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P31 - The LIFT Strategy: Transforming Informatics into a Data-Driven Healthcare World
Shellon Blackman-Lees, PhD, MS    |     Michael Cassara, DO, MSEd    |     Keriann Latten, DNP, FNP-C, AE-C, MS, RN    |     Anncy Thomas, DO, DABFM, FAMIA    |     Kimberly Velez, MSN, RN, Expert Clinical Informatics Trainer and Educator, Northwell Health    |     Lisa Geller, MD

Updated: 03/05/26

Updated: 03/05/26
Purpose: Health care is rapidly evolving, especially in the field of informatics. Our healthcare leaders need to not only understand informatics but be able to apply these skills to tackle the challenges they face every day.
Description: LIFT (Leveraging Informatics for Transformation) is an eight-month program created to provide clinician leaders with the informatics knowledge and skills needed to drive transformation in their field. The program integrates American Medical Informatics Association (AMIA) podcasts, webinars, and text in combination with in-person lectures, case studies, and collaborative work amongst the cohort.
After participants complete the program, clinicians present their work at a practicum showcase, where they translate the knowledge and skills they have learned within the program and apply it to propose a technical solution to the challenges within their specific department. Participants collaborate closely with their mentors and LIFT faculty to understand the background of their problem, develop workflow processes as well as outcomes that have the potential to translate into our healthcare system. The practicum showcase demonstrates critical thinking, application of informatics, and dissemination of scholarly work from the cohort.
Evaluation/outcome: The projects presented at the showcase highlighted various problems and how informatics principles can solve these challenges. Two outcomes included AI-assisted remote patient monitoring to increase the diagnosis of obstructive sleep apnea in children and an effective change management for a pediatric hematology oncology order set implementation.
Projects were evaluated using rubrics to assess the student’s ability to demonstrate the application of their LIFT informatics knowledge, as well as the demonstration of clinical informatics competencies.
The LIFT program was individually assessed by each participant using pre- and post-surveys. After two implementations, the program yielded statistically significant improvement in self-reported clinical informatics competencies. Qualitative post-survey data from participants highlighted themes of “shared challenges,” “team understanding,” “preparedness for future challenges,” and “professional development.”
Future cohorts will expand to other interdisciplinary members, including a dedicated track to nursing informatics. With the ability to scale the LIFT program, we are increasing the capacity to transform health care across multiple levels.

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.

P32 - Enterprise-Wide Optimization of Nursing Delirium Assessment through Informatics-Driven Workflow Redesign
Maria Abalco, MS, RRT    |     Michael Kaufman, MBA, RN, NI-BC, CPHIMS    |     Pamela White, BA

Updated: 03/05/26

Updated: 03/05/26
Delirium is a prevalent and serious condition among hospitalized patients, particularly those aged 65 and older. It is associated with increased morbidity, prolonged hospital stays, and higher rates of discharge to post-acute care facilities. Accurate and timely delirium assessment is a critical component of the recently enacted Center for Medicare and Medicaid Services Age-Friendly Health Systems initiative and the Society of Critical Care Medicine ICU liberation bundle. In addition, the geriatric ED level-one gold accreditation identified a need for improved delirium screening workflows and documentation. Historically, inconsistent workflows and fragmented documentation processes have hindered effective delirium screening and management across care settings.
This project describes the enterprise-wide redesign of information technology (IT)-supported nursing workflows to enhance the accuracy, consistency, and visibility of delirium assessments across emergency department (ED), inpatient (non-ICU, ICR, and ICU), and pediatric intensive care unit (PICU) settings within a multi-hospital health system in Suffolk County, New York. This initiative replaced outdated, underutilized and inconsistent assessment tools with age friendly and specialty-based validated instruments. These tools include the 4AT Assessment test for delirium & cognitive impairment for ED patients, nursing delirium screening scale (NuDESC) for adult non-critical care units, intensive care delirium screening checklist (ICDSC) for ICUs, and Cornell assessment of pediatric delirium (CAPD) for pediatric populations.
Key informatics interventions included the development of dynamic, rules-based clinical decision support (CDS) logic to automate the generation of delirium assessment orders and corresponding nursing tasks. These were tailored to patient age, unit type, and acuity level, ensuring appropriate timing and frequency of assessments. This logic prevents manual entry of duplicate orders and tasks and also cancels old orders and generates new orders when patients transition between levels of care to ensure seamless automated updates during intrahospital transfers. Additionally, the IT team collaborated closely with nursing subject matter experts to streamline and enhance documentation by optimizing placement of assessments within the electronic medical record to improve visibility.
Pre-/post-implementation workflow diagrams illustrate the transformation from a fragmented, partially manual process to a fully integrated, IT-driven system. To enable longitudinal tracking and intervention planning, a new order, positive delirium screen quality measures, is automatically triggered by a positive delirium score. Icons display on patient tracking boards for patients who score positive. Nursing transfer notes were updated to provide real-time visibility into delirium status enterprise-wide to support continuity of care and interdisciplinary communication. Iterative validations proved the need to revise and share delirium interventions fields to standardize documentation across the organization.
This project demonstrates the power of nursing informatics in driving clinical transformation. By aligning IT capabilities with front-line nursing workflows, the initiative provides the framework to improve compliance with regulatory standards, streamline and optimize workflows, enhance the quality and safety of patient care, and relieve the burdens of manual order entry for providers and overtasking for nursing. The scalable design supports future expansion to additional facilities within the enterprise.

