Purpose: To demonstrate how nurse informaticists can apply traditional and non-traditional leadership strategies to empower clinical transformation, using a professional billing and charge capture project as the model. Even in the electronic age of 2025, providers at this pediatric hospital were using paper forms and emailed spreadsheets to submit professional billing and related diagnosis codes. This project transitioned the professional billing system from a manual system to an integrated EHR electronic billing system. This shift required strong nurse informatics leadership and mentorship to align stakeholders and support clinical adoption.
Description: The professional billing and charge capture project aimed to automate professional billing workflows across pediatric specialties by creating an EHR department context dedicated to professional billing. The traditional leadership method of structured governance was complemented by adaptive strategies like real-time rounding, contextual workflow design, and informal storytelling. Nurse informaticists used hands-on training, tip sheets, and quick feedback loops to support provider adaptation and technology alignment. These efforts were grounded in evidence-based leadership strategies and supported by mentorship models that empowered informaticists to function as change agents.
During this transition, a significant design challenge emerged: specialty-specific expectations for template configuration. Hospitalists prioritized streamlined templates for rapid documentation, while specialists advocated for detailed, procedure-specific fields. These competing priorities created tension around usability, cognitive load, and billing integrity.
This challenge underscores a critical leadership insight: billing workflows are not one size fits all. Nurse informaticists played a pivotal role by leading collaborative design, analyzing workflows, and applying evidence-based human factors principles. The final solution incorporated role-based customization, balancing efficiency for hospitalists with the granularity required by specialists.
Evaluation/outcome: The transition from paper and email-based billing to an integrated EHR electronic billing system eliminated manual processes, improving accuracy and efficiency. Within 2 months post-go-live, the revenue cycle department was able to reduce charge entry staff by 1 FTE and decrease charge lag. Organizational empowerment emerged through collaboration across revenue cycle, informatics, and clinical teams, with clinical informatics RNs guiding physician leaders in designing the EHR billing department context. The organizational culture has shifted to one where the physician expectation is that all billing and charges will be completed electronically.
Post-go-live transition: Initially, the clinical informatics RNs ran reports and provided data on incorrect billing to medical staff leaders. One key outcome was training the revenue cycle team to run these reports independently and then transitioning this responsibility to that department, supporting sustainability and role clarity. A structured process for updating billing codes and training new providers was established to provide for ongoing system changes.

Purpose: Advances in healthcare technology demand that nurses possess robust informatics competencies to optimize patient outcomes, support data-driven decision-making, and enhance clinical workflows. Although the American Association of Colleges of Nursing revised its Essentials to emphasize informatics and healthcare technologies as a core domain, standardized tools to evaluate these competencies remain limited. The purpose of this evidence-based practice project was to develop and validate a prototype assessment tool designed to measure nursing informatics competencies and program outcomes within a competency-based education (CBE) framework.
Description: The project goal was to develop, pilot, and refine an assessment tool called the nursing informatics competency evaluation (NICE). Guided by CBE principles and aligned with the AACN Essentials and ANA Nursing Informatics: Scope and Standards of Practice, phase 1 involved a comprehensive literature review and expert consultation to identify key domains and measurable indicators of informatics competency. A group of informatics educators assessed content validity to ensure item relevance and clarity. The preliminary NICE instrument integrated evidence from established frameworks including the technology informatics guiding education reform (TIGER) initiative and validated scales such as the self-assessment of nursing informatics competencies scale (SANICS) and TIGER-based assessment of nursing informatics competencies (TANIC). Phase 2 pilot testing will be conducted with a convenience sample of master’s-level informatics students at a large academic institution in the fall of 2025. Participants will complete the online NICE survey, providing both quantitative competency ratings and qualitative feedback on tool usability. Statistical analysis will assess reliability (Cronbach’s alpha) and construct validity, with iterative refinement based on findings.
Evaluation/outcome: Preliminary results are expected to demonstrate acceptable levels of internal consistency (α ≥ 0.70) and strong content validity across identified AACN domain 8 informatics sub-competences. The pilot data will inform psychometric refinement and usability optimization. The final validated tool will provide a reliable mechanism to evaluate competency attainment and program effectiveness, supporting both formative and summative assessment within CBE-aligned curricula. Findings will be reported in spring 2026 and will contribute to national dialogue on standardizing informatics competency evaluation and establishing benchmarks for advanced nursing education. Long-term, the NICE tool may be adapted for broader use across nursing programs to ensure alignment with national standards and evolving workforce needs.
Implications for practice: This project supports the advancement of nursing informatics education through the integration of evidence-based evaluation strategies. By providing a valid and reliable tool for competency assessment, nurse educators can better track student progression, guide curricular improvements, and strengthen workforce readiness in data-driven healthcare environments. The NICE tool aligns with the AACN Essentials and ANIA’s mission to promote innovation and excellence in nursing informatics practice.

