in evaluating prototypes and had limited engagement in the early stages. Iterative responsiveness factor activities were underreported or limited in scope. The use of approaches, theories, and frameworks was inconsistent. Consumer involvement in the co-design of diabetes self-management apps is often limited to later phases, with minimal engagement during the critical preprototype phase. To enhance the relevance, effectiveness, and adoption of diabetes self-management apps, app designers should improve the reporting of co-design activities and engage consumers across all co-design phases.

BACKGROUND:Standardized taxonomies (STs) facilitate knowledge representation and semantic interoperability within health care provision and research. However, a gap exists in capturing knowledge representation to classify, quantify, qualify, and codify the intersection of evidence and quality improvement (QI) implementation. This interprofessional case report leverages a novel semantic and ontological approach to bridge this gap. OBJECTIVES:This report had two objectives. First, it aimed to synthesize implementation barrier and facilitator data from employee wellness QI initiatives across Veteran Affairs health care systems through a semantic and ontological approach. Second, it introduced an original framework of this use-case-based taxonomy on implementation barriers and facilitators within a QI process. METHODS:We synthesized terms from combined datasets of all-site implementation barriers and facilitators through QI cause-and-effect analysis and qualitative thematic analysis. We developed the Quality Improvement and Implementation Taxonomy (QIIT) classification scheme to categorize synthesized terms and structure. This framework employed a semantic and ontological approach. It was built upon existing terms and models from the QI Plan, Do, Study, Act phases, the Consolidated Framework for Implementation Research domains, and the fishbone cause-and-effect categories. RESULTS:The QIIT followed a hierarchical and relational classification scheme. Its taxonomy was linked to four QI Phases, five Implementing Domains, and six Conceptual Determinants modified by customizable Descriptors and Binary or Likert Attribute Scales. CONCLUSION:This case report introduces a novel approach to standardize the process and taxonomy to describe evidence translation to QI implementation barriers and facilitators. This classification scheme reduces redundancy and allows semantic agreements on concepts and ontological knowledge representation. Integrating existing taxonomies and models enhances the efficiency of reusing well-developed taxonomies and relationship modeling among constructs. Ultimately, employing STs helps generate comparable and sharable QI evaluations for forecast, leading to sustainable implementation with clinically informed innovative solutions.

Background Health informatics education is pivotal in integrating diversity, equity, inclusion, and accessibility (DEIA) principles into curricula and leveraging data with equity considerations. Integrating clinically driven data with other datasets is crucial to comprehensive understanding of patient care demographics, experiences, and outcomes to create equity-minded data storytelling. Publicly available Healthy People 2030 (HP2030) resources complement academic electronic health records, supporting tailored learning activities in informatics education to enhance educational utility through a DEIA lens. Objectives This case report describes the expansion of an existing diversity, equity, and inclusion (DEI) checklist to an updated DEIA checklist for preparing future informaticians to collect and critically evaluate DEIA features using this checklist in creating equity-minded data storytelling. Methods The DEI-Oriented Data Storytelling Model and the HP2030 framework were utilized to develop the DEIA checklist. We employed an informal cognitive walkthrough to expand the DEIA checklist and evaluate the DEIA measures or characteristics within datasets from the HP2030 social determinants of health (SDOH) five topics using this checklist. Results We reviewed 76 available SDOH-related datasets and added six measures to "demographics" and seven to "skills, abilities, and accessibility" of the DEIA checklist. Our evaluation of the DEIA checklist verified HP2030's inclusion of all measures, except "religions/beliefs." All DEIA measures were linked to equity and accessibility, one in inclusion, and the inclusion of three characteristics comprising the category "language" and six characteristics comprising the category "images." Conclusion Results highlighted the accessibility and comprehensiveness of HP2030 demographic data resources, considering SDOH factors and promoting inclusive data representation to address health disparities. The DEIA checklist provides a structured tool in facilitating unbiased data collection and visualization of SDOH-related data through an equity-informed lens. Integrating an equity-minded data storytelling with frameworks like HP2030 enriches health informatics education, broadens students' understanding of health disparities, and supports evidence-based interventions for improved health outcomes.

PurposeTo examine the relationship between quality of life (QoL) and chronic pelvic pain (CPP), including an evaluation of whether differences exist between reported races and coping mechanisms used.MethodsWe used a cross-sectional survey design and analyzed data using descriptive and inferential statistics. We administered two surveys: the World Health Organization Quality of Life-BREF (26 items) and the Impact of Female Chronic Pelvic Pain Questionnaire (8 items). We recruited young adults aged 18-25 who menstruate from college campuses in a large metropolitan area in the Midwest region of the United States, utilizing flyers, online social media platforms, and snowball sampling techniques.ResultsOut of the 585 respondents, 153 (26%) reported "yes," and 95 (16%) were "unsure" they had CPP. Those with CPP and unsure reported using various coping mechanisms for pain. They had lower scores in all four domains (physical health, psychological, social relationship, and environment) and statistically significant lower scores in three domains (physical health, social relationship, and environment) on the World Health Organization Quality of Life-BREF when compared to those who said "no." Respondents identifying as Black, Indigenous, or People of Color had statistically significantly lower QoL in the physical health and environment domains compared to white respondents.ConclusionYoung adults with CPP experience a significantly lower QoL than those without CPP, and racial differences further widen this gap. Future research should explore coping mechanisms that could benefit young adults' daily lives.