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.

P33 - DNP Project Alignment with the Iowa Model of Evidence-Based Practice: Implementing Veteran Status Screening in the Emergency Department to Reduce Suicide Risk
Christina Weaver, MSN, RN

Updated: 03/05/26

Updated: 03/05/26
Introduction to problem: Veteran suicide remains a critical public health issue, with veterans representing only 7.6% of the US population but 14% of all suicides. Despite available veterans’ affairs (VA) crisis and mental health resources, nearly 40% of veterans receive care exclusively in community settings, where veteran status is often undocumented. Lack of systematic screening and education in civilian emergency departments (EDs) leads to missed opportunities for early connection to crisis resources and suicide prevention programs. This project seeks to address the gap in veteran identification and crisis-resource awareness using a standardized screening tool and educational intervention guided by the Iowa model of evidence-based practice.
Framework and theory: The Iowa model of evidence-based practice will guide this DNP project. The model provides a structured approach for integrating evidence into practice through problem identification, pilot testing, evaluation, and sustainment. Additionally, Orem’s self-care deficit nursing theory (SCDNT) supports the project’s educational component by emphasizing the nurse’s role in helping individuals meet self-care demands.
Integration and implications: Applying the Iowa model integrates evidence into a structured workflow for identifying veterans during triage and delivering brief, standardized crisis education. Findings align with DNP essentials II, IV, VII, and X by addressing health systems leadership, information management, and population health.
Implications: For nursing practice: Embeds veteran identification as a standard of care.
For leadership: Builds cross-system collaboration and measurable process improvement.
For population health: Advances suicide prevention efforts for an underserved group. This nurse-led initiative exemplifies how evidence, theory, and community partnership can inform real-world solutions.
Literature review: A synthesis of 18 studies (2017–2025) supports systematic veteran screening as an evidence-based strategy to improve identification and access to crisis services. Themes identified include under-identification in civilian settings: Many veterans remain unrecognized in EHRs. Effective interventions: Adding a veteran-status question increased identification tenfold. Crisis-resource education: Structured teaching about 988 “press 1” improves utilization of crisis lines. Gaps: Most studies are VA-based; few evaluate civilian ED implementation or combined screening/education models. Addressing this gap through nurse-led interventions in community EDs could improve equity, continuity, and suicide prevention outcomes.

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.