Background: In the fall of 2023, the pediatric post-operative anesthesia care unit (PACU) nursing staff identified issues with documentation burden and requested assistance from Nursing Informatics. During a site visit by the Epic chief nursing informatics officer, a deep dive was conducted, and opportunities identified. Research reveals this was not just limited to pediatrics.
Objective: A project was launched to address the problem of inefficient and redundant documentation in for all PACU areas at Vanderbilt University Medical Center. This increased burden contributes to increased workload, potential for errors, and decreased focus on patient care. Reduce time charting and number of clicks for PACU nurses. Improve compliance and reduce confusion of necessary documentation in PACU. Improve continuation of care for patients staying overnight.
Methodology: This project involves a work group of subject matter experts from each PACU. Nursing informatics and analyst analyzing a comprehensive review of current PACU flowsheets will identify and eliminate redundant data elements, such as assessment done in other sections of the flowsheet rows. The PACU intervention section will be removed, as interventions are more effectively documented within other section of the flowsheet row. System assessment definitions will be revised to incorporate the Saba models of care approach. SOPs will be developed based on evidence-based practices and regulatory guidelines (ASPA and Joint Commission) to standardize documentation and improve consistency.
Results: Preliminary post-go-live analysis (March–May 2025) shows a 43% overall decrease in free-text comments across all PACU documentation. Specific improvements include: Neuro assessment flowsheet: 75% decrease in free-text comments. Fall risk/safety risk assessment: 60% decrease following added row information and focused education. Skin assessment flowsheet: 44% decrease after defining expected post-surgical incision appearance. Additional physiological system assessment categories showed decreases ranging from 42% to 61%. User log data analysis indicates an average time savings of 15 seconds per nurse per patient encounter, which is statistically significant at p

Amid persistent nursing staff shortages and increasing clinical demands, health systems face an urgent imperative to deploy innovative technological solutions to support staff and improve operational efficiency. Virtual nursing (VN) is an emerging technology-enabled, hybrid care delivery model where virtual nurses remotely assist bedside nurses (BN) with key nursing tasks (e.g., admission, discharge, patient education, etc.) to improve patient care delivery.
This pilot project aimed to assess the impact of VN on nurses’ workforce well-being and task times on a telemetry and surgical unit at XX, a 9-community hospital health system in the United States.
A pre-/post-design was used. After informed consent, virtual and bedside nurses completed an electronic survey pre- and two months post-VN implementation. Surveys used items adapted from validated instruments, including measures of burnout (MBI), technology acceptance (TAM), quality of care, self-care, breaks, time management, hourly rounding, and overall satisfaction. VN tracker was used to collect data on VN time spent on admission, discharge, audits, dual signoffs, patient education, and safety checks. Paired t-tests and McNemar’s chi-squared tests were conducted to examine pre and post differences.
The VN pilot began in mid-June 2025 but paused due to technical and equipment issues, resuming in August 2025 (site 1) and September 2025 (site 2). A total of 34 bedside and 11 virtual nurses participated across both units. One unit has completed data collection; final results will be available at the end of 2025. Two-thirds of bedside nurses had 1-6 years of nursing experience (n=16); 60% of virtual nurses had 10+ years of nursing experience (n=6). Post-implementation, BNs reported consistent hourly rounding on their shifts (p = 0.015). VNs showed significant increases in perceived usefulness (p = 0.001), ease of use (p=0.016), intention to use (p=0.04) and overall satisfaction with VN technology (p=0.001). No significant changes were observed in burnout, self-care, taking breaks, and time management.
Across both units over two months, on average, VNs spent time on admissions (51.6 mins), discharges (18.7 mins), safety assessments (34.1 mins), chart audits (50.7 mins), dual-sign-offs (10.6 mins), pain assessment (4.7 mins), patient education (17 mins), and other tasks (40.1 mins).
Initial pilot results suggest that VN can assist bedside nurses with key nursing tasks which may free time for hourly rounding and other nursing duties requiring physical care. Acceptance and satisfaction with VN technology show positive nursing feedback; however, impact on well-being was not observed, suggesting nursing is multi-faceted and cannot be singularly addressed by VN. While the pilot phase shows initial positive trends, further assessment will measure sustained impact and inform potential scalability planning.

