Learner objectives: Evaluate the impact of educational interventions on nursing documentation efficiency and patient story clarity and apply lessons learned to similar practice improvement projects within their organizations.
Purpose: To evaluate the impact of a targeted educational intervention on inpatient nurse documentation efficiency, accuracy, and overall burden reduction, aligning with the national goal to reduce healthcare documentation burden by 25%.
Background/significance: Documentation inefficiencies identified through NEAT and KLAS data, along with staff feedback, revealed opportunities to improve efficiency in four main areas: electronic medical record (EMR) documentation, patient story clarity, reporting, and training. These inefficiencies contribute to clinician burden, detracting from direct patient care. Addressing these gaps supports both national regulatory priorities and organizational goals for improving nurse efficiency, satisfaction, and patient experience.
Methods: All inpatient nurses were assigned a scenario-based Amplifire gap finder course to assess knowledge of nursing process documentation best practices. Nurses scoring below 80% received additional in-person training through an instructor-led nurse process documentation course, facilitated by clinical educators, clinical nurse specialists, and nurse informaticists. Post-intervention, key performance indicators (KPIs) were measured using a combination of system-based metrics, manual chart audits, and observation by educators. Documentation efficiency was compared using NEAT/Signal data pre- and post-intervention.
Results: Analysis of a three-month pre-/post-comparison among nurses who completed the classroom intervention demonstrated notable improvements in documentation behaviors and system utilization:
Metrics: Documentation: Percent of nurses using flowsheet macros, 28% to 34%. Flowsheet rows documented per shift-hour (chart by exception), 63.84 to 57.59. Flowsheet documentation latency, 102.35 minutes to 91.07, 7.24%.
Notes: Time in notes per patient per shift, 7.34% decrease. Manual composition, 4% decrease. Time in care plan per shift-hour, 17.4% decrease.
Workload: Active time in system per patient per shift, 25.33 minutes 24.25 minutes, 1.99% reduction. Active time in system per shift-hour, 12.60 minutes to 11.91, 4.6% change reduction.
Clinical review: Time in patient reports per shift hour, 1.1 to 1.24 minutes, 5% change. Additionally, the “distance from peers” metric (variance from optimal performance benchmarks) narrowed substantially across multiple measures, suggesting improved alignment with best practice documentation behaviors.
Conclusions/implications for practice: The educational intervention successfully reduced documentation time and improved efficiency metrics, allowing nurses to spend more time on direct patient care activities. Early results indicate progress toward national documentation burden reduction goals and demonstrate the effectiveness of targeted, data-driven education paired with workflow optimization. These findings reinforce the value of collaborative efforts between nursing education, clinical informatics, and front-line nursing teams in advancing evidence-based documentation practices and improving the clarity of the patient story within the EMR.

Background: As artificial intelligence (AI) tools become increasingly integrated into health care, nursing students must be prepared to engage with these technologies ethically and effectively. Although today’s undergraduate nursing students belong to Generation Z and are often labeled “digital natives,” familiarity with consumer technology does not translate into digital health literacy or competence in emerging AI applications in health care. The rapid diffusion of generative AI tools has created a need for nursing education to provide purposeful instructions related to ethical use of AI tools. Nursing education must intentionally develop students’ ability to critically appraise AI outputs and implication for clinical decision making for professional practice.
Methods/teaching strategy: Faculty designed a structured learning activity embedded in a prelicensure nursing course that integrates the NURSES framework. As part of the assignment, the students use a generative AI tool to assist in developing a nursing care plan. Students then will critically appraise the AI-generated content for accuracy, appropriateness, and alignment with evidence-based practice. Additionally, the students evaluate the end user experience and reflect on the potential benefits, limitations, and ethical concerns associated with AI in health care.
Future implication/next steps: This assignment aligns with the American Association of Colleges of Nursing (AACN) Essentials domain 8: informatics and healthcare technologies, in promoting competence in using digital tools to support ethical person-centered care to patients. The process of embedding structured AI learning experiences within nursing curricula helps to prepare entry-to-practice nursing students to navigate the evolving digital healthcare landscape with ethical awareness, critical judgment and confidence in using technology in clinical practice.