P34 - Workflow Revolution: Unburden Nurses by Automating Routine Fall Risk Assessments: Redefining Fall Prevention as a Continuous, Data-Informed Process—Rather than a Static Checklist to Support Timely, Personalized Care
Stephanie Zebehazy, MSN, RN, NI-BC

Updated: 03/05/26

Updated: 03/05/26
Learning outcome: Participants will be able to evaluate the integration of a fall predictive model (FPM) into current workflow and understand how automation reduces documentation burden, improves identification of high fall risk patients, and helps assign fall risk interventions.
Background: Significant resources are dedicated to preventing falls. Falls during a hospital stay can result in severe injuries, extended hospital stays, and increased healthcare costs. Despite current assessment tools, identifying high-risk patients remains challenging. Fall assessment using manual tools is subjective and time consuming, requiring 2-5 minutes per patient. This contributes to a significant documentation burden with recurrent and routine assessments.
Problem: Manual assessments flag 88% of patients as high fall risk, leading to overclassification that strains nursing resources diverting attention from those truly at risk. Additionally, fall risk changes during a patient’s stay may go unnoticed until the next assessment, delaying timely interventions.
Patient data must be continuously reviewed throughout the patient stay to detect any changes that may increase fall risk. Manual fall assessments are required upon admission and a minimum once per shift adding up to a significant amount of time spent documenting on patients who may not be at a significant risk for falls.
Solution: The FPM automates fall risk assessment by continuously analyzing patient data from the electronic health record. Integrated into nursing workflows, the FPM provides objective, real-time risk evaluation and alerts RNs when a patient’s fall risk increases, prompting early reassessment of interventions. The model was piloted in two departments and is being scaled across nine hospitals.
Goals: Reduce average documentation time per shift by 2-5 minutes through automation of routine fall risk assessment. Validate accuracy in identifying high-risk patients, as evidenced by alignment between predicted risk and actual fall events.
Outcomes: Implementation of the FPM reduced patients flagged as high risk from 88% to 49%. This refinement enabled RNs to prioritize preventive interventions for those at greatest risk. Over the three-month pilot, the fall rate declined by 20%, falls resulting in injury decreased by 17%, and assisted falls increased by 44%. These improvements translated to an estimated reduction of six falls and two falls with injury during the pilot timeframe. Data for reduced time in the EHR is pending but is trending downward.

Conclusion: Automating the fall risk assessment leverages EHR data aiding in improving fall risk identification. Documentation burden with routine, required assessments is reduced. Real-time alerts triggered by rising fall risk support early intervention, helping nurses tailor fall risk strategies to changing patient conditions.

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.

P35 - Expanding Patient-Family Communication through Text Translation
René Singer, MBA, BSN, RN, CPN

Updated: 03/05/26

Updated: 03/05/26
Introduction: Effective communication is vital to patient- and family-centered care, particularly during surgical procedures. Families of patients with limited English proficiency often face barriers that increase anxiety and reduce equitable access to timely information.
Identification of the problem: At Children’s Hospital Colorado, perioperative text message updates generated by Epic were only available in English. Spanish-speaking families, therefore, did not receive the same quality or frequency of intraoperative updates, creating communication inequities during high-stress surgical events.
QI question/purpose of the study: This project sought to improve intraoperative communication and health equity by implementing automated and custom text messages translated into Spanish. The aim was for 90% of Spanish-speaking families to receive automated text updates in their preferred language by May 2024 and expand custom messaging capabilities by August 2024.
Methods: Phase I (November 2023) utilized Epic’s embedded functionality to deliver automated text updates in Spanish based on perioperative event times.
Phase II (August 2024) focused on developing translated “dot phrases” and click-box options for commonly used custom messages, standardizing communication across surgical teams. Education for perioperative nurses was provided via email, signage, and staff meetings.
Outcomes/Results: Automated text messaging for Spanish-speaking families increased from 0% to 97% system-wide within months of implementation and has sustained rates above 95%. Custom message utilization averages 66% across surgical areas, with variation from 56% to 98%. Families reported improved understanding, reduced anxiety, and greater trust in care teams.
Discussion: Delivering real-time updates in families’ native language increased access to vital information, enhanced inclusivity, and promoted a more compassionate care environment. Variability in custom message use highlights the need for continued staff education and reinforcement.
Conclusion: Bilingual text messaging through Epic successfully improved equity in intraoperative communication for Spanish-speaking families.
Implications for perianesthesia nurses and future research: Perianesthesia nurses play a key role in supporting communication equity through consistent use of multilingual tools. Expanding this initiative to include additional languages and refining staff education may further reduce disparities and strengthen family-centered care in the perioperative setting.