Purpose. This study synthesizes current evidence on how data analytics enhances nurse managers’ administrative decision-making (ADM) and identifies innovative approaches through which data analytics drives transformation of leadership and management practices within digital healthcare environments. The aim is to demonstrate how integration of data analytics advances nursing management decision-making practices toward data-informed, proactive, and strategic leadership.
Background/significance. Data analytics is rapidly reshaping healthcare leadership by facilitating conversion of raw data into meaningful insights that inform organizational and clinical decisions. Nurse managers—who play a critical role in resource allocation, care quality improvement, and nursing workforce engagement—are increasingly challenged to navigate large and complex data environments. The ability to interpret data analytics outputs and apply them to management decisions represents a new frontier in nursing leadership, and evidence describing the practical and measurable impact of data analytics on ADM remains fragmented. However, understanding how data analytics supports nursing leadership reasoning across the descriptive, diagnostic, predictive, and prescriptive levels can support development of informatics competencies and evidence-based decision-support structures essential for the digital healthcare era.
Methods. We conducted a systematic review to identify empirical research on data analytics use to support nurse managers’ decision-making or administrative processes. Following PRISMA guidelines, we searched for relevant studies published in English between 2019 and 2024 in PubMed, CINAHL, MEDLINE, and Embase. 21 studies encompassing eight research designs were included, and each was appraised for quality using specific validated tools. Data were extracted and synthesized in narrative form, emphasizing the level of data analytics applied, managerial roles supported, and outcomes illustrating how data analytics fosters innovation in nursing management. Results. Review findings revealed diverse applications of data analytics across four levels—descriptive (n = 4), diagnostic (n = 2), predictive (n = 9), and prescriptive (n = 1)—and in integrated multi-level approaches (n = 5). Data analytics was used to enhance patient care quality, strategic management, workforce engagement, and nursing management during health crises. Predictive and prescriptive models—such as machine-learning algorithms for turnover forecasting and simulation-based scheduling—showed improved decision-making efficiency and strengthened organizational resilience during crisis situations (e.g., pandemic surge capacity).
Conclusions/implications. Integrating data analytics within nursing management constitutes a transformative innovation that bridges the data–information–knowledge–wisdom (DIKW) continuum. Data analytics can help empower nurse leaders to translate complex data into actionable visions, improving strategic foresight, decision-making quality, and workforce stability. To sustain innovative use of data analytics in nursing management, we should: 1) strengthen analytical and informatics competencies among nurse managers through targeted training and continuing professional development, 2) advance interoperability and data infrastructure to facilitate timely and transparent, and 3) foster a culture of data-driven leadership that values interdisciplinary collaboration.

Purpose: This project aims to evaluate and optimize blood transfusion documentation in the electronic medical record (EMR) to reduce error rates and inefficiencies in the delivery of blood from the blood bank to nursing. The motivation behind this project stems from the critical importance of accurate and efficient blood transfusion documentation in ensuring patient safety and improving healthcare outcomes.
Background/significance: The background for this initiative is rooted in the American Nurses Association's principles for nursing documentation, which emphasize the need for accurate and clear representation of nursing care. The hypothesis driving this project is that by updating the electronic blood transfusion documentation form, enhancing decision support alerts, implementing an order validation alert, and increasing educational opportunities for nurses, we can significantly reduce documentation errors and improve the efficiency of blood transfusion processes.
Methods: The project followed a systematic approach to evaluate and optimize blood transfusion documentation in the electronic medical record (EMR). The procedure involved analyzing consistent monthly errors in documentation to identify breakdowns in workflow. The steps taken included updating the electronic blood transfusion documentation form to make it easier to complete the transfusion, enhancing decision support alerts to be more actionable in the nursing workflow, implementing an order validation alert to prevent requests for blood before it is ready, and increasing educational opportunities for nurses to understand proper documentation procedures. Collaboration across departments was essential in tracking and evaluating data, ensuring efficient use of knowledge and expertise to reduce errors in blood documentation.
Results: When comparing pre- and post-intervention data on the number of uncompleted blood documentation errors and total blood documentation errors, there was a significant decrease in the number of errors each month. This indicates that the steps taken to improve blood documentation and alerts were highly effective in reducing error rates per month. Additionally, the order validation alert reduced premature blood requests, improving workflow efficiency between nursing and the blood bank.
Conclusions/implications: The collaborative efforts and systematic approach taken in this project have demonstrated that interdisciplinary cooperation and targeted interventions can significantly enhance the accuracy and efficiency of blood transfusion documentation. These improvements not only reduce errors but also contribute to better patient care and streamlined workflows. The larger implications of this work suggest that similar approaches can be applied to other areas of healthcare documentation, leading to broader improvements in patient safety and care quality. By addressing documentation errors at their source, we can create a more reliable and efficient healthcare system, ultimately benefiting both healthcare providers and patients.

