Iterative Improvement Process Research Articles

Closed-Loop Analysis of Nanocomposite Magnets: Integrating Micromagnetic Simulation and Experimental Testing

This study presents a framework for simulation-experiment closed-loop analysis of nanocomposite magnets. Firstly, hysteresis loops of nanocomposite permanent magnets were calculated using micromagnetic simulations. Then, the experimental preparation was carried out by rapid quenching of the melt and spark plasma sintering (SPS). We analyzed the effect of different sintering temperatures on the physical phase composition and magnetic properties of the samples. Characterization results show that the samples sintered at 600 °C have the highest magnetic energy product. Using transmission electron microscopy data, the polycrystalline model was refined, and further micromagnetic simulations were performed. The close agreement between simulated and experimental data confirms the improved accuracy of our simulation model. This iterative process of experimental characterization and simulation improvement reduces the discrepancy between simulation predictions and actual sample performance. It could facilitate rapid iteration in magnet development and provide valuable insights for optimizing the preparation of nanocomposite permanent magnets.

Modified One-Point-and-Area Algorithm for Sub-division of Irregular Parcels

Parcel sub-division is a crucial part of the Land Administration, and its techniques depend on the user requirements and other constraints. Splitting of a parcel based on a given area is one of such constraints whose subdivision has been popularly carried out using the trial-and-error method which is less accurate and time-consuming. For more accurate and direct subdivision, Habib developed algorithms based on popular eight cases. Among them, when the area to be split is known, a point or line is selected as a reference. In case if it has to be a point, then a one-point-and-area method is used for subdivision while a move-line-and-area method is used if line segment is used as reference. It was found that Habib Algorithm worked well for convex shapes, however it could not account for the concavity of irregular-shaped parcels. It was found that the approach for the calculation of the area considered only the cases of convex shapes. Therefore, the researchers observed the issues of clinging and retracting were identified. Thus, this study proposes an effective algorithm for the division of an irregular parcel using an improved version of One-Point-and-Area method used in Habib Algorithm. An additional stage of generating a convex hull was introduced to guide the iteration process, the formula to calculate area was adjusted to compute only the interior area and finally, the determination of the partition polygon was also done through an iterative improvement process rather than the existing one-step process. Experiments showed that the proposed algorithm accurately divided both irregular-shaped parcels and convex parcels. In the case of convex parcels, the result was found to be the same as Habib’s method. Due to the addition of an extra stage of generating a convex hull and extra iterations in the final stage, the proposed algorithm consumed more time. However, it was found negligible in regard to the volume of parcels involved in splitting at a time. Overall, the proposed algorithm accurately solved the issues of clinging and retracting irregular polygons and without changing the results in convex polygons. So, it was concluded to be a suitable replacement of Habib Algorithm, and its implementation will provide more accurate results in parcel sub-division problems.

Abstract C115: Framework for a community-based multiple myeloma screening program

Abstract Purpose: We develop a framework for a community-based screening program for multiple myeloma (MM). MM is known to be twice as common in Black/African American (B/AA) persons compared to white persons and B/AA patients have a higher risk of delay in initial treatment compared to white patients. Therefore, we developed a community-wide screening program for B/AA persons 50 years and older. As the first of these programs, we reveal an organizational framework that prioritizes engagement with key community partners who address barriers to cancer screening. Experimental Procedures: We expand organizational development knowledge about how to implement community-based MM screening and address barriers. The study uses an iterative and qualitative continuous improvement process. The study draws on literature review and multi-disciplinary team collaboration among clinicians, researchers, administrators, and the Cleveland Clinic Community Outreach Program (Cleveland Outreach). Data Summary: Our framework includes three interrelated partners and each address barriers to MM screening: 1) Cleveland Clinic Cancer Center. The multi-disciplinary team designs an educational brochure, secures pilot grant funding, plans for insurance/financial assistance as needed, develops care algorithms, and evaluates the impact of the program. Cleveland Outreach, a department within the Cancer Center, uses the Harold Freeman model as its foundational approach, which includes patient navigators and community outreach managers who are culturally/linguistically matched to medically underserved communities. The Cancer Center addresses barriers including program implementation despite a lack of national screening guidelines, insurance/financial barriers, and patient navigation to address fears and health literacy. 2) Faith-based organizations. Church leaders committed to cancer prevention participate in Cleveland Outreach’s faith-based outreach model. Faith-based partners address barriers of trust and fear. Also, by providing space at churches for on-site screening blood draws, they address transportation barriers. 3) Clinicians at Federally Qualified Health Centers (FQHC’s). Providers and nurses at FQHC’s receive a Continuing Medical Education program about MM screening designed/funded by the Cancer Center. From within the community, providers notify medically underserved patients about the screening. With their primary care patient population, FQHC partners address barriers such as trust, health literacy, and fear of blood draws. They facilitate on-site screening at FQHC’s and health fairs, which addresses transportation barriers. Conclusions: Given that there are no universal guidelines for MM screening, we develop a novel approach to early disease detection in B/AA patients. Our framework is a partnership between the Cancer Center, faith-based organizations, and FQHC’s. Collaboratively, we address barriers to screening including fear, trust, insurance/financial, health literacy, transportation, and patient navigation if abnormal results suggest the need for further testing. Citation Format: Heather McKee Hurwitz, Kimberly Bell, Diana Basali, Raymond D. Jackson II, Jason Valent. Framework for a community-based multiple myeloma screening program [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr C115.