OBJECTIVES:The goal of this work was to provide a review of the implementation of data science-driven applications focused on structural or outcome-related nurse-sensitive indicators in the literature in 2021. By conducting this review, we aim to inform readers of trends in the nursing indicators being addressed, the patient populations and settings of focus, and lessons and challenges identified during the implementation of these tools. METHODS:We conducted a rigorous descriptive review of the literature to identify relevant research published in 2021. We extracted data on model development, implementation-related strategies and measures, lessons learned, and challenges and stakeholder involvement. We also assessed whether reports of data science application implementations currently follow the guidelines of the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by AI (DECIDE-AI) framework. RESULTS:Of 4,943 articles found in PubMed (NLM) and CINAHL (EBSCOhost), 11 were included in the final review and data extraction. Systems leveraging data science were developed for adult patient populations and were primarily deployed in hospital settings. The clinical domains targeted included mortality/deterioration, utilization/resource allocation, and hospital-acquired infections/COVID-19. The composition of development teams and types of stakeholders involved varied. Research teams more frequently reported on implementation methods than implementation results. Most studies provided lessons learned that could help inform future implementations of data science systems in health care. CONCLUSION:In 2021, very few studies report on the implementation of data science-driven applications focused on structural- or outcome-related nurse-sensitive indicators. This gap in the sharing of implementation strategies needs to be addressed in order for these systems to be successfully adopted in health care settings.

Meaningful use of data generated from electronic health records (EHRs) exerts influential impacts on every aspect of healthcare to facilitate clinically intelligent decision-making and improve healthcare outcomes. As nurses are called to chart a path of equity in healthcare, there presents a growing need of incorporating diversity, equity, inclusion (DEI) perspectives into data courses generated from academic EHRs in academic informatics education. This paper describes the development of a DEI data standard model and the evaluation of data courses residing within an academic EHR platform using a cognitive walkthrough method. Data points selected for learning activities in the evaluated data courses appeared to be predominantly clinically driven and lack DEI-informed data features. To facilitate DEI-informed graduate health informatics education and the seamless transfer of health professional students to workforces, data courses built within academic EHRs should integrate DEI-informed data measures and thinking in course curriculum design and development.

BACKGROUND:Although mobile health (mHealth) apps for both health consumers and health care providers are increasingly common, their implementation is frequently unsuccessful when there is a misalignment between the needs of the user and the app's functionality. Nurses are well positioned to help address this challenge. However, nurses' engagement in mHealth app development remains unclear. OBJECTIVE:This scoping review aims to determine the extent of the evidence of the role of nurses in app development, delineate developmental phases in which nurses are involved, and to characterize the type of mHealth apps nurses are involved in developing. METHODS:We conducted a scoping review following the 6-stage methodology. We searched 14 databases to identify publications on the role of nurses in mHealth app development and hand searched the reference lists of relevant publications. Two independent researchers performed all screening and data extraction, and a third reviewer resolved any discrepancies. Data were synthesized and grouped by the Software Development Life Cycle phase, and the app functionality was described using the IMS Institute for Healthcare Informatics functionality scoring system. RESULTS:The screening process resulted in 157 publications being included in our analysis. Nurses were involved in mHealth app development across all stages of the Software Development Life Cycle but most frequently participated in design and prototyping, requirements gathering, and testing. Nurses most often played the role of evaluators, followed by subject matter experts. Nurses infrequently participated in software development or planning, and participation as patient advocates, research experts, or nurse informaticists was rare. CONCLUSIONS:Although nurses were represented throughout the preimplementation development process, nurses' involvement was concentrated in specific phases and roles.

BACKGROUND:The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. OBJECTIVES:This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. METHODS:We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. RESULTS:Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. CONCLUSION:This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

As a new era of healthcare advocates a more valuable and intelligent approach to care management and delivery based on values and outcomes, shifts toward risk management to boost performance should be considered that encompass the capitalization of health assets or health strengths. To make full use of individuals' or populations' health assets, data capture and representation are needed. This paper uses a strengths-oriented case study mapped to an inter-disciplinary standardized terminology, the Omaha System, to illustrate and compare the conventional problem-based approach to care management with the strengths-oriented approach to care that demonstrates whole-person data capture of an individual's health and health assets leveraged to promote health values and performance. The Omaha system provides a standardized framework to organize the concepts of all of health from a whole-person perspective for documentation to enable data analysis, interoperability, and health information exchange.