Purpose: In the evolving landscape of ambulatory infusion nursing, traditional electronic health record (EHR) documentation models, borrowed from inpatient settings, have proven suboptimal. Inpatient assessments prioritize granular details for acutely ill, unstable patients. However, ambulatory oncology care infusion patients present a distinct profile: they are generally more stable but face ongoing challenges from treatment-related toxicities that affect daily functioning and therapy adherence. As healthcare systems transition higher-acuity therapies to ambulatory care environments to enhance operational efficiency, the need to refine nursing assessments becomes critical. Conventional models capture organ toxicities (e.g., bone marrow, cardiac, renal, and hepatic) but inadequately address functional and self-care deficits stemming from prevalent symptoms like fatigue, nausea, and neuropathy. These gaps can hinder patients' ability to tolerate and complete prescribed regimens, potentially leading to increased hospitalizations, treatment interruptions, or suboptimal outcomes.
Description: Recognizing these limitations during a planned EHR platform migration, a nursing team advocated for an innovative integration of the National Cancer Institute (NCI) common terminology criteria for adverse events (CTCAE), also known as the common toxicity criteria. CTCAE provides a standardized framework for grading adverse events (AEs) associated with cancer therapies. Toxicities are graded on a scale of 1 (mild) to 4 (life-threatening), with parameters tailored to specific organ systems or symptoms. This system is widely used in clinical trials and chemotherapy management by providers to ensure consistency in reporting and dosing adjustments. For the ambulatory care infusion context, the criteria were modestly adapted to the ambulatory care infusion setting focusing on the most common side effects: nausea, vomiting, fatigue, diarrhea, constipation, peripheral neuropathy, insomnia, mood alterations, mucositis, dysphagia, dyspnea, cough, and rash. These graded assessments were embedded into standard body-system-organized categories within the EHR and were described as a straightforward build.
Outcome: Implementation yielded improvements in documentation efficiency and clinical utility by streamlining workflow. Staff nurses reported that the new assessment tool was intuitive and user-friendly, was less time intensive, and reduced the volume of narrative documentation. By quantifying symptom severity through CTCAE grades, it minimized subjective variability and enabled easier tracking of changes via flowsheet views. Moreover, it fostered real-time patient education, as nurses could discuss self-care tips for patients and assisted in empowering patients to recognize and report escalations. Inter-encounter handoffs improved, as the CTCAE grading criteria allowed for patient-reported toxicity levels to inform care continuity.
The project's reception has been overwhelmingly positive, extending beyond the initial oncology-focused infusion site. Non-oncology ambulatory care infusion units have requested similar EHR builds, indicating broader applicability across infusion specialties. Notably, providers—who historically overlooked nursing notes—now are engaging to have access to the nursing assessment which is inclusive of the universal CTCAE language that aligns with medical literature. This shared vocabulary enhances interdisciplinary communication, informing treatment decisions and monitoring patient tolerance to regimens.

Breast cancer is the most prevalent form of cancer among females and is a leading cause of cancer deaths. About 80% of breast cancer patients take oral endocrine therapy (OET), which increases survival, improves quality-of-life (QOL), and decreases healthcare costs. Despite the positive implications of OET, many patients are non-adherent to their medication. The purpose of this study is to create a model that affects OET-taking behaviors in women with breast cancer using data mining. The World Health Organization’s model of factors that influence medication adherence (i.e., patient-related, condition-related, therapy-related, social/economic-related, and healthcare team/system-related factors), will be used to delineate multi-level determinants of OET adherence through the lens of Bronfenbrenner’s ecological system theory. Exploring multi-level determinants and developing a model in large samples across diverse populations is critical for understanding OET adherence. A total of 141,457 samples will be surveyed, from January 2019 to December 2019 in surveillance, epidemiology, and end results (SEER) Medicare database. Multiple logistic regression and decision tree approaches will be utilized. Preliminary result demonstrated that OET-NA was significantly affected by patient-related factors of ethnicity and psychological issues, socioeconomic-related factors of marital status, and lifestyle, therapy-related factors of switching OET medications and increased number of drug therapy experiences, condition-related factors of cancer stage and comorbidities, and healthcare-team/system-related factors of characteristics of healthcare-team and system. Creating a model of OET adherence will be the first step in developing and testing interventions to improve OET adherence with breast cancer, which has the potential to decrease morbidity and mortality and increase QOL.

