ANIA 2026 Annual Conference

P21 - Making Telehealth Visits Is Positively Associated with Engagement in Walking as Exercise in Older Adults

Shamatree Shakya, PhD, RN

Updated: 03/05/26

Background: Walking is the simplest form of physical activity, with many positive health benefits for older adults. Despite this, many older adults are inactive, and most older adults do not meet the minimum physical activity level of at least 150-300 minutes per week, as recommended by the physical activity guidelines. Prior clinical interventions examining the influence of structured physical activity programs incorporating digital technology have shown that these programs resulted in immediate improvements in walking and physical activity levels among older adults. However, the long-term impact of these structured physical activity interventions with digital technology on the physical activity levels of older adults is found to be low to none. One promising solution to sustainably promote walking in older adults is to investigate the association between the use of existing digital health technology and walking in this group.
Methods: This cross-sectional study used data from the National Health and Aging Trends Study (Year 2022) involving Medicare beneficiaries (65 years and older, N= 4562). Digital health technology use included making telehealth visits, managing Medicare or insurance data, scheduling medical appointments and filling prescriptions online, and accessing health information online. The association between digital health technology use and walking as exercise in the previous month was examined using logistic regression, adjusting for sociodemographic characteristics, morbidities, lower limb strength, and self-rated health. We used R 4.4.2 to conduct these statistical analyses.
Results: Most older adults reported using digital health technology to make telehealth visits (51.9%), handle insurance data (42.5%), schedule medical appointments and fill prescriptions (30.1%), and access health information (25.8%). Telehealth visits (aOR = 1.41; 95% CI = 1.14-1.75) were positively associated with walking as exercise in the previous month among older adults, after controlling for sociodemographic and clinical characteristics. Among the covariates, non-smoking, higher self-rated health, and osteoporosis were positively associated with walking, while reduced lower limbs strength and arthritis were negatively associated with it.
Conclusion: Our findings suggest that making telehealth visits enhances engagement in walking as exercise among older adults. Longitudinal studies are needed to investigate the association between telehealth visits and walking trajectories in older adults. This finding highlights the need to develop and evaluate telehealth-based interventions that promote walking and objectively track walking activity.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P22 - Human-Centered Artificial Intelligence to Reduce Nursing Workload and Burnout: A Fellowship-Supported Quality Initiative

Alexis Collier, DHA, MHA, CALA

Updated: 03/17/26

Updated: 03/05/26

Nursing workload remains a persistent driver of burnout, documentation fatigue, and turnover across healthcare systems. Traditional workflow interventions have focused on staffing ratios or process redesign but have rarely leveraged existing electronic health record (EHR) data to identify documentation inefficiencies. The purpose of this project is to design and evaluate a human-centered artificial intelligence (AI) framework that applies secondary analysis of de-identified EHR audit logs to model nursing workload and recommend data-driven strategies for improved efficiency and well-being.
This quality improvement initiative was developed under the AIM-AHEAD CLINAQ (Clinicians Leading Ingenuity in AI Quality) fellowship. The project uses secondary datasets derived from de-identified EHR audit logs containing documentation time, chart navigation, and communication activity metrics. By analyzing temporal and sequential data patterns, the model identifies redundant documentation loops and time-intensive behaviors contributing to nurse fatigue.
The design integrates principles of human-centered AI, emphasizing transparency, interpretability, and respect for nursing judgment. The project framework draws on the American Nurses Association (ANA) guidelines for trustworthy AI in nursing and aligns with HIPAA/HITECH requirements for secondary data use. Ethical safeguards include exclusion of personal identifiers, bias assessment, and secure data handling under controlled research infrastructure.
The analysis incorporates machine learning techniques such as clustering and pattern recognition to visualize workload distribution and predict high-burden documentation intervals. Model interpretability is ensured through feature-importance analysis and explanatory summaries that can be translated into workflow improvement recommendations. The ultimate goal is to produce actionable insights that augment nurse decision-making while protecting data integrity and privacy.
Evaluation focuses on three domains derived from secondary metrics within the EHR dataset: 1) Efficiency: Reduction in average documentation time per patient encounter following workflow redesign. 2) Performance variation: Distribution analysis of documentation frequency and task-switching patterns across clinical units. 3) Quality indicators: Relationship between documentation efficiency and care continuity measures such as handoff completeness.
Results will quantify documentation inefficiencies and highlight modifiable workflow factors. Pilot testing of the model outputs within simulated environments will inform the development of AI-enabled dashboards for future deployment. Findings will contribute to a scalable framework for using secondary data to support workforce optimization and ethical AI integration in clinical informatics.
Learning outcome: Participants will describe how secondary EHR data can be leveraged to develop and evaluate AI-driven workload optimization strategies that enhance nursing efficiency and reduce burnout.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P23 - Co-Leading with Pharmacy: A Novice Nurse Informaticist’s Playbook for Smart Pump Adoption