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.

P36 - Charge Ahead: Leveraging the Facility Charge Calculator for Higher Clinic Revenue
Irene David, MS, BSN, RN    |     Erika Lopez, BSN, RN

Updated: 03/05/26

Updated: 03/05/26
Hospital-based clinics (HBCs) follow high regulatory standards, (i.e., Joint Commission), and are specialized in treating more critically ill and advanced illness patients. As the demand for care grows, the current clinic facility fees are insufficient to cover the rising operational costs, potentially impacting the ability to maintain the level of care these patients require. Therefore, the revenue integrity team partnered with Claro Healthcare to leverage our electronic medical record (EMR) technology to improve the accuracy of charge capturing as completed through clinical documentation. The goal was to increase facility level charges in HBCs by 10% baseline of 3104 occurrences in August to September 2024 to 3414 occurrences post-implementation in January to February 2025.
A SWOT analysis revealed the discrepancies and lack of utilization of the EMR’s documentation tool for facility fee assignment also known as the facility charge calculator (FCC). The historical FCC criteria did not consistently reflect facility resource utilization and patient acuity. Also, there was a variance in workflows among HBCs on the usage of the FCC. Therefore, the project team was developed to review and optimize the facility charge workflow. This team consists of stakeholders from the Claro Healthcare team, revenue integrity team, HBC leaders, EMR team, and clinical workflow informaticist team. Work groups were developed to focus on aspects of the FCC enhancements such as reviewing the point allocation for the nursing led activities and evaluating nursing documentation. The EMR team incorporated the work groups’ input and information as they built the upgraded FCC tool into the EMR. In September 2024, the FCC build and testing was completed. The tip sheet was created and distributed by the EMR trainers, and the FCC training and demos were provided for all clinic staff who utilized the FCC. To assist the revenue integrity team with the auditing of clinical documentation and its associated facility charges, the revenue integrity team was granted access to the FCC.
Post-go-live of FCC enhancements and training, there was a 315.496% increase in utilization of facility level charges for new and established, non-procedure visits post intervention. There was a significant increase in charges from $4.5 million in December 2023 to January 2024 (pre-go live) to $11 million in December 2024 to January 2025 (post-go-live). Therefore, the aim of the project was met. To ensure the success of this initiative, additional tools have been developed (i.e., FCC Point Guide) to support follow-up education on FCC changes. Ongoing monitoring and reporting are underway during the present control phase. Leveraging technology and improving clinical documentation has aligned facility fees with rising care costs for critically ill patients, ensuring HBCs can continue providing high-quality treatment and preserve a qualified nursing workforce capable of delivering high-acuity care in ambulatory care clinics.

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.

P37 - Enhancing Patient Care and Workflow Efficiency through Virtual Nursing: A Multi-Faceted Implementation at Cooper University Health Care
Liezel Chmielewski, DNP, RN, NI-BC, BS

Updated: 03/05/26

Updated: 03/05/26
This presentation outlines the successful implementation and evaluation of a comprehensive virtual nursing (VRN) program at Cooper University Health Care, aimed at improving workflow efficiency, patient safety, and staff satisfaction. The initiative focused on reimagining clinical workflows, specifically virtual admissions, rounding, and discharges. This is supported by the deployment of AI models for fall risk and pressure injury prevention. A user-centered interface was also developed to project real-time Epic data onto whiteboards in patient rooms, improving visibility and communication. The project included extensive collaboration with vendors and nursing education to develop training demonstrations and tip sheets. Go-live support spanned four weeks, with continued on-call and weekend coverage to ensure successful adoption.
To evaluate impact, Microsoft Forms surveys were administered pre- and post-implementation to bedside nurses and virtual nurses. Quantitative data over 4 months demonstrated strong outcomes: Time saved at bedside: 5 days, 12 hours on unit 1; 4 days, 16 hours on unit 2. Reduced average unit length of stay: From 162.1 to 124.8 hours on unit 1; from 144.4 to 95.1 hours on unit 2. Improved call bell response times: Decreased by 1 minute on unit 1 and 30 seconds on unit 2.
Benchmarking via VRN platform positioned Cooper above national averages, with 3.94 virtual nursing hours per room, highlighting strong utilization.
Qualitative feedback from both VRNs and bedside nurses was highly favorable, with themes of ease of use, improved patient engagement, and enhanced team collaboration emerging from the data.
This project demonstrates the clinical and operational value of integrating virtual nursing with AI and health IT tools. A collaborative effort with nursing, nursing education, IT, Informatics, and the virtual nursing platform showed a proven strategy for implementation, change management, and outcome measurement.

