High impact biomedical research is increasingly conducted by large, transdisciplinary, multisite teams in an increasingly collaborative environment. Thriving in this environment requires robust teamwork skills, which are not acquired automatically in the course of traditional scientific education. Team science skills training does exist, but most is directed at clinical care teams, not research teams, and little is focused on the specific training needs of early-career investigators, whose early team leadership experiences may shape their career trajectories positively or negatively. Our research indicated a need for team science training designed specifically for early-career investigators.

To address this need, we designed and delivered a 2-day workshop focused on teaching team science skills to early-career investigators. We operationalized team science competencies, sought the advice of team science experts, and performed a needs assessment composed of a survey and a qualitative study. Through these multiple approaches, we identified and grouped training priorities into three broad training areas and developed four robust, hands-on workshop sessions.

Attendees comprised 30 pre- and post-doc fellows (TL1) and early-career faculty (KL2 and K12). We assessed impact with a pre- and post-workshop survey adapted from the Team Skills Scale. Results from the pre- and post-test Wilcoxon signed-rank analysis (n = 25) showed statistically significant improvement in team science skills and confidence. Open-ended responses indicated that the workshop focus was appropriate and well targeted to the trainees’ needs.

Although team science education is still very much in its infancy, these results suggest that training targeted to early-career investigators improves team skills and may foster improved collaboration.

We describe the effectiveness of community outreach and engagement in supporting recruitment for the US National Children’s Vanguard Study between 2009 and 2012.

Thirty-seven study locations used 1 of 4 strategies to recruit 18–49-year-old pregnant or trying to conceive women: (1) Initial Vanguard Study used household-based recruitment; (2) Direct Outreach emphasized self-referral; (3) Enhanced Household-Based Recruitment enhanced Initial Vanguard Study strategies; and (4) Provider-Based Recruitment recruited through healthcare providers. Outreach and engagement included advance letters, interactions with healthcare providers, participation in community events, contacts with community organizations, and media outreach.

After 1–2 years, 41%–74% of 9844 study-eligible women had heard about the National Children’s Vanguard Study when first approached. Women who heard were 1.5–3 times more likely to consent. Hearing via word-of-mouth or the media most frequently predicted consent. The more sources women heard from the higher the odds of consent.

We conclude that tailored outreach and engagement facilitate recruitment in cohort studies.

Recent revisions to the US Federal Common Rule governing human studies funded or conducted by the federal government require the provision of a “concise and focused” key information (KI) section in informed consent forms (ICFs). We performed a systematic study to characterize KI sections of ICFs for federally funded trials available on ClinicalTrials.gov.

We downloaded ICFs posted on ClinicalTrials.gov for treatment trials initiated on or after the revised Common Rule effective date. Trial records (n = 102) were assessed by intervention type, study phase, recruitment status, and enrollment size. The ICFs and their KI sections, if present, were characterized by page length, word count, readability, topic, and formatting elements.

Of the 102 trial records, 76 had identifiable KI sections that were, on average, 10% of the total length of full ICF documents. KI readability grade level was not notably different from other sections of ICFs. Most KI sections were distinguished by section headers and included lists but contained few other formatting elements. Most KI sections included a subset of topics consistent with the basic elements of informed consent specified in the Common Rule.

Many of the KI sections in the study sample aligned with practices suggested in the preamble to the revised Common Rule. Further, our results suggest that some KI sections were tailored in study-specific ways. Nevertheless, guidelines on how to write concise and comprehensible KI sections would improve the utility and readability of KI sections.

Local nodes on federated research and data networks (FR&DNs) provide enabling infrastructure for collaborative clinical and translational research. Studies in other fields note that infrastructuring, that is, work to identify and negotiate relationships among people, technologies, and organizations, is invisible, unplanned, and undervalued. This may explain the limited literature on nodes in FR&DNs in health care.

A retrospective case study of one PCORnet® node explored 3 questions: (1) how were components of infrastructure assembled; (2) what specific work was required; and (3) what theoretically grounded, pragmatic questions should be considered when infrastructuring a node for sustainability. Artifacts, work efforts, and interviews generated during node development and implementation were reviewed. A sociotechnical lens was applied to the analysis. Validity was established with internal and external partners.

Resources, services, and expertise needed to establish the node existed within the organization, but were scattered across work units. Aligning, mediating, and institutionalizing for sustainability among network and organizational teams, governance, and priorities consumed more work efforts than deploying technical aspects of the node. A theoretically based set of questions relevant to infrastructuring a node was developed and organized within a framework of infrastructuring emphasizing enacting technology, organizing work, and institutionalizing; validity was established with internal and external partners.