Maria Belinda Herrera, MSN, RN, NI-BC

Updated: 03/05/26

Learning outcomes: 1) Describe the importance of interdisciplinary collaboration on smart infusion pump project implementation. 2) Identify and prioritize high-impact actions a novice nurse informaticist can take across an end-to-end implementation to increase efficiency and influence. 3) Apply a cross-disciplinary communication plan to reduce training burden and close communication loops during go-live.
Purpose: Share lived lessons from a novice nurse informaticist perspective while co-leading a systemwide smart infusion pump implementation in a nurse-pharmacist co-lead model, demonstrating how intentional interdisciplinarity improved visibility, standardization, and safe adoption across facilities.
Description: Smart IV pump implementation affects nearly every discipline, yet the work involved often occurs in silos. In this case, a legacy to new platform conversion, a concurrent new facility build, and newly established clinical informaticist roles multiplied the complexity. From a novice nurse informaticist perspective co-leading with pharmacy, four pragmatic tactics were distilled to move the project from intention to execution.
1) Define the scope and claim the lane: In a newly established role, others may define scope by default, increasing the potential for workflow gaps. The novice nurse informaticist drafted a concise scope statement (what is owned, consulted on, or not owned), socialized it with leaders, and referenced it in forums. When invitations were absent, the informaticist self-identified the role’s value and requested inclusion. A small coalition (mentor, peer ally, executive sponsor) reinforced the scope and unblocked decisions.
2) Show up everywhere, early: Instead of waiting for invitations, proactively join “adjacent” forums (biomed/clinical engineering, pharmacy operations, materials management, IT change control, nursing education, risk/quality) to surface hidden dependencies (e.g., tubing/set availability, drug-library naming conventions, policy timing) and accelerate informed decisions.
3) Verify and amplify: don’t rely on one messenger: Communication breakdowns frequently traced back to single-channel relays. Formal updates were paired with brief, direct summaries to charge nurses, educators, and vendor teams; closed-loop checks were used before critical cutovers; and a simple “who needs to know today?” roster reduced last-minute surprises.
4) Shared governance means everyone: Drug-library decisions, policy updates, deployment waves, and education plans were owned in a cross-disciplinary forum including nursing, pharmacy, biomed/clinical engineering, IT, education, materials management, quality and safety, and the vendor. This broadened accountability, reduced silo friction, and created a durable venue for post-go-live optimization.
Evaluation/outcome: Following workflow standardization and cross-disciplinary governance, drug-library compliance increased from ~70% to >95%. Units reported fewer basic-mode infusions and fewer last-minute at-the-pump edits (early safety signals). Front-line feedback cited clearer escalation pathways and faster issue resolution during and after go-live. The shared-governance forum persisted beyond go-live, enabling rapid iteration of the drug library and supporting sustained adoption across facilities, directly aligning with the impact on practice track’s focus on optimizing workflows and safe technology adoption.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P24 - Revamping Nursing Informatics Course for Nurse Educators: Integrating AI Tools to Prepare Microlearning Content