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.

P38 - Applying Machine Learning to Predict Correlation between Tracheotomy Timing and Maximum Ventilator Weaning Rate in Respiratory Care Center Patients
Yi Tsao Chen

Updated: 03/05/26

Updated: 03/05/26
This study aimed to identify the most significant factors influencing ventilator weaning using statistical methods and to determine the optimal tracheotomy timing via ML models, thereby providing evidence for precision medicine.
Source: The admitted cases in RCC from 2018 to 2023. Number of records: 819 records, about 200 usable records.
Purpose: Which variable has the greatest impact on ventilator weaning? Use the machine learning (ML) model to identify the optimal tracheotomy timing.
Research design: This study is a retrospective observational cohort study, with the use of the secondary data for analysis and the ML algorithm for establishing a prediction model for the optimal tracheotomy timing. Data are from relevant reports built in the VPN of respiratory therapists in the hospital, basic patient information, etc.
Data are divided into 2 sets. The training set is used for the ML model training, and the testing set is used to evaluate the quality of the model. The QOCA AIM Quanta AI medical cloud is used for analysis. It is estimated that 80% of the data will be used for training and 20% for testing (or based on the system's recommended allocation ratio). The best prediction model will be selected by interpreting the Confusion matrix in combination with the ROC curve and the evaluation of the AUC to establish a prediction of the optimal tracheotomy timing.
Research site and subjects: Research site: Subacute respiratory care unit of NTUH Hsin-Chu Branch. Research subjects: 1) Main inclusion criteria: Patients admitted to the subacute respiratory care unit of NTUH Hsin-Chu Branch with endotracheal intubation. 2) Main exclusion criteria: None.
Discussion: The probability of weaning from mechanical ventilation in internal medicine departments was 1 - 0.385 lower than that in surgery departments, and patients in surgery departments had higher successful weaning rates than those in internal medicine departments. Patients who underwent a surgery had 2.684 - 1 times higher probability of successful weaning compared to non-surgical patients, and intubation patients due to a surgery exhibited higher weaning success rates than non-surgical intubation patients. Patients with tracheotomy had 1 - 0.468 lower probability of successful weaning than those without tracheotomy. Tracheotomy patients showed lower weaning rates (tracheotomy is recommended for difficult-to-wean patients). Each additional day of intubation reduced the weaning probability by 1 - 0.936. The more the intubation days, the more the difficulty in weaning. Each day delay between intubation and tracheotomy reduced weaning probability by 1 - 0.975. The earlier the tracheotomy for patients who need a tracheotomy, the higher the weaning success.

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.

P39 - The Future of Nursing Notes: Integrating AI into Care Planning
Maureen Harding, MSN, RN, Director, Nursing Informatics, Mount Sinai Health System    |     Marcela Torres, MSN, RN