Establishing and sustaining an acute care for elders unit: An incremental journey to success.

Acute Care for Elders (ACE) units reduce hospital-associated delirium, functional decline, and lengths of stay. However, establishing and sustaining such units have proven difficult. There are only 43 ACE units among the >3500 hospitals in the United States. This study describes an iterative quality improvement process, which allowed us to establish and sustain an ACE unit care model in a modern academic hospital. This continuous process was centered on implementing the key principles of the ACE unit model of care: patient-centered care assessments, medical care review, specialized prepared environment, early mobilization, physical therapy, and early planning for discharge to home. Quality of care and patient outcomes data for older adults admitted to our ACE unit includes mortality index (observed/expected) consistently <1 (FY22 = 0.86), 30-day readmission rate of <10% (FY22 9.31%), and length of stay index of ~1 (FY22 1.07). We describe how work on our ACE unit has led to hospital-wide initiatives, including dementia-friendly hospital certification. Our hope is that others can use this process to enhance the dissemination of the ACE unit model of care.

Parenting app to support socio-emotional and cognitive development in early childhood: iterative codesign learnings from nine low-income and middle-income countries

ObjectiveMany children in low-income and middle-income countries are disadvantaged in achieving early developmental potential in childhood as they lack the necessary support from their surroundings, including from parents and caregivers....

Analysis methodology and design verification for strengthening moment-resisting caisson foundations

Many existing monopole towers rely on caisson foundations to resist the effects of large overturning moments. Increased loading on these towers, coupled with material deterioration and more stringent code requirements, often results in the overloading of the existing caissons. The strengthening of these caissons for additional overturning resistance presents many challenges and typically requires the installation of new piles. A pile cap is commonly used to connect the existing caisson to the new piles. The proper design of the pile cap has a critical importance in ensuring an effective stress transfer and creating a resilient foundation system. While a number of strengthening design approaches are repeatedly used in industry, there is a dearth of guidance and proven analysis methods to verify the performance achieved with these approaches. This study proposes a well-defined analysis methodology for the system-level structural design verification of the strengthened foundation systems. The methodology provides two routes, using either the strut-and-tie or nonlinear finite element analysis methods, to quantify the load, deformation, cracking, and failure responses. The methodology helps expose and address the weak aspects of the design using an iterative design improvement process. A common strengthening design approach from industry, involving a moment-resisting caisson strengthened with four concrete piles and a pile cap, is used as a case study to demonstrate the application of the methodology. The results demonstrate that the methodology provides significant insight into the system-level behavior both at the serviceability and ultimate limit states. The analysis results are found useful in identifying the locations where modified bar detailing could significantly improve the system performance. The improved design from the methodology provided a 56 % increase in the load capacity for the case-study foundation while still using the same amounts of reinforcing steel and concrete as compared to the initial design before the application of the proposed methodology. This paper presents the studies undertaken, conclusions reached, and recommendations made for developing resilient pile cap designs for strengthening moment-resisting caisson foundations.