Shamatree Shakya, PhD, RN

Updated: 03/05/26

Purpose: The American Association of Colleges of Nursing is championing competency-based education for nurses, which emphasizes an active learning process to translate knowledge, skills, and attitudes into measurable outcomes. This shift in focus necessitates adjustments to the existing informatics curricula/courses for nurse educator programs, enabling novice educators to develop competencies to prepare evidence-based interactive educational content. With widespread access to technology, microlearning can be a powerful means of delivering evidence-based, interactive educational content tailored to the target audience in academic, healthcare, and industry settings. However, the current informatics course for nurse educators has not fully explored the use of informatics and AI tools to develop timely, engaging, and personalized microlearning content. The poster presentation will outline the proposed revisions to the nursing informatics course within the Master in Nurse Educator program, aiming to achieve competencies in using informatics and AI tools to prepare microlearning content, and discuss the potential impact on nursing education and practice.
Significance: Key areas of focus include 1) revamping the existing informatics curriculum/ course for the MSN nurse educator program to align with competency-based education model; 2) introducing the evidence-based microlearning strategies; 3) integrating the informatics and AI tools to understand the learning needs of audience at academic, industry, and healthcare settings, considering social determinants of learning; 4) expanding knowledge on principles of multimedia for designing microlearning content; 5) hands-on experience developing evidence-based lesson plans for preparing microlearning content; 6) applying AI tools to prepare microlearning content in the form of infographics, videos, and quizzes and disseminating to peers; 7) engaging students in the self-reflection during the course and along with multiple assessment checkpoints for personalized feedback for continuous improvement; and 8) analyzing legal challenges and considerations of using AI tools for preparing microlearning content.
Evaluation/outcome: To measure the impact and effectiveness, we will collect feedback from students and educators, assessing areas for improvement and relevance. We will collect data both pre- and post-implementation of the revised informatics course in the MSN nurse educator program. We will work closely with course designers and the curriculum development committee to continuously update the course materials. The results from the assessments will be consolidated and disseminated in the form of peer-reviewed manuscripts and conference presentations to share insights into developing competency-based informatics courses for nurse educators, leveraging AI tools to prepare microlearning content. The dissemination of learning outcomes will catalyze the momentum of forwarding competency-based education, enabling nurse educators to develop the competencies necessary to utilize the latest advancements in informatics and AI tools and prepare evidence-based microlearning content.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P25 - Cognitive Offloading in EHR Use

Amber Massey, MSN, RN, NI-BC

Updated: 03/05/26

Background: Technological advancement has profoundly transformed nursing practice, particularly through electronic health records (EHRs), barcode medication administration (BCMA) systems, and decision-support tools. These systems allow nurses to externalize memory and decision-making processes—an act known as cognitive offloading. Originating from cognitive psychology, cognitive offloading refers to the intentional transfer of mental tasks to external tools to reduce cognitive burden. While this practice enhances efficiency and safety, it may also foster overreliance on technology, posing risks when systems are unavailable.
Purpose: This concept analysis aimed to clarify the meaning of cognitive offloading within the context of nursing, where high cognitive load and technology integration make the phenomenon both pervasive and consequential. Using Walker and Avant’s method, the analysis sought to identify defining attributes, antecedents, consequences, and empirical referents, and to situate cognitive offloading within nursing informatics theory and practice.
Methods: Walker and Avant’s eight-step method guided the analysis, integrating evidence from nursing, cognitive psychology, education, and human–computer interaction literature. Sources were examined for recurring patterns related to the defining features, preconditions, and outcomes of cognitive offloading in clinical settings.
Results: Five defining attributes emerged: 1) intentional transfer of cognitive tasks to external resources, 2) use of physical or digital tools to support cognition, 3) reduction in immediate cognitive load, 4) a trade-off between short-term performance and long-term retention, and 5) susceptibility to overreliance on technology. Antecedents include the availability of external aids, high task complexity, metacognitive awareness of limitations, and supportive organizational cultures. Consequences span both benefits—such as improved efficiency, accuracy, and reduced cognitive strain—and drawbacks, including skill degradation, diminished memory retention, and vulnerability during EHR downtime. Empirical referents encompass measurable indicators like audit log data, system-use patterns, simulation performance, and self-reported reliance on decision-support tools.
Theoretical integration: Cognitive offloading aligns with three theoretical perspectives: 1) cognitive load theory, which explains offloading as an adaptive response to excessive mental workload; 2) the theory of technology dominance, which warns of cognitive dependency when decision aids assume primary responsibility; and 3) metacognition theory, which elucidates how awareness of cognitive limits guides offloading decisions. Together, these frameworks position cognitive offloading as an intentional, context-driven strategy rather than a passive byproduct of technology use.
Implications: For nursing informatics, understanding cognitive offloading is essential to designing systems that optimize performance without eroding clinical judgment. Informatics leaders should promote adaptive offloading—the use of technology that augments, rather than replaces, cognitive work—through thoughtful system design, training, and simulation of downtime scenarios. Empirical measurement of offloading behaviors will also support future research and inform the balance between technological support and cognitive resilience.
Conclusion: Cognitive offloading is a critical and evolving concept in nursing practice, reflecting the interplay between technology, cognition, and patient safety. By clarifying its defining features and theoretical foundations, this analysis lays the groundwork for developing measurement tools and interventions that harness the benefits of cognitive offloading while mitigating its risks in increasingly digital healthcare environments.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P26 - Embracing Self-Service Analytics and AI in Nursing Informatics