Alert fatigue continues to pose a significant challenge in clinical environments, particularly when interruptive alerts within the electronic health record (EHR) are not thoughtfully designed or strategically implemented. Inefficient or poorly optimized alerts can disrupt clinical workflows, reduce user engagement, and undermine the effectiveness of clinical decision support (CDS) tools. To address this issue, the nursing “our practice advisories” (OPA) reduction initiative was launched in October 2024—a targeted, multi-phase effort aimed at evaluating, redesigning, and refining interruptive OPAs within the EHR to enhance nursing workflow and reduce cognitive burden.
Phase I of the initiative focused on empowering nurses to take meaningful action on OPAs by introducing enhanced acknowledgment options. These options allowed nurses to select context-specific reasons when the recommended action was not appropriate for the patient, thereby improving the relevance and usability of alerts. As a result, the percentage of successful OPAs—defined as alerts that led to appropriate clinical action rather than dismissal, increased from 7.39% in October 2024 to 49.47% in July 2025, demonstrating a substantial improvement in alert engagement.
Phase II emphasized the clinical refinement of OPAs to ensure that alerts were not only actionable and relevant but also aligned with nursing workflows and clinical intent. This phase involved collaboration with front-line nursing staff, informatics specialists, and CDS stakeholders to assess alert content, timing, and placement within the EHR interface. To date, the initiative has successfully eliminated over 138,784 interruptive OPAs, significantly improving alert management and streamlining nursing workflows. This ongoing work supports enhanced clinical decision-making, reduces unnecessary interruptions, and promotes EHR wellness across the care continuum. By delivering timely, context-aware alerts that integrate seamlessly into nursing practice, we aim to mitigate alert fatigue and foster a more intuitive and supportive digital environment for clinicians.

Background: The balanced scorecard approach has been widely used since its development in 1992 by Norton and Kaplan to assist healthcare establishments in linking organizational goals to measurable outcomes across clinical, financial, operational, and patient experience realms. Traditional balanced scorecards are often maintained in static spreadsheets or slide decks, requiring manual updates and limiting their ability to provide timely insights. The healthcare industry is more data driven than ever, from clinical patient records to financial transactions and human resources information. Because of this without centralized data, making informed strategic decisions is challenging. Business intelligence opens many opportunities to use data for different purposes with the common goal of making better informed decisions.
Methods: This project implemented a modernized balanced scorecard, going from not having any type of scorecard, to using a combination of Excel and PowerPoint to utilizing Microsoft Power BI within an independent urology practice. Data from multiple clinical, financial, and operational systems were integrated into a centralized data model. Performance metrics were updated monthly, and the dashboards were designed with user-friendly interfaces to provide easy access for practice administrators, physicians, and senior leadership.
Results: By giving providers, practice administrators and senior leadership instant access to key performance indicators, the modernized scorecard heightened transparency and increased accountability. This enabled the balanced scorecard to evolve from being handed out as PDF document monthly into a proactive tool for management, allowing for evidence-based decision-making and continuous quality improvements. The scorecard also provides objective data to back up performance discussions.
Conclusion: Modernizing the balanced scorecard through dynamic dashboards that pull from financial, clinical, and human resource systems transforms it into a proactive management tool in health care, improving data accessibility, timeliness, and alignment with strategic value-based care initiatives.