Using Project-Based Learning to Teach Advanced Practice Nurses About Quality Improvement

Using Project-Based Learning to Teach Advanced Practice Nurses About Quality Improvement

PP16 Machine Learning In The Treatment Of Spinal Deformities: Early Life-cycle Economic Analysis In Australia

IntroductionPatients with spinal deformity require implanted spinal rods to be specifically shaped to their anatomy. Spinal rods which are manually shaped in the operating theatre are prone to fracture and malpositioning. This emphasizes the need for preoperative planning, intraoperative imaging, and accurate shaping of the implant. The application of machine learning (ML) has enabled precision in shaping patient-specific rods, with the potential to reduce adverse events. Our objective is to assess the economic value of this technology, early in its lifecycle, in the context of Health Technology Assessment (HTA) in Australia.MethodsA budget impact analysis was performed to quantify the economic value of patient-specific spinal rods from an Australian payor perspective. Clinical outcomes were sourced from literature review, and cost inputs were obtained from Medicare, Private Health Data Bureau and Hospital Casemix Protocol Data databases.ResultsPreliminary analysis indicates that a reduction in the rate of revision surgery due to decreased instrument failure results in cost-savings to the healthcare system, despite a higher outlay for the patient-specific rods. Adolescents who may have remained sagittally malaligned after the implantation of manually bent rods are expected to derive the greatest benefit from this ML application. The key uncertainty in this analysis is the limited real-world data of this emerging technology. ML is an iterative process of continuous improvement, identifying correlations within the data collected. As additional surgical data are integrated into predictive models, we anticipate ML technology will enhance decision-making support in surgical strategy and enable better implant precision, resulting in further decreased operating time, reduced mechanical complications, and increased healthcare savings.ConclusionsML technology is enabling precision in patient-specific implants, which is expected to drive healthcare cost-savings due to a reduction in instrument failure. Fewer replacement surgeries are an important patient-relevant outcome, especially for adolescents with spinal deformity. This preliminary analysis demonstrates the economic value of ML enabled patient-specific rods to Australian payors, early in its lifecycle.

Improving detection of scanning attacks on heterogeneous networks with Federated Learning

Scanning attacks are the first step in the attempt to compromise the security of systems. Machine learning (ML) has been used for network intrusion detection systems (NIDS) to protect systems by learning misbehavior based on network traffic. This paper demonstrates that Federated Learning (FL) is a promising approach to achieve better detection performance than traditional local training and inference on distributed agents. Also, this FL approach brings privacy, efficiency, and it is suitable for distributed ML-based NIDS solutions. We present a horizontal FL setup using Logistic Regression with FedAvg strategy applied to 13 agents (data silos) capable of providing an iterative process of constant learning improvement. Our results indicate a more stable learning process when observed the F1-score average, whereas the traditional NIDS approach (local trained models) present lesser performance and bigger variability to classify scanning and benign traffic. We tested our model performance on the TON_IoT dataset containing network traffic from a virtualized heterogeneous network composed of cloud, fog, and edge layers.

Measuring and driving hospitalist value: Expanding beyond wRVUs.

Measuring and driving hospitalist value: Expanding beyond wRVUs.

Adding an Inception Network to Neural Network Open Information Extraction

This article presents a method to resolve tuples from plain text by adding an inception network, and dependence path embedding to existing neural network methods of open information extraction (Open IE). Inception networks are used in analysis of computer vision, and dependence path embedding in text processing, but neither has been reported with Open IE. Performance was measured on benchmark datasets using two existing Open IE deep learning methods, one using bidirectional long short-term memory and BIO tagging (RnnOIE-verb), and another using a span-based model (SpanOIE). RnnOIE-verb was compared with RnnOIE-verb plus inception network and/or dependence path embedding. SpanOIE was compared with SpanOIE plus inception network. Performance slightly increased with the addition of inception network to RnnOIE-verb (before AUC 0.45, F1 0.59; after AUC 0.46, F1 0.60) and inception network to SpanOIE (before AUC 0.63, F1 0.748; after AUC 0.64, F1 0.764). The performance gain was minor but potentially relevant to an iterative process of improvement.

Visualizing Opioid-Use Variation in a Pediatric Perioperative Dashboard.