Cheryl Cordova, MSN, RN, OCN

Updated: 03/05/26

Purpose: The nursing informatics landscape is transforming as we move from data discomfort to forging digital friendships using AI models as conduits. The emergence of big data and advanced analytic techniques in evidence-based practice introduces new challenges for nursing professionals, encouraging them to broaden their data sources and analytical approaches. As electronic health records (EHRs) increasingly incorporate self-service analytics tools, nursing professionals are empowered to engage directly with data. This shift allows nursing informaticists to explore EHR data transparently, moving away from the previous black-box model. Furthermore, with AI-enhanced SQL capabilities in applications like Databricks, these professionals can independently access and analyze data, significantly streamlining the process of transforming data into actionable insights. This emphasis on fostering data literacy empowers nursing informaticists to deepen their understanding of analytics, ultimately leading to enhanced operational efficiency across the organization.
Description: The challenges of standardization and scalability in data reporting and analytics across the enterprise are critical issues we are addressing. Our efforts have improved the process of obtaining standardized data for SQL writing, leveraging our strengths as senior program managers and informaticists who excel in understanding workflows and the context of data. However, as institutions increasingly prioritize cleaner data sources and operational efficiencies, our analytics team is evolving to source data directly from the EHR whenever possible and to execute SQL queries as needed. This shift necessitates significant upskilling, but the integration of AI models such as Claude, Co-Pilot, and Genie is proving invaluable. These tools enhance our capabilities as clinical informaticists, despite the steep learning curve involved.
While self-service analytics models enable us to quickly address straightforward questions, more complex inquiries still require robust SQL writing. When we need to pull data from multiple sources, challenges arise due to unclear data definitions and schemas. Here, AI can assist in navigating these complexities, but establishing clear data standards is essential for ensuring accuracy and reliability. By leveraging our clinical knowledge alongside these advanced tools, we are well-equipped to adapt to these changes and improve our reporting accuracy, ultimately enhancing the quality of our work.
Evaluation and outcomes: In light of recent organizational changes, we have experienced a temporary reduction in resources, particularly among our data architects, resulting in an 18.1% decrease in tasks closed over the past five months. However, this transition has led to an increase in productivity, with tasks completed per full-time equivalent (FTE) rising from approximately 14.29 to 20.5. During this time, we have also been migrating from on-premises systems to the cloud, adding complexity to our workload. Looking ahead to the new year, we are excited to develop a comprehensive training plan that will empower our team to manage incoming work effectively while building on our successes. Together, we are turning challenges into opportunities for growth and innovation.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P27 - Navigating the Move from Bedside to Informatics: Storytelling through Change