Chronic pain is a significant health issue that affects approximately 50 million adults in the United States. Traditional interventions are not always an effective treatment strategy for pain control. However, the wide adoption of smartphones and the rapid growth of health information technologies over the past decade have created opportunities to use mobile health (mHealth) applications (apps) for pain tracking and self-management. In this PRISMA-compliant systematic review, we assessed the current U.S.-based research on pain-related mHealth apps to describe the app components and determine the efficacy of these interventions for persons with acute or chronic pain. We conducted a comprehensive search of five databases based on methodological guidelines from the Joanna Briggs Institute. We included articles reporting original data on mHealth interventions with pain intensity as a primary or secondary outcome and excluded articles that utilized multimodal interventions. Of the original 4959 articles, only five studies met the eligibility criteria. Most of the interventions included feasibility or pilot studies, and all studies were published between 2015 and 2018. Two of the five studies used visual analog scales. Only two of the studies reported statistically significant pain intensity outcomes, and considerable heterogeneity between the studies limited our ability to generalize findings or conduct a meta-analysis. Research investigating the components and efficacy of pain-related mHealth apps as interventions is an emerging field. To better understand the potential clinical benefits of mHealth apps designed to manage pain, further research is needed.

Mary Anne Schultz, BSN, MSN, MBA, PhD, Rachel Lane Walden, MLIS, Kenrick Cato, RN, PhD, CPHIMS, FAAN, Cynthia Peltier Coviak, PhD, RN, FNAP, Christopher Cruz, MSHI, RN-BC, CPHIMS, Fabio D'Agostino, PhD, MSN, RN, Brian J. Douthit, MSN, RN-BC, Thompson Forbes, PhD, RN, Grace Gao, PhD, DNP, RN-BC, Mikyoung Angela Lee, PhD, RN, Deborah Lekan, PhD, RN-BC, Ann Wieben, MS, BSN, RN-BC, Alvin D. Jeffery, PhD, RN-BC, CCRN-K, FNP-BC

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Diseases have no borders, and global health operates from both within and beyond. Global health informatics can adopt an assets-oriented approach to mitigate concerns by maximizing global health data, principles, and resources combined with geographic information systems' use case mapping. This exploratory study utilizes an assets-oriented approach to analyze four global social determinants of health indicators, including Skilled Birth Attendance, Measles Immunization Coverage, Education (Female), and the Healthcare Access and Quality Index in relation to countries' income and geographical region. Data were extracted and analyzed from two publicly available datasets. Positive trends and variations were detected among all variables aggregated by countries' income category and geographical region. These findings pinpoint potential health assets that the discipline of nursing can leverage to build healthier global health communities.

BACKGROUND AND PURPOSE:Little is known about how nursing assessments of strengths and signs/symptoms inform intervention planning in assisted living communities. The purpose of this study was to discover associations among older adults' characteristics and their planned nursing interventions.METHODS:This study employed a data-driven method, latent class analysis, using existing electronic health record data from a senior living community in the Midwest. A convenience sample comprised de-identified data of well-being assessments and care plans for 243 residents. Latent class analysis, descriptive, and inferential statistics were used to group the sample, summarize strengths and problems attributes, nursing interventions, and Knowledge, Behavior, and Status scores, and detect differences.RESULTS:Three groups presented based on patterns of strengths and signs/symptoms combined with problem concepts: Living Well (n = 95) had more strengths and fewer signs/symptoms; Lower Strengths (n = 99) had fewer strengths and more signs/symptoms; and Resilient Survivors (n = 49) had more strengths and more signs/symptoms. Some associations were found among group characteristics and planned interventions. Living Well had the lowest average number of planned interventions per resident (Mean = 2.7; standard deviation [SD] = 1.7) followed by Lower Strengths (Mean = 3.8; SD = 2.6) and Resilient Survivors (Mean = 4.1; SD = 3.4).IMPLICATIONS FOR PRACTICE:This study offers new knowledge in the use of a strengths-based ontology to facilitate a nursing discourse that leverages use of older adults' strengths to address their problems and support their living a healthier life. It also offers the potential to complement the problem-based infrastructure in clinical practice and documentation.

Diabetes is a manageable chronic condition that contributes significantly to the global health burden of diseases and mandates a global collective effort to create an effective solution. This paper describes a community diabetes care pathway built upon a Strengths-Oriented Global Health Informatics Framework and an interdisciplinary standardized terminology, the Omaha System, along with a related translational process to disseminate best practices in diabetes care in China. This project demonstrates a novel strengths-oriented collaborative approach to disseminate best practices of diabetes management in global health communities and offers a potential to bring person-centered coordinated care to multi-levels of engagement that generate actionable and measurable results. Such collaboration opens a continued dialogue in the discourse for constructing global health informatics principles and practice to reduce the burden of diseases around the world.