FR&DNs are expanding; we provide a sociotechnical perspective on infrastructuring a node. Future research should evaluate the applicability of the framework and questions to other node and network configurations, and more broadly the infrastructuring required to enable and support federated clinical and translational science.

We tested the ability of our natural language processing (NLP) algorithm to identify delirium episodes in a large-scale study using real-world clinical notes.

We used the Rochester Epidemiology Project to identify persons ≥ 65 years who were hospitalized between 2011 and 2017. We identified all persons with an International Classification of Diseases code for delirium within ±14 days of a hospitalization. We independently applied our NLP algorithm to all clinical notes for this same population. We calculated rates using number of delirium episodes as the numerator and number of hospitalizations as the denominator. Rates were estimated overall, by demographic characteristics, and by year of episode, and differences were tested using Poisson regression.

In total, 14,255 persons had 37,554 hospitalizations between 2011 and 2017. The code-based delirium rate was 3.02 per 100 hospitalizations (95% CI: 2.85, 3.20). The NLP-based rate was 7.36 per 100 (95% CI: 7.09, 7.64). Rates increased with age (both p < 0.0001). Code-based rates were higher in men compared to women (p = 0.03), but NLP-based rates were similar by sex (p = 0.89). Code-based rates were similar by race and ethnicity, but NLP-based rates were higher in the White population compared to the Black and Asian populations (p = 0.001). Both types of rates increased significantly over time (both p values < 0.001).

The NLP algorithm identified more delirium episodes compared to the ICD code method. However, NLP may still underestimate delirium cases because of limitations in real-world clinical notes, including incomplete documentation, practice changes over time, and missing clinical notes in some time periods.

Dissemination and implementation (D&I) science is not a formal element of the Clinical Translational Science Award (CTSA) Program, and D&I science activities across the CTSA Consortium are largely unknown.

The CTSA Dissemination, Implementation, and Knowledge Translation Working Group surveyed CTSA leaders to explore D&I science-related activities, barriers, and needed supports, then conducted univariate and qualitative analyses of the data.

Out of 67 CTSA leaders, 55.2% responded. CTSAs reported directly funding D&I programs (54.1%), training (51.4%), and projects (59.5%). Indirect support (e.g., promoted by CTSA without direct funding) for D&I activities was higher – programs (70.3%), training (64.9%), and projects (54.1%). Top barriers included funding (39.4%), limited D&I science faculty (30.3%), and lack of D&I science understanding (27.3%). Respondents (63.4%) noted the importance of D&I training and recommended coordination of D&I activities across CTSAs hubs (33.3%).

These findings should guide CTSA leadership in efforts to raise awareness and advance the role of D&I science in improving population health.

Individuals experiencing different medical conditions, as well as healthy volunteers, may often be interested in trial participation, and researchers similarly need to find participants to advance medical knowledge. The ResearchMatch (RM) Trials Today clinical trial searching tool leverages clinicaltrials.gov data to enable potential participants to look for trial opportunities relevant to their situation. To facilitate expanded use of this tool, we undertook a national digital public awareness campaign to increase awareness of Trials Today among members of the general public.

The awareness campaign promoted Trials Today using Facebook and digital banner messages in 2017, encompassing nine cities across the USA. The digital strategy was complemented by print media in several outlets. We employed descriptive statistics to summarize campaign metrics and site usage data during the campaign.

The campaign was successful in increasing visits to Trials Today, with 142,303 sessions logged during its run, as compared to pre-campaign data indicating 104,688 total sessions during the entire 2-year period since the site’s inception. The city-specific click-through rate for all digital impressions, combining Facebook and banner messaging, ranged from 0.50% to 1.09%, resulting in a cost-per-click range of $0.69–$1.15. In addition, visitors conducted 29,697 searches and viewed individual trial records 173,512 times.

The public awareness campaign was successful in increasing use of the RM Trials Today clinical trial searching tool. Our findings support the value of digital media messaging as a cost-effective vehicle for promoting clinical trial awareness, especially for chronic ailments.

Clinical trials, which are mainly conducted in urban medical centers, may be less accessible to rural residents. Our aims were to assess participation and the factors associated with participation of rural residents in clinical trials.

Using geocoding, the residential address of participants enrolled into clinical trials at Mayo Clinic locations in Arizona, Florida, and the Midwest between January 1, 2016, and December 31, 2017, was categorized as urban or rural. The distance travelled by participants and trial characteristics was compared between urban and rural participants. Ordinal logistic