Anesthesiologists integrate numerous variables to determine an opioid dose that manages patient nociception and pain while minimizing adverse effects. Clinical dashboards that enable physicians to compare themselves to their peers can reduce unnecessary variation in patient care and improve outcomes. However, due to the complexity of anesthetic dosing decisions, comparative visualizations of opioid-use patterns are complicated by case-mix differences between providers. This single-institution case study describes the development of a pediatric anesthesia dashboard and demonstrates how advanced computational techniques can facilitate nuanced normalization techniques, enabling meaningful comparisons of complex clinical data. We engaged perioperative-care stakeholders at a tertiary care pediatric hospital to determine patient and surgical variables relevant to anesthesia decision-making and to identify end-user requirements for an opioid-use visualization tool. Case data were extracted, aggregated, and standardized. We performed multivariable machine learning to identify and understand key variables. We integrated interview findings and computational algorithms into an interactive dashboard with normalized comparisons, followed by an iterative process of improvement and implementation. The dashboard design process identified two mechanisms-interactive data filtration and machine-learning-based normalization-that enable rigorous monitoring of opioid utilization with meaningful case-mix adjustment. When deployed with real data encompassing 24,332 surgical cases, our dashboard identified both high and low opioid-use outliers with associated clinical outcomes data. A tool that gives anesthesiologists timely data on their practice patterns while adjusting for case-mix differences empowers physicians to track changes and variation in opioid administration over time. Such a tool can successfully trigger conversation amongst stakeholders in support of continuous improvement efforts. Clinical analytics dashboards can enable physicians to better understand their practice and provide motivation to change behavior, ultimately addressing unnecessary variation in high impact medication use and minimizing adverse effects.

The COVID-19 Pandemic as an Opportunity and Challenge for Registries in Health Services Research: Lessons Learned from the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS)

The Coronavirus Disease-2019 (COVID-19) pandemic has brought opportunities and challenges, especially for health services research based on routine data. In this article we will demonstrate this by presenting lessons learned from establishing the currently largest registry in Germany providing a detailed clinical dataset on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infected patients: the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS). LEOSS is based on a collaborative and integrative research approach with anonymous recruitment and collection of routine data and the early provision of data in an open science context. The only requirement for inclusion was a SARS-CoV-2 infection confirmed by virological diagnosis. Crucial strategies to successfully realize the project included the dynamic reallocation of available staff and technical resources, an early and direct involvement of data protection experts and the ethics committee as well as the decision for an iterative and dynamic process of improvement and further development. Thanks to the commitment of numerous institutions, a transsectoral and transnational network of currently 133 actively recruiting sites with 7,227 documented cases could be established (status: 18.03.2021). Tools for data exploration on the project website, as well as the partially automated provision of datasets according to use cases with varying requirements, enabled us to utilize the data collected within a short period of time. Data use and access processes were carried out for 97 proposals assigned to 27 different research areas. So far, nine articles have been published in peer-reviewed international journals. As a collaborative effort of the whole network, LEOSS developed into a large collection of clinical data on COVID-19 in Germany. Even though in other international projects, much larger data sets could be analysed to investigate specific research questions through direct access to source systems, the uniformly maintained and technically verified documentation standard with many discipline-specific details resulted in a large valuable data set with unique characteristics. The lessons learned while establishing LEOSS during the current pandemic have already created important implications for the design of future registries and for pandemic preparedness and response.

The development of the Delivery Assessment Tool (DAT) to facilitate quality improvement in burns services in low-middle income countries

Implementation science is a useful tool to consider ways in which we can introduce improvements to burn services in low-and-middle income countries (LMICs), where the majority of the burden of burn injury is now experienced. This paper outlines the development of the Delivery Assessment Tool (DAT), a method for facilitating quality improvement in burn services in LMICs. We used a participatory approach that ensured that local clinicians and experts were fully involved in piloting the tool. The DAT is based on internationally agreed operational standards for burn care service delivery and has undergone an iterative process of improvement and refinement through an initial three-year project in Nepal and Bangladesh. The DAT, a 50-item tool organised into 10 subsections, is used to assess a service through a participatory focus group discussion with a mixed, multidisciplinary team of staff working at the burn service, typically 6–10 participants. This usually lasts 2–3 h. The staff in the unit then select priority areas for quality improvement programmes that are within their control to achieve, which starts a cycle of audit and review. The final version of the tool was used in a further three-year project to evaluate 11 hospitals in Nepal and Bangladesh. Education and training are key components of this work; both were provided by Interburns as part of on-going support for the clinical teams. At the end of the project a>19% improvement in scores was demonstrated using the final version of the DAT in both Nepal and Bangladesh; this achievement is remarkable given the continued difficulties in service provision where patient numbers far outstrip the resources available to care for them. As a result of this work, we have made a digital version of the tool available free of charge.

Image and report quality in non-obstetric ultrasound examinations undertaken by sonographers: Results of an audit of 3731 cases.

This paper reports the results of a rolling audit of sonographer-performed non-obstetric ultrasound examinations undertaken between 2010 and 2020 in a large University Teaching Hospital Ultrasound Department in the United Kingdom. We believe that this represents the largest published audit of sonographer non-obstetric ultrasound examination quality. Random samples of sonographer ultrasound examinations were regularly and systematically audited by consultant-level ultrasound practitioners through review of soft copy images and reports. Examination and report quality were assessed against an internal audit standard in 3731 patients over an 11-year period and also against externally set audit standards in 3186 patients over a nine-year period. Both image and report quality exceeded externally set audit standards in all nine years of audit. In the internal audit, the quality standard just failed to be met for the first five years of audit but was achieved in all the subsequent six years. This audit provides further information on the quality and safety of sonographer-led ultrasound service delivery within a service that has quality safeguards, readily available support and an active education programme. It is used not only to provide assurance to patients, clinicians, managers and commissioners of this service but also to direct individual professional development and drive an iterative process of quality improvement.

Standardized aseptic dressing change procedure: Optimizations and adherence in a prospective pre- and postintervention cohort study

The "HygArzt" project investigated the effectiveness of hygiene measures introduced by an infection prevention link physician (PLP). To investigate whether the introduction of a standardized aseptic dressing change concept (ADCC) by a PLP can increase hand hygiene adherence and adherence to specific process steps during an aseptic dressing change (ADC) in a trauma surgery and orthopedic department. We defined 4 required hand disinfection indications: (1) before the preparation of ADC equipment, (2) immediately before the ADC, (3) before the clean phase, and (4) after the ADC. A process analysis of the preintervention phase (331 ADCs) was used to develop a standardized ADCC. The ADCC was introduced and iteratively adopted during the intervention phase. The effect was evaluated during the postintervention phase (374 ADCs). Hand hygiene adherence was significantly increased by the introduction of the ADCC for all indications: (1) before the preparation of the ADC equipment (from 34% before to 85% after, P <.001), (2) immediately before an ADC (from 32% before to 85% after; P < .001), (3) before the clean phase (from 42% before to 96% after; P < .001), and (4) after an ADC (from 74% before to 99% after; P < .001). Overall hand hygiene adherence was analyzed before the indications for an ADC (from 9.6% before to 74% after; P < .001). The same strategy was applied to the following process parameters: use of a clean work surface, clean withdrawal of equipment from the dressing trolley, and appropriate waste disposal. A PLP sufficiently implemented a standardized concept for aseptic dressing change during an iterative improvement process, which resulted in a significant improvement in hand hygiene and adherence to other specific ADCC process steps.

Designing and Implementing an IT Architecture for a Digital Multicenter Dementia Registry: digiDEM Bayern.

Background Registries are an essential research tool to investigate the long-term course of diseases and their impact on the affected. The project digiDEM Bayern will set up a prospective dementia registry to collect long-term data of people with dementia and their caregivers in Bavaria (Germany) supported by more than 300 research partners. Objective The objective of this article is to outline an information technology (IT) architecture for the integration of a registry and comprehensive participant management in a dementia study. Measures to ensure high data quality, study governance, along with data privacy, and security are to be included in the architecture. Methods The architecture was developed based on an iterative, stakeholder-oriented process. The development was inspired by the Twin Peaks Model that focuses on the codevelopment of requirements and architecture. We gradually moved from a general to a detailed understanding of both the requirements and design through a series of iterations. The experience learned from the pilot phase was integrated into a further iterative process of continuous improvement of the architecture. Results The infrastructure provides a standardized workflow to support the electronic data collection and trace each participant's study process. Therefore, the implementation consists of three systems: (1) electronic data capture system for Web-based or offline app-based data collection; (2) participant management system for the administration of the identity data of participants and research partners as well as of the overall study governance process; and (3) videoconferencing software for conducting interviews online. First experiences in the pilot phase have proven the feasibility of the framework. Conclusion This article outlines an IT architecture to integrate a registry and participant management in a dementia research project. The framework was discussed and developed with the involvement of numerous stakeholders. Due to its adaptability of used software systems, a transfer to other projects should be easily possible.

Brain Tumor Biobank Development for Precision Medicine: Role of the Neurosurgeon.

Neuro-oncology biobanks are critical for the implementation of a precision medicine program. In this perspective, we review our first year experience of a brain tumor biobank with integrated next generation sequencing. From our experience, we describe the critical role of the neurosurgeon in diagnosis, research, and precision medicine efforts. In the first year of implementation of the biobank, 117 patients (Female: 62; Male: 55) had 125 brain tumor surgeries. 75% of patients had tumors biobanked, and 16% were of minority race/ethnicity. Tumors biobanked were as follows: diffuse gliomas (45%), brain metastases (29%), meningioma (21%), and other (5%). Among biobanked patients, 100% also had next generation sequencing. Eleven patients qualified for targeted therapy based on identification of actionable gene mutations. One patient with a hereditary cancer predisposition syndrome was also identified. An iterative quality improvement process was implemented to streamline the workflow between the operating room, pathology, and the research laboratory. Dedicated tumor bank personnel in the department of neurosurgery greatly improved standard operating procedure. Intraoperative selection and processing of tumor tissue by the neurosurgeon was integral to increasing success with cell culture assays. Currently, our institutional protocol integrates standard histopathological diagnosis, next generation sequencing, and functional assays on surgical specimens to develop precision medicine protocols for our patients. This perspective reviews the critical role of neurosurgeons in brain tumor biobank implementation and success as well as future directions for enhancing precision medicine efforts.

Development of research methods curriculum for an integrated care fellowship

ObjectiveTo describe the design and delivery of a curriculum in research methods for clinical fellows in integrated care. MethodTo design the curriculum, a standard curriculum development approach was applied through an iterative improvement process with input from researchers, clinical educators, and the first cohort of fellows. The curriculum has three central goals: (1) develop fellows' capacity to interpret the integrated care literature and apply findings in practice; (2) develop fellows' capacity for conducting quality improvement programs informed by knowledge of clinical research methods; and (3) enhance workforce capacity for practice-based research partnerships by increasing research understanding among clinical providers. A variety of educational strategies were employed to introduce each research method and apply these to the integrated care literature. ResultsA description, rationale, and resources for each content domain is presented. The curriculum was delivered to two cohorts of fellows. Evaluation data supports the curriculum's relevance and quality. ConclusionsA rigorous development process yielded a brief research curriculum targeting the needs of clinical fellows in integrated care. The curriculum is well-received by fellows and adaptable for other subspecialties. It may serve as a model for other clinical training programs seeking to enhance their fellows' fluency in research methods.

Improving Pediatric Resident Safety Event Reporting Using Quality Improvement Methods.

Safety event reporting systems facilitate identification of system-level targets to improve patient safety. Resident physicians report few safety events despite their role as frontline providers and the frequent occurrence of events. The objective of this study is to increase the number of pediatric resident safety event submissions from <1 to 4 submissions per 14-day period within 12 months. We conducted an iterative quality improvement process with 39 pediatric residents at a children's hospital. Interventions focused on 4 key drivers: user-friendly event submission process, resident buy-in, nonpunitive safety culture, and data transparency. The primary outcome measure of number of pediatric resident event submissions was analyzed by using statistical process control. Balancing measures included time from submission to feedback, duplicate submissions, and nonevent submissions. As a control, the primary outcome measure was monitored for nonpediatric residents during the same period. The mean number of pediatric resident event submissions increased from 0.9 to 5.7 submissions per 14 days. Impactful interventions included a designated space in the resident workroom to list safety events to submit, monthly project updates, and an interresident competition. There were no duplicate submissions or nonevent submissions in the postintervention period. Time to feedback in the postintervention period had both upward and downward shifts, with >8 consecutive points above and below the baseline period's centerline. The control group showed no sustained change in event submissions. Our improvement process was associated with significant increase in pediatric resident safety event submissions without an increase in the number of submissions categorized as duplicates or nonevents.