To quantifying the interdependency within the regulatory environment governing human subject research, including Institutional Review Boards (IRBs), federally mandated Medicare coverage analysis and contract negotiations.

Over 8000 IRB, coverage analysis and contract applications initiated between 2013 and 2016 were analyzed using traditional and machine learning analytics for a quality improvement effort to improve the time required to authorize the start of human research studies.

Staffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Using key variables, a predictive algorithm identified outliers for a workflow distinct from the standard process. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.

Understanding and improving the interdependencies between IRB, coverage analysis and contract negotiation offices requires a systems approach and might benefit from predictive analytics.

The field of dissemination and implementation science has the potential to narrow the translational research-to-practice gap and improve the use of evidence-based practices (EBPs) within community-based settings. Yet, foundational research related to dissemination efforts, such as understanding researcher attitudes, practices, and the determinants to sharing research findings, is lacking within extant literature.

A sequential explanatory (QUAN $$ \to $$ qual) mixed methods design was used to examine 85 academic researchers’ perspectives and self-reported dissemination methods used to share research outcomes with community stakeholders to better understand researcher’s usual dissemination practices (referred to as dissemination-as-usual). Quantitative surveys collected researcher demographic data, attitudes toward dissemination efforts, and dissemination strategy use.

Multiple linear regression examined predictors of the quantity of dissemination strategies utilized by researchers, finding that years since earning their degree, time spent disseminating, and the number of reasons for engaging in dissemination efforts predicted greater numbers of dissemination strategies utilized by researchers. Individual, semi-structured interviews with a subset of researchers (n = 18) expanded upon quantitative findings, identifying barriers and facilitators to their dissemination efforts. Data strands were integrated using a joint display, and the Dissemination of Research model guided data interpretation. More established researchers experienced fewer barriers and more facilitators to support their use of a variety of dissemination strategies to share findings with community stakeholders. However, researchers reported needing specific training, institutional support, and/or dedicated time to plan and enact dissemination strategies.

The necessary first step in research translation is the dissemination of research evidence, and understanding dissemination-as-usual can identify areas of need to advance translational science.

Community stakeholders often participate in community research training curricula development. There is limited information describing how their input informs curricula. This paper describes input solicitation methods, input received, and examples of its integration.

From June 2014 to June 2016, community members (CMs) and community-based organizations (CBOs) guided curricula development tailored for CMs and CBOs, respectively. Engagement methods included a strategic planning retreat, surveys, a listening session, workgroup meetings, and community engagement studios. Descriptive statistics were used to summarize survey input. For other methods, input was extracted and compiled from facilitator notes.

CMs (n=37) and CBOs (n=83) providing input included patients and caregivers and advocacy, community service, and faith-based organizations, respectively. The major feedback categories were training topic priorities, format (e.g., face-to-face vs. online), logistics (e.g., training frequency), and compensation (e.g., appropriateness). Input directly guided design of CBO and CM curricula (e.g., additional time devoted to specific topics based on feedback) or helped to finalize logistics.

Multiple quantitative and qualitative methods can be used to elicit input from community stakeholders to inform the development of community research training curricula. This input is essential for the development of training curricula that are culturally relevant and acceptable.

Studies examining the role of geographic factors in coronavirus disease-2019 (COVID-19) epidemiology among rural populations are lacking.

Our study is a population-based longitudinal study based on rural residents in four southeast Minnesota counties from March through October 2020. We used a kernel density estimation approach to identify hotspots for COVID-19 cases. Temporal trends of cases and testing were examined by generating a series of hotspot maps during the study period. Household/individual-level socioeconomic status (SES) was measured using the HOUSES index and examined for association between identified hotspots and SES.

During the study period, 24,243 of 90,975 residents (26.6%) were tested for COVID-19 at least once; 1498 (6.2%) of these tested positive. Compared to other rural residents, hotspot residents were overall younger (median age: 40.5 vs 43.2), more likely to be minorities (10.7% vs 9.7%), and of higher SES (lowest HOUSES [SES] quadrant: 14.6% vs 18.7%). Hotspots accounted for 30.1% of cases (14.5% of population) for rural cities and 60.8% of cases (27.1% of population) for townships. Lower SES and minority households were primarily affected early in the pandemic and higher SES and non-minority households affected later.

In rural areas of these four counties in Minnesota, geographic factors (hotspots) play a significant role in the overall burden of COVID-19 with associated racial/ethnic and SES disparities, of which pattern differed by the timing of the pandemic (earlier in pandemic vs later). The study results could more precisely guide community outreach efforts (e.g., public health education, testing/tracing, and vaccine roll out) to those residing in hotspots.

With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist.

HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions?

This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected.

This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.

The Clinical and Translational Science Awards (CTSA) Consortium, about 60 National Institutes of Health (NIH)-supported CTSA hubs at academic health care institutions nationwide, is charged with improving the clinical and translational research enterprise. Together with the NIH National Center for Advancing Translational Sciences (NCATS), the Consortium implemented Common Metrics and a shared performance improvement framework.

Initial implementation across hubs was assessed using quantitative and qualitative methods over a 19-month period. The primary outcome was implementation of three Common Metrics and the performance improvement framework. Challenges and facilitators were elicited.