Falls are a significant problem at Nebraska Medicine and nationally. Each year, somewhere between 700,000 and 1,000,000 people in the United States fall in the hospital. A fall may result in fractures, lacerations, or internal bleeding, leading to increased health care utilization. Research shows that close to one-third of falls can be prevented. Fall prevention involves managing a patient's underlying fall risk factors and optimizing the hospital's physical design and environment.
According to national data reported by the Joint Commission, from January 1 to December 31, 2022, patient falls accounted for 42% of the reported sentinel events, with 5% resulting in death and 70% causing severe harm. The most common injuries reported were head injuries or bleeding, and hip or leg fractures. Patient falls while ambulating, falling from bed, and falling while toileting were the leading mechanisms for falling. At Nebraska Medicine between January 2021 and March 2023, 1,880 falls were recorded, with 40 classified as serious safety events/sentinel events.
Operational improvement and clinical informatics conducted a thorough assessment of the current workflow by observing staff members in action and interviewing key stakeholders. Several rounds of shadowing were conducted over a six-week period by observing staff members in action and interviewing key stakeholders such as nurses, providers, patient care technicians, NPDS, unit leads, supervisors, and managers.
Nebraska Medicine took the initiative to revamp the fall prevention program by utilizing validated tools and automation of nursing documentation. This allowed effective and efficient documentation for the nursing staff while ensuring that patient safety was still at the forefront. Through the revamp of the fall prevention program at Nebraska Medicine, there was an increase in accuracy related to the assessment tool as well as a decrease in patient falls with injury. When conducting post-go-live evaluations, the team found that there was confusion amongst nurses on interventions which led to simplification.
In October of 2024, the falls core team began the process to simplify the required interventions for bedside nursing staff to have a better understanding of the fall prevention program. At that time, the fall rate for falls with any injury for NMC and BMC was 1.15 per 1,000 patient days. The core falls team provided bedside nursing with education during competencies, as well as utilizing our mobility and safety committee members and unit leaders as subject matter experts on the simplification of fall risk level interventions and automation of the fall risk assessment tool. Our automated tool and simplification of interventions launched in March 2025. Fall rates for falls that resulted in any injury decreased overall for both NMC and BMC campuses. Fall rates combined for both campuses were reduced to 1.02 per 1,000 patient days in April, 0.99 per 1,000 patient days in May, and 0.97 per 1,000 patient days in June.

Efficient use of the electronic health record (EHR) is critical for safe, high-quality, patient-centered nursing care, yet variations in documentation practices and inconsistent training contribute to workflow inefficiencies, increased cognitive load, and frustration among nurses. Many nurses remain unaware of optimal workflows or system features that could save time and enhance documentation accuracy. The purpose of this initiative was to enhance EHR efficiency and strengthen nursing informatics competencies through a structured, systemwide EHR coaching program led by clinical informatics specialists. A multidisciplinary workgroup developed standardized educational materials and monthly coaching content focused on high-impact documentation workflows. These topics were selected based on nursing feedback and observational workflow assessments of areas where inefficiencies most frequently occurred.
A train-the-trainer model was implemented to prepare informatics specialists across multiple facilities to deliver consistent, evidence-based coaching. Training emphasized adult learning principles, communication strategies for bedside engagement, and practical application of EHR workflows to support real-time learning and retention. Beginning in mid-2025, the program was launched across ten acute care hospitals. Coaching sessions were delivered via multiple methods including purposeful rounding, unit huddles, shared governance meetings, and individualized sessions, allowing flexibility and accessibility for bedside nurses. Each month focused on a specific EHR competency, promoting sustained learning and reinforcing evidence-based best practices in documentation and workflow optimization.
To evaluate program effectiveness, post-coaching surveys were administered following each session. Over 1,000 hospital-based nurses, approximately 13.8% of the total workforce, participated, with more than 300 completing surveys that assessed confidence, satisfaction, and applicability of learning to daily practice. 90% of respondents strongly agreed or agreed that they felt more confident using the EHR tools and workflows discussed, sessions met their expectations, and they could apply what they learned to patient care. These outcomes are consistent with literature demonstrating that targeted, hands-on EHR education reduces workflow interruptions, decreases cognitive burden, and improves nursing performance. Furthermore, structured informatics competency training is associated with increased nurse engagement, confidence, and consistent documentation practices.
This initiative demonstrates that informatics-led coaching can provide measurable improvements in nursing efficiency and informatics competency. By leveraging clinical informatics specialists as educators and change agents, hospitals can foster standardized best practices, enhance nurse confidence, and promote sustainable improvements in patient care quality. Future coaching will focus on delivering 1:1 sessions tailored to nurses’ individual needs, personalizing the coaching experience to maximize learning, workflow improvement, and informatics competency development.