Updated: 03/05/26

Updated: 03/05/26
Purpose: An artificial intelligence (AI) tool called end-of-shift-summary was enabled for nursing care plans in the electronic health record (EHR). The aim was to streamline end of shift summary notes, further individualize care plans, and reduce time spent in the EHR.
Background/significance: As primary end users of the EHR, nurses spend up to 35% of their shift on documentation alone. Increasing documentation requirements contribute to cognitive and documentation burden—factors that have been linked to clinician burnout.
To decrease this burden on nursing, AI can be leveraged to streamline workflows. The aim of the tool is to decrease time spent drafting care plan notes while enabling a more comprehensive note. This tool extracts key chart elements and generates a summary of significant events. Nurses are then able to review, add clinical insights, and verify the generated note, saving time on documentation and improving accuracy of care plans and end of shift notes.
Methods: End-of-shift summary was enabled in one critical care unit and two medical-surgical units from July 2025 to September 2025. A pre-survey was collected prior to go-live to capture qualitative data of the current care plan workflow. Post-go-live support was provided on an ongoing basis through rounding and on-demand educational content. Metrics are being collected and reviewed with stakeholders during bi-weekly meetings. A post-survey will be distributed five months post-implementation to collect feedback on the end of shift summary tool. Results to be included in the final presentation.
Results: Expected decrease in time spent generating care plan notes, positive feedback from nursing on the AI tool, and increased confidence in utilization of new AI tools. Complete metrics and post-survey results to be collected at the beginning of January 2026.
Conclusion: Preliminary findings have shown that the integration of generative AI into the EHR has positively enhanced nursing workflows through individualization of care plan and end of shift notes. It can minimize repetitive workflows, decreasing documentation and cognitive burden. This pilot has allowed front-line nurses to see the benefits of AI technology tools, understanding that the purpose of AI is to support and enhance workflows, not replace clinicians. Without nurses at the bedside, the power of this tool is diminished as data has no meaning without interpretation and validation.

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.

P40 - Perceptions of Documentation Burden and Barriers in an ICU Setting
Megan Brown, MSN, RN, NI-BC

Updated: 03/05/26

Updated: 03/05/26
Purpose: The purpose of this project was to evaluate nurses' perceptions of documentation burden and identify barriers related to the use of the electronic medical record (EMR). The goal was to develop interventions that optimize workflow, minimize redundancy, and improve satisfaction and patient care delivery.
Background/significance: With the implementation of the HITECH Act and Meaningful Use standards, EMRs were introduced to improve patient safety, help streamline documentation, and increase data accessibility. However, ICU nurses have reported increased documentation burden due to redundant entries, insufficient workflows, and uncertain expectations. Literature has suggested that documentation can consume up to 41% of a shift, leading to decreased time spent with the patient and increasing burnout. The evidence also shows that redundancy, lack of standardization, and excessive documentation needed for regulatory compliance add little clinical value.
Methods: A mixed-method program evaluation was conducted at a 241-bed Midwest level one trauma center. The validated burden of documentation for nurses and midwives (BurDoNsaM) survey was completed by ICU nurses (n=46), and semi-structured interviews were conducted with nurses (n=16) and key informants (n=7). Quantitative data were analyzed using t-tests, and qualitative data were thematically analyzed to identify burdens and workflow challenges.
Results: Most participants (82.6%) agreed there is “too much documentation and too little time,” and 58.7% reporting often staying late after shifts to complete documentation. While 71.7% agreed that documentation aids communication, only 39.1% believed it ensures quality care. About 90% perceived much of the documentation as unnecessary or was completed due to compliance/regulatory requirements. Additionally, 76.1% cited double documentation as an avoidable burden. Only 21.7% felt they had enough time during their shift to complete documentation, and just 23.9% felt leadership understood this burden. Nurses with more than six years of Epic or clinical experience reported documentation as more straightforward to complete, but still complex (p < .05).
Interviews revealed key themes: nurses reported spending 20–60% of their shift documenting, which they described as time taken away from patient care. Redundancies like repeated Braden scores, fall risk assessments, and care plans were seen as particularly frustrating. Navigating between flowsheets and the shift navigator was a significant barrier. One nurse stated, “I find myself flipping back and forth…and some things don’t always flow well.” There was also a clear call for standardized documentation expectations, better orientation, ongoing refresher training, and more explicit guidance on assessment frequency.
Conclusion/implications: The project identified significant barriers to meaningful and efficient documentation in the ICU setting. Nurses reported that excessive documentation requirements reduce time spent with patients and increase burnout. Recommended interventions include streamlining documentation workflows, removing redundant fields, optimizing Epic tools, and providing consistent training and super user support. Integrating feedback into the EHR design and policy decisions is also essential.

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.

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Evaluation