Among 59 hubs with data, all began implementing Common Metrics, but about one-third had completed all activities for three metrics within the study period. The vast majority of hubs computed metric results and undertook activities to understand performance. Differences in completion appeared in developing and carrying out performance improvement plans. Seven key factors affected progress: hub size and resources, hub prior experience with performance management, alignment of local context with needs of the Common Metrics implementation, hub authority in the local institutional structure, hub engagement (including CTSA Principal Investigator involvement), stakeholder engagement, and attending training and coaching.

Implementing Common Metrics and performance improvement in a large network of research-focused organizations proved feasible but required substantial time and resources. Considerable heterogeneity across hubs in data systems, existing processes and personnel, organizational structures, and local priorities of home institutions created disparate experiences across hubs. Future metric-based performance management initiatives across heterogeneous local contexts should anticipate and account for these types of differences.

How the productivity and careers of KL2 scholars compare with scholars receiving individual K-awards is unknown.

The productivity of KL2 scholars (n=21) at our institution was compared with that of K08 (n=34) and K23 (n=26) scholars.

KL2 and K23 scholars had greater productivity than K08 scholars (p=0.01). Professional advancement was similar among groups.

At our institution, scholarly productivity and professional advancement did not differ by type of K-award.

Health systems have many incentives to screen patients for health-related social needs (HRSNs) due to growing evidence that social determinants of health impact outcomes and a new regulatory context that requires health equity measures. This study describes the experience of one large urban health system in scaling HRSN screening by implementing improvement strategies over five years, from 2018 to 2023.

In 2018, the health system adapted a 10-item HRSN screening tool from a widely used, validated instrument. Implementation strategies aimed to foster screening were retrospectively reviewed and categorized according to the Expert Recommendations for Implementing Change (ERIC) study. Statistical process control methods were utilized to determine whether implementation strategies contributed to improvements in HRSN screening activities.

There were 280,757 HRSN screens administered across 311 clinical teams in the health system between April 2018 and March 2023. Implementation strategies linked to increased screening included integrating screening within an online patient portal (ERIC strategy: involve patients/consumers and family members), expansion to discrete clinical teams (ERIC strategy: change service sites), providing data feedback loops (ERIC strategy: facilitate relay of clinical data to providers), and deploying Community Health Workers to address HRSNs (ERIC strategy: create new clinical teams).

Implementation strategies designed to promote efficiency, foster universal screening, link patients to resources, and provide clinical teams with an easy-to-integrate tool appear to have the greatest impact on HRSN screening uptake. Sustained increases in screening demonstrate the cumulative effects of implementation strategies and the health system’s commitment toward universal screening.

To identify associations between demographics, social determinants of health, health conditions, and reported history of insomnia. A cross-sectional study including 11,960 adult community members recruited through HealthStreet, a community outreach program at University of Florida.

Health assessments were conducted via interviews. Participants reported their demographic background, level of social support, history of health conditions, and insomnia. Logistic regression was used to understand associations between risk factors and history of insomnia.

The prevalence of self-reported insomnia was 27.3%. Adults aged ≥ 65 years (OR = 1.16) and women (OR = 1.18) reported higher rates of insomnia than their counterparts. Black/African American individuals reported lower rates of insomnia (OR = 0.72) than White individuals. Individuals with food insecurity (OR = 1.53), a military history (OR = 1.30), lower social support (OR = 1.24), living alone (OR = 1.14), anxiety (OR = 2.33), cardiometabolic disease (OR = 1.58), and attention-deficit hyperactivity disorder (ADHD) (OR = 1.44) were significantly more likely to endorse insomnia compared with their counterparts. Depression (OR = 2.57) had the strongest association with insomnia.

This study provides evidence regarding who is at greater risk for insomnia among a large community-based sample. Our findings highlight the importance of screening for insomnia, particularly among patients who experience food insecurity, are military veterans, have anxiety, depression, ADHD, or cardiometabolic disease, as well as those who live alone or have lower levels of social support. Future public health campaigns should provide education on insomnia symptoms, treatments, and evidenced-based sleep-promotion strategies.

To enhance enrollment into randomized clinical trials (RCTs), we proposed electronic health record-based clinical decision support for patient–clinician shared decision-making about care and RCT enrollment, based on “mathematical equipoise.”

As an example, we created the Knee Osteoarthritis Mathematical Equipoise Tool (KOMET) to determine the presence of patient-specific equipoise between treatments for the choice between total knee replacement (TKR) and nonsurgical treatment of advanced knee osteoarthritis.

With input from patients and clinicians about important pain and physical function treatment outcomes, we created a database from non-RCT sources of knee osteoarthritis outcomes. We then developed multivariable linear regression models that predict 1-year individual-patient knee pain and physical function outcomes for TKR and for nonsurgical treatment. These predictions allowed detecting mathematical equipoise between these two options for patients eligible for TKR. Decision support software was developed to graphically illustrate, for a given patient, the degree of overlap of pain and functional outcomes between the treatments and was pilot tested for usability, responsiveness, and as support for shared decision-making.

The KOMET predictive regression model for knee pain had four patient-specific variables, and an r2 value of 0.32, and the model for physical functioning included six patient-specific variables, and an r2 of 0.34. These models were incorporated into prototype KOMET decision support software and pilot tested in clinics, and were generally well received.

Use of predictive models and mathematical equipoise may help discern patient-specific equipoise to support shared decision-making for selecting between alternative treatments and considering enrollment into an RCT.