A nursing director was inspired to replicate a documentation optimization initiative after viewing a poster presentation at a professional conference. Upon returning to the organization, members from the hospital’s clinical documentation task force met, including a nurse informaticist, IT nursing analyst, and staff nurse. The team shared a unified goal to reduce the documentation burden on nurses while maintaining data quality and improving patient care outcomes. Excessive documentation contributes to workflow inefficiencies, nurse dissatisfaction, and reduced time for direct patient care. Recognizing this, the group sought to identify high-impact opportunities for change within the electronic health record (EHR). This initiative illustrates how engaging an interdisciplinary team and leveraging frontline feedback can lead to sustainable documentation improvements and measurable outcomes.
To ensure improvements reflected clinical realities, the task force surveyed nurses across all departments to identify documentation perceived as redundant, unnecessary, or of limited value. Survey results were categorized into three groups: 1) documentation elements governed by regulation or safety standards, 2) potential changes requiring significant redesign, and 3) achievable optimizations with moderate effort and minimal risk. The team prioritized the third category to achieve meaningful, immediate results that would reduce redundancy and streamline workflows.
The first optimization removed a recreational screening task that automatically fired every 24 hours. Review revealed the information was not used by any clinical area and provided no value to patient care. Its removal eliminated an average of 2,613 clicks per month and saved approximately 44 minutes per month. The second optimization addressed smoking status documentation. The team discovered that a reporting requirement prompting manual input was obsolete. System logic was implemented to auto-populate “N/A” for patients below a specified age, eliminating an average of 36,465 clicks per month and reducing manual documentation by 73%. This change saved 10.13 hours per month and streamlined workflow while maintaining data accuracy and compliance.
The most significant optimization targeted overdue tasks, a major source of frustration among nurses. Data analysis revealed that overdue medication and assessment tasks often persisted because nurses completed documentation in flowsheets without clearing associated tasks. This inflated overdue task counts and increased the risk of incomplete handoffs. The team collaborated with nursing leadership, risk management, and medication safety to implement workflow education and adjust task-retention times. As a result, overdue medication tasks decreased by 30%, from a weekly average of 165 to 115. Additionally, the IT nursing analyst developed rules to automatically clear five recurring assessment tasks once corresponding documentation was completed in the flowsheet. This enhancement reduced overdue tasks by 55% (from 348 to 155 weekly) and eliminated approximately 965 unnecessary clicks each week.
This initiative demonstrates that meaningful documentation optimization can be achieved through an interdisciplinary, data-driven approach. By engaging front-line nurses in identifying redundant documentation and collaborating with the nurse informaticist and IT nursing analyst, the team achieved measurable outcomes that improved efficiency, reduced documentation fatigue, and enhanced patient safety. Most importantly, these changes allowed nurses to spend more time on what matters most—direct patient care.

As artificial intelligence (AI) emerges as a transformative force in health care, its integration into nursing practice has profound implications for the therapeutic nurse–client relationship, professional competency, and ethical care delivery. This poster will explore how AI technologies can enhance—yet also challenge—the foundations of nursing practice, emphasizing that AI can support nursing practice but does not replace clinical knowledge, professional judgment, or human connection.
The use of AI in clinical decision-support systems, predictive analytics, and documentation workflows has the potential to improve efficiency, accuracy, and patient outcomes. However, the introduction of AI into the nurse-client relationship requires careful consideration to ensure the maintenance of trust, empathy, and person-centered care. Nurses must remain vigilant in communicating clearly with patients, preserving relational continuity, and ensuring that technology does not create distance or reduce individualized care.
Nursing competency is vital when incorporating AI-enabled tools. Competency involves not only technical proficiency, but also understanding AI limitations, potential risks, and mechanisms to evaluate output validity. This poster will highlight the importance of education, policy support, and competency frameworks to prepare nurses to critically appraise AI recommendations and integrate them safely into practice.
Key ethical considerations include legal compliance, informed consent, cultural safety, and transparency regarding AI involvement in care. Patients must understand how AI informs their care and consent to its use, while nurses must evaluate whether AI outputs reflect cultural responsiveness and avoid reinforcing existing systemic biases. Heightened awareness and active mitigation strategies are essential to address bias and ensure equitable AI-supported care delivery.
Ultimately, AI offers powerful tools that can strengthen nursing practice, but it must be grounded in ethical principles, cultural humility, regulatory compliance, and unwavering commitment to the therapeutic nurse-client relationship.

