David Williams Lounsbury

Objective: Pediatric tracheostomy emergencies are high‐acuity events requiring rapid, coordinated team‐based care. This study assessed objective team performance and used in situ simulation to identify systems factors, including latent safety threats (LSTs) and resilience supports, that influence pediatric tracheostomy emergency readiness across diverse clinical settings. Methods: In situ simulations of pediatric tracheostomy emergencies (obstructed or partially dislodged tubes) were conducted across inpatient floors, critical care units, and emergency departments at 11 pediatric hospitals. Time to completion of critical actions was recorded using the NeoCHART+™ for PEAK‐II mobile application. Structured debriefs identified LSTs and resilience supports at unit and hospital levels. Multivariable Cox proportional hazards models assessed predictors of time‐critical actions (tube replacement, suction, first effective ventilation) and the percentage of critical actions completed. Results: Sixty‐seven baseline simulations were conducted across 11 institutions. Seventy‐seven percent of teams replaced an identical tracheostomy tube within 5 minutes (median 188.4 seconds, [IQR 165.3, 204.6]). Teams reported a median of 3.2 LSTs per simulation (IQR 3.0, 4.0). Common LSTs included attempted ventilation through occluded tracheostomy tubes, unclear leadership, ambiguous role definitions, and non‐standardized equipment locations. Teams with a RT and at institutions with a dedicated tracheostomy team performed time‐critical actions faster. Discussion: In situ simulation revealed LSTs, resilience supports, and performance variability that may influence effective pediatric tracheostomy emergency response at the systems level. Implications for Practice: Variability identified in systems factors and team performance can guide targeted interventions to improve pediatric tracheostomy emergency preparedness.

Background: Neighborhood revitalization is a process through which land use rezoning and capital investment can spur new resources, such as access to healthful food and amenities for physical activity. While revitalization efforts may promote cardiovascular health, their benefits may not be distributed equally across sociodemographic groups. Objective: The objective of the study is to apply a socioecological framework that uses a multimethod approach incorporating quantitative data (longitudinal electronic health records and cross-sectional surveys) and qualitative data (longitudinal “walk-a-long” interviews) to examine the short-term effect of neighborhood land use rezoning and revitalization efforts on cardiovascular disease (CVD), CVD-related health behaviors, and access to and utilization of health care. System science methods, namely microsimulation modeling and system dynamics modeling, will be used to assess the long-term effects of land use rezoning policy and revitalization efforts on cardiovascular health and ways to sustain priority health equity goals in revitalized neighborhoods. Methods: We leverage a land use rezoning initiative in the Bronx, New York, where a largely commercial area is being rezoned along with capital investments to expand healthful neighborhood resources. Using electronic health records from a single hospital system, we will follow cohorts of midlife and older adults (≥50 y) residing in both the rezoned area and a comparison area. We will assess clinically measured incident CVD and other CVD risk factors to evaluate changes in cardiovascular health over time. In parallel, we will conduct a cross-sectional survey and a purposive sampling of patients for in-person “walk-a-long” qualitative interviews to understand how residents perceive neighborhood access to healthful resources after land use rezoning. To estimate long-term effects, we will use a validated microsimulation model to project CVD outcomes and costs. Finally, we will use system dynamics modeling to integrate quantitative and qualitative findings to inform future revitalization and public health strategies. Results: Midlife and older adult patients (N=10,813) in the intervention area and the comparison area will be followed for approximately 7 years following land use rezoning and revitalization efforts to compare CVD risk between neighborhoods. The cross-sectional survey (n=300) and qualitative assessment (n=36) will increase understanding of perceptions of access to healthful resources and related health behaviors among residents. Systems science approaches will estimate long-term CVD risk and related costs associated with revitalization efforts. An advisory committee of clinical and community stakeholders will assist in interpreting results and developing dissemination strategies for their constituents. This study was funded from January 2023 until December 2026. Conclusions: This study uses a socioecological framework to provide a novel, transferable method for evaluating the impact of neighborhood revitalization efforts on cardiovascular health by combining methods to examine short- and long-term effects across individual, neighborhood, and structural (system) levels over time. Findings will inform policies aimed at reducing CVD through equitable urban revitalization.

Public health data and tools have proliferated, yet practical guidance for community-engaged data-driven decision making is limited. The HEALing Communities Study (HCS) was a randomized, wait-list controlled trial to assess the impact of an intervention to reduce fatal opioid overdoses in 67 highly affected communities across 4 sites (Kentucky, Massachusetts, New York, and Ohio). HCS researchers implemented the Communities That HEAL intervention, a phased approach which included a coalition-engaged, data-driven approach to selection of evidence-based practice strategies to reduce fatal opioid overdoses. Core steps to the data-driven approach included data selection, access, display, and engagement. Staff selected metrics that aligned with study goals, accessed data from numerous sources, created visualizations, and engaged coalition members to assess resource gaps and intervention opportunities. At the intervention conclusion, all 4 sites’ staff collectively workshopped best practices and barriers encountered to data-driven decision making. This article explains the data-driven decision-making approach implemented, assessment results, alterations for subsequent implementation, and guidance for future implementations.

The NIH’s Clinical and Translational Science Award (CTSA) program has placed greater emphasis on Continuous Quality Improvement (CQI) in recent years. Our institution’s CTSA-supported Institute for Clinical and Translational Research (ICTR) implemented a novel CQI process in response. This manuscript shares lessons learned from our implementation, reflecting a paradigm shift from managing an “evaluation program” to creating a process whose central goal is CQI. Our objective is to share these reflections to support other CTSA hubs’ efforts to successfully implement CQI programs. Key elements of our implementation included (1) establishing a shared understanding about CQI’s purpose; (2) leveraging a centralized management approach while addressing barriers to implementation; and (3) creating structures that foster collaboration. The CQI framework we chose, FACE (Focus, Analyze, Change, Evaluate), enabled us not only to improve the activities of ICTR modules but also, over time, to refine the CQI process itself. Through regular convenings of module leaders, the ICTR has sought to cultivate a culture of CQI as a dynamic, participatory process that supports mutual learning and collective problem-solving.