Maria Belinda Herrera, MSN, RN, NI-BC

Updated: 03/05/26

Purpose: As more nurses transition into informatics roles, understanding the human side of change is essential for retention and role satisfaction. This poster illustrates how storytelling, guided by Bridges’ transition model, can help novice nurse informaticists navigate the emotional and professional shifts that accompany leaving bedside practice. By framing transition as both a personal and developmental process, it offers reflective habits that promote self-awareness, a sense of belonging, and confidence in a new professional identity.
Description: Ending, Losing, Letting Go: For many nurses, moving into informatics begins with a quiet sense of loss. The nurse in this story recognized that stepping away from bedside care meant saying goodbye to patients and colleagues who once anchored her sense of purpose. Naming those losses allowed her to carry forward the strengths that defined her nursing identity: a sense of curiosity, advocacy, and dedication to making care safer and more efficient.
The Neutral Zone: Informatics introduced a new kind of uncertainty. She was no longer the expert she once was at the bedside, and self-doubt surfaced in unfamiliar workflows and acronyms. To stay grounded, she started a daily debrief: a quick, end-of-day reflection on what drained or energized her, what surprised her, and what she learned. Each entry was tagged to a Bridges phase, supported by a simple phase check + choice: In ending, acknowledge one thing missed and one strength to carry forward. In neutral, identify one area to understand better and plan to ask or observe it next time. In new beginning, name a win or moment of connection that felt affirming. These micro-reflections transformed uncertainty into movement, giving structure to an otherwise ambiguous middle stage.
New Beginning: Over time, the nurse realized that the joy of informatics mirrored the underlying satisfaction found at the bedside: helping people. The setting had changed, but the purpose stayed the same. Community connections became the bridge from doubt to confidence: through regular one-on-one mentor sessions, participation in informatics-focused social media forums, joining an ANIA chapter, serving on the social media committee, and attending conferences. These touchpoints provided language, validation, and a sense of belonging during professional reinvention.
Evaluation/outcome: Over several months, consistent daily debriefs tracked the nurse’s progress through Bridges’ phases, revealing a growing sense of clarity and confidence. Professional engagement deepened through participation in online communities and sustained involvement in ANIA, culminating in new leadership roles as co-chair of the social media committee and secretary on the South Texas chapter board. Together, these experiences demonstrated that self-reflection and community connection can transform uncertainty into resilience, helping early career informaticists find a sense of belonging and purpose during their transition.
Learning objectives: Identify the emotional and professional stages of transition described by Bridges’ transition model as they relate to moving from bedside to informatics practice. Apply storytelling as a reflective strategy to navigate change and foster resilience during professional identity shifts. Recognize the role of professional communities, such as ANIA, in supporting successful transitions and ongoing growth in nursing informatics.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P28 - Optimizing Telemetry Alarm Management Workflow to Reduce Alarm Fatigue

Ashley Hunsucker, MSN, RN

Updated: 03/05/26

Background: Alarm fatigue remains a critical safety issue in health care, contributing to delayed responses and a delay in patient care. The 2025 National Patient Safety Goals (NPSG) goal 6 emphasizes the need to understand how to reduce alerts to caregivers by addressing alarm overload and ensuring caregivers receive actionable alarms to provide timely safe patient care. Partnership and pilot to develop a standardized workflow for a centralized telemonitor unit for patient monitoring for patients on a cardiac ICU unit.
Objective: To design and implement an alarm management workflow with the current telemonitoring system to reduce alarm volume to the caregiver, improve staff response rate, send meaningful alerts to caregivers, and improve staff satisfaction while continuing to provide safe patient care.
Methods: An organic approach was used in a high=acuity ICU unit over several months encompassing alarm data analysis to identify top dispatched alarms and refine any thresholds; deployment of suspend features to specific alarms to allow for self-correction prior to dispatching an alarm; evaluation of response times before and during deployment of pilot; survey and evaluation of staff prior to pilot and during daily debriefing for real-time qualitative data; deployment of unit-specific pilot with re-worked workflow with priority-based alerting; and measured impact by alarm volume, suspension, responses and staff satisfaction.
Conclusion: Optimization to the telemonitoring clinical workflow for the telemetry alarms align with the NPSG goal 6 to reduce patient harm associated with clinical alarm systems and improve the safety of clinical alarm systems and proved to be a positive impact per this study. A 40 – 60 % alarm reduction was identified after pilot program go-live. Organization go-live for all 12 facilities went live in January 2025. One year data will be presented at conference. During the pilot a patient's progression to ventricular tachycardia was prevented through workflow changes. This shows an immediate impact to patient safety in care.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P29 - STAMP Out Violence