Artificial intelligence (AI) has the potential to be a transformative tool in health care, offering solutions to prevent hospital-acquired pressure injuries (HAPIs), which cost the US healthcare system billions of dollars a year. This quality improvement project evaluated the value of an AI-enhanced video monitoring system in reducing HAPI incidence within a medical-surgical unit. The AI model identified patients at risk, and a daily positional report was generated to guide nursing interventions for patients with a Braden scale score of 15 or less. Over the course of eight weeks, pre- and post-intervention data were analyzed using both descriptive and inferential statistics. While the intervention did not show a statistically significant reduction in stage III and IV HAPIs, the outcomes were clinically important, indicating improved patient care. Nurse survey results reflected positive attitudes with the use of AI in identifying immobility, satisfaction with workflow integration, and no concerns regarding privacy and ethics. Limitations included the absence of real-time tele-nurse monitoring and a small sample size. Using Locsin’s theory of technological competency as caring in nursing (TCCN), this project highlights the balance between technology and patient care. Future studies incorporating real-time alerts and larger populations are recommended to validate and strengthen AI-supported HAPI prevention strategies.

Purpose: This project aimed to implement a Closed-loop critical lab communication system within the electronic health record (EHR) to enhance care across a hospital system. The system introduces a push notification feature that enables providers to acknowledge critical lab values with a single click—streamlining communication and reducing delays. While the concept has garnered interest across healthcare institutions, few have successfully integrated it into their EHR workflows. By automating this process, clinical staff—including nurses, unit secretaries, and laboratory personnel—can focus more on their core responsibilities rather than acting as intermediaries.
Background and significance: Timely communication of critical lab results is vital to patient safety. Within the inpatient units, the previous workflow involved multiple handoffs—lab staff contacted unit secretaries, who relayed messages to nurses, who then paged providers. Within the outpatient areas, lab staff relied on nursing to relay messages to providers when providers failed to answer a phone call. These manual processes were prone to delays and inefficiencies, often resulting in frustration and increased workload. The closed-loop system offers a novel, technology-driven solution to a long-standing challenge, enabling direct, real-time communication that many institutions have yet to achieve.
Methods: An interdisciplinary team—comprised of clinical informaticists, providers, nurses, lab managers, EHR analysts, and technical support—mapped all communication pathways based on existing policies. To comply with lab delivery time standards, providers were given a seven-minute window to acknowledge notifications. A contingency protocol involving sequential phone calls ensured delivery if the initial notification went unanswered. Once the EHR build was ready in the test environment, the team conducted comprehensive testing across all communication scenarios.
Training and education: Training efforts targeted three key groups: 1) nursing staff: leadership presentations, nursing EHR superuser committee sessions, and a pre-launch email blast; 2) providers: presentations to leadership and specialty user groups, rounding in units and clinics, and a targeted email campaign; and 3) laboratory staff: direct engagement and workflow integration support.
Results: Five months post-implementation, approximately 70% of critical lab results are acknowledged via push notification. The average time to acknowledgment is just two minutes—dramatically faster than the previous 13-minute average for lab-to-nurse communication, which excluded provider notification time. Providers have expressed strong support for the system, noting the convenience of receiving results immediately and avoiding phone tag with nursing staff. Unit secretaries report having more time to focus on administrative duties, while laboratory staff are fielding significantly fewer follow-up calls.
Conclusion: This project demonstrates that closed-loop communication for critical lab values is not only feasible but transformative. As one of the first implementations of its kind, it significantly reduces notification time, enhances patient safety, and alleviates the communication burden on clinical staff—setting a new standard for care delivery.