Purpose: To evaluate quality improvement initiatives to increase the use of an electronic health record (EHR) embedded self-service reporting portal, SlicerDicer, amongst nurse informaticists within a large healthcare system.
Background/significance: Data-related competencies in nursing practice are crucial for transforming data into information, knowledge and wisdom. Many nurses lack the skills to retrieve, analyze, aggregate, and interpret data, which are essential to evaluate outcomes, inform decisions and advancing the discipline. Nursing should not only collect data but convert it into information and actions for improvement. Utilizing data to facilitate open and transparent discussions about improvements between nursing leaders and staff is essential. However, the use of the SlicerDicer tool among nurse informaticists in the system is low, limiting access to relevant data and knowledge for informing decisions, practices, and quality improvement initiatives. Enhancing nurse informaticists' competencies in using the SlicerDicer tool could improve data extraction and translation into knowledge.
Methods: Using purposive sampling, 22 nurse informaticists voluntarily participated in a virtual,1.5-hour case-based, hands-on training session on SlicerDicer. Reaction was assessed using both qualitative and quantitative data. Learning was evaluated by comparing pre-to-post training self-assessment of data related competencies using a paired t-test. Behavior changes were measured by comparing frequency of the use of SlicerDicer tool by the participants pre-to-post training. Identifiable participants’ usage of the tool was securely stored and only available to the DNP project team.
Results: Participants had favorable reaction to the learning as supported by overall ratings between agree and strongly agree to statements about the training intervention. Some participants shared recommendations for more training and varied content. There was statistical significance in the differences in the responses of self-perceived competencies before and after the training intervention, using Mann Whitney tests with p values

Purpose: Unplanned electronic health record (EHR) outages are more than technical disruptions—they can compromise clinical workflows, remove decision-support safeguards, and threaten patient safety. Effective downtime preparedness requires not only available tools but also staff competence and coordinated response. This initiative aligns with the ANIA conference theme, “Honoring Tradition, Advancing Care,” by combining long-standing principles of clinical readiness with innovative, technology-enabled strategies to protect patients and maintain continuity of care in a highly digital environment.
Description: A comprehensive needs assessment identified significant gaps in readiness: only 60% of downtime boxes were adequately stocked, 30% of business continuity devices were nonfunctional, and staff familiarity with downtime tools was limited. Departmental variations in the process further reduced efficiency and consistency.
A multidisciplinary team of clinical informaticists, emergency management leaders, and nurse educators implemented five key interventions: 1) workflow standardization: creation of a concise, system-wide downtime checklist and standardized documentation forms; 2) technology optimization: restocking and color-coding downtime boxes, verifying and expanding functional business continuity workstations; 3) targeted education: development of interactive downtime simulation modules emphasizing patient identification, communication workflows, and recovery processes; 4) ongoing monitoring: establishment of monthly audits with data-driven feedback loops; and 5) equity integration: inclusion of role-specific support to ensure resource accessibility and readiness across all departments.
Evaluation/outcome: Outcomes demonstrated measurable improvements in organizational readiness and staff confidence. Downtime box readiness increased to 99%, functional business continuity devices reached near full operational capacity, and audit compliance improved to 95% within seven months.
In the pilot emergency department downtime training program, perceived preparedness improved across all domains. Confidence in printing from downtime workstations increased by 83%, and confidence in performing clinical information recovery rose by 62%. Each preparedness measure improved by at least 1.03 points (p < 0.001). Reported use of business continuity workstations increased from 48% to 92%, and awareness of downtime folders rose from 52% to 81%. Familiarity with red electrical outlets increased from 89% to 100%. These improvements were sustained over multiple months, and patient care remained uninterrupted during actual outage events. By merging proven preparedness practices with adaptive, technology-driven solutions, this initiative demonstrates how honoring tradition can advance care across complex health systems.
Learning outcomes: Describe the process of developing and implementing a standardized, technology-enabled downtime workflow that safeguards patient safety. Identify approaches to leveraging innovation to strengthen resource readiness, staff competence, and continuity of care during EHR outages. Discuss strategies for integrating simulation-based training, continuous feedback, and equity principles into healthcare technology optimization initiatives.