Amanda Lee, MSN, RN, NI-BC

The workplace violence initiative began by optimizing the existing emergency department (ED) FYI flag workflow. The interim plan expanded this workflow to an organization-wide approach. During this phase, we introduced a standardized, auto-populated SmartText within the FYI flag documentation and implemented a storyboard banner trigger to enhance visibility and awareness across care teams.
The next phase centered on implementing the STAMP assessment tool within the ED, addressing a critical gap in the early identification and management of patients at risk for violence. This session will explore the innovative integration of STAMP and behavioral safety alert FYI flags into clinical workflows, highlighting how this approach enhances safety, communication, and decision-making across interdisciplinary teams.
Context and problem statement: Violence risk in patients is often under-recognized until escalation occurs, posing significant safety risks to both patients and staff. Prior to implementing STAMP, our organization lacked a standardized, proactive method for identifying early signs of violence risk. This resulted in inconsistent documentation, delayed interventions, and a reliance on reactive measures such as restraints or security involvement.
Innovation and novelty: STAMP empowers front-line staff to identify potential violence risk early through observable behavioral cues. What sets this implementation apart is its seamless integration into Epic, alignment with FYI flagging, and incorporation into real-time clinical workflows. The tool fosters a shared language across disciplines and supports timely escalation protocols, improving both patient outcomes and staff confidence. The use of standardized SmartText and storyboard banners within the FYI flag workflow ensures continuity across encounters, promoting organization-wide awareness and utilization.
Reporting and outcomes: Using EHR reporting, a positive STAMP rate ranging from 1.7% to 9.2% per month depending on facility. Of the number of patients with preexisting FYI flags, 40% are STAMP positive. STAMP allows the nurse to prioritize interventions for the highest risk patients. Our organization saw a decrease of WPV events from our MIDAS event system from a rate of 4.18 (2024) to 3.93 (2025), a 6% reduction, surpassing the 3% organizational goal.
Supporting evidence: Research supports the use of structured violence screening tools to reduce adverse events and improve behavioral health management. Our internal data demonstrates increased awareness of patients with potential for violence and a reduction in risk-related incidents.
Learner impact: Attendees will leave this session with 1) a clear understanding of how STAMP functions and its significance in clinical settings; 2) practical strategies for integrating violence screening into existing workflows; 3) insights into change management, staff training, and EHR optimization; and 4) tools and guidance to replicate or adapt the STAMP model within their own organizations.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.

P30 - Leveraging Technology at the Bedside to Support Specialty Surface Selection

Jena Brandt, BSN, RN, CCRN | Clarinda Brewer, MBA, MHIIM, RN, NI-BC, Associate Vice President Clinical Informatics, Lakeland Regional Health | Kelly A. McKee, MSN, RN, CI-BC | Lauren Morata, DNP, APRN-CNS, CCRN, CCNS, CPHQ | Fanny Tshimbalanga, BS | Riley Hayes, BSc CS

Updated: 03/05/26

Purpose: Providing patients with the right surface (e.g., therapeutic bed, mattress overlay) to support wound management may be challenging for customers who are not wound care experts. Over the last five years, an 864-bed tertiary referral center has grown to a 910-bed teaching, tertiary health system. The specialty surface selection and acquisition process evolved from a fragmented, manual workflow to an integrated, informatics-driven model leveraging decision support technology. Throughout this evolution, data analysis and workflow mapping highlighted process failures that led to lost rental surfaces and failed acquisitions. Through close collaboration with end users, the clinical quality team partnered with the informatics team, applying systems thinking to redesign processes and increase surface utilization, ensuring patients’ needs are met more efficiently and safely.
Description: A surface selection decision support tool was designed and embedded within the electronic health record (EHR), triggering completion based on assessment documentation that indicated surface need. Continuous evaluation occurred through reports, rounding, and user feedback. Using the plan–do–study–act framework, the process evolved from both informatics and quality perspectives, producing actionable data that supported the creation of a centralized bed controller role. Cross-technology notifications were developed to enable interoperability between systems, including EHR order entry, logistics notifications, and secure messaging, reflecting the importance of communication and human-technology interaction in care coordination.
Evaluation: The surface selection decision support tool began integrating with the logistics requests beginning in June 2024. When comparing July 2024 to July 2025, there was a 339% increase in surface orders. This increase has sustained given the reliability of the process change. Prior to implementation, an average of 132 beds were ordered each quarter compared to 660 each quarter post-implementation.
The bed controller role now receives secure text notifications as part of ongoing optimization, reducing misplaced surfaces. Data transparency enabled leadership to allocate resources effectively and expand surface inventory. The wound care team has expanded within the health system; therefore, surfaces will no longer be requested using the surface selection decision support tool. To support decision making at the bedside, these clinical experts have opted to maintain the indications within the order.
Outcome: This project demonstrates how nursing informatics principles—workflow analysis, decision support, data-driven evaluation, and interdisciplinary collaboration—can enhance quality outcomes and system reliability. In partnership with the quality team, informatics helped translate frontline challenges into actionable process improvements. Leveraging technology to align clinical, operational, and human factors optimized patient care and highlighted how quality and informatics together can drive sustainable, system-level change.

Learning Objective:

After completing this learning activity, the participant will be able to assess innovations being used by other professionals in the specialty and the potential of implementing the improvements into practice.