Objectives: This paper describes how artificial intelligence (AI) was used to analyze meeting minutes from community coalitions participating in the HEALing Communities Study. We examined how often coalitions discussed stigma when selecting evidence-based practices (EBPs), variations in stigma-related discussions across coalitions, how these discussions addressed race, ethnicity, and racial inequity, and whether the frequency of stigma discussions was associated with the proportion of minoritized populations in each community. Methods: We used Natural Language Processing, Machine Learning, and Large Language Models, employing ChatGPT Enterprise to code data, ensuring data security and privacy compliance with the General Data Protection Regulation and HIPAA. Results: Community coalitions varied in the extent to which they discussed stigma during meetings focused on EBPs to reduce overdose deaths. Stigma was mentioned more frequently in the context of medication for opioid use disorder compared with other EBPs. As the percentage of racial/ethnic minority populations increased in a county, so did the strength of the association between discussions of EBPs and stigma. Counties with a greater proportion of racial/ethnic minority populations were more likely to integrate discussions of EBPs with stigma-related issues. Specifically, discussions about stigma were ~57% more likely to occur when racial or ethnic disparities were mentioned, compared with when they were not (odds ratio=1.57; 95% CI: 1.22, 2.03). Conclusions: The paper highlights the potential for integrating AI-human collaboration into community-engaged research, particularly in leveraging qualitative data such as meeting minutes. It shows how AI can be used in real-time to enhance community-based research.

Structured processes to improve the quality and impact of clinical and translational research are a required element of the Clinical and Translational Sciences Awards (CTSA) program and are central to awardees’ strategic management efforts. Quality improvement is often assumed to be an ordinary consequence of evaluation programs, in which standardized metrics are tabulated and reported externally. Yet evaluation programs may not actually be very effective at driving quality improvement: required metrics may lack direct relevance; they lack incentive to improve on areas of relative strength; and the validity of inter-site comparability may be limited. In this article, we describe how we convened leaders at our CTSA hub in an iterative planning process to improve the quality of our CTSA program by intentionally focusing on how data collection activities can primarily advance continuous quality improvement (CQI) rather than strictly serve as evaluative tools. We describe our CQI process, which consists of three key components: (1) Logic models outlining goals and associated mechanisms; (2) relevant metrics to evaluate performance improvement opportunities; and (3) an interconnected and collaborative CQI framework that defines actions and timelines to enhance performance.

This study employs a combination of the Health Belief Model (HBM) and System Dynamics (SD) modeling to investigate the dynamics of COVID-19 vaccine acceptance in the United States. Utilizing survey data from the CHASING COVID study from April 2020 to December 2021, our model explores relationships between HBM constructs and vaccination intentions. The system is dominated by three feedback loops, where increased vaccination positively impacts perceived benefits (Reinforcing Loop 1) and lowers perceived barriers (Reinforcing Loop 2), accelerating vaccine acceptance, and decreased perceived threat due to declining infection rates (Balancing Loop 1) hinders vaccine acceptance. Compared to their White counterparts, young Black adults exhibited unique behavior with higher perceived barriers and prolonged vaccination timelines, leading to lower vaccine acceptance. This analysis highlights the utility of the HBM and SD modeling in comprehending and addressing vaccine hesitancy, offering insights applicable beyond pandemics to improve responses during public health emergencies.

Many public health challenges are characterized by complexity that reflects the dynamic systems in which they occur. Such systems involve multiple interdependent factors, actors, and sectors that influence health, and are a primary driver of challenges of insufficient implementation, sustainment, and scale of evidence-based public health interventions. Implementation science frameworks have been developed to help embed evidence-based interventions in diverse settings and identify key factors that facilitate or hinder implementation. These frameworks are largely static in that they do not explain the nature and dynamics of interrelationships among the identified determinants, nor how those determinants might change over time. Furthermore, most implementation science frameworks are top-down, deterministic, and linear, leaving critical gaps in understanding of both how to intervene on determinants of successful implementation and how to scale evidence-based solutions. Design thinking and systems science offer methods for transforming this problem-oriented paradigm into one that is solution-oriented. This article describes these two approaches and how they can be integrated into implementation science strategies to promote implementation, sustainment, and scaling of public health innovation, ultimately resulting in transformative systems changes that improve population health.

Objective: Low and middle income countries of Africa account for a disproportionate amount of the global health burden of cancer. Providing patients access to psychosocial oncology and palliative care through policy structures such as the National Cancer Control Plans (NCCP) is essential to improving the care provided to patients and their families. The first phase of this study sought to determine the extent to which palliative care and psychosocial oncology were integrated in NCCPs in African countries.Methods: A qualitative thematic analysis of the plans was used using Nvivo, with two‐raters coding and continuous team discussions. Data were organized into an infographic map showing the coverage of themes across African countries.Results: Fifty‐eight NCCPs and NCD plans were analyzed in the 54 countries in Africa. The findings illustrate a lack of standardization across countries' NCCPs in addressing psychosocial oncology and palliative care themes. Certain areas presented good coverage across several plans, such as barriers to access, education, awareness, and health behaviors, coordination of care, families, caregivers and community involvement, and palliative care. Other themes presented low coverage, such as doctor‐patient communication, mental health, bereavement, psychosocial care, survivorship care, and traditional medicine.Conclusions: One may consider further developing NCCP areas as they pertain to psychosocial oncology and palliative care to ensure their proper place on the policy agenda for a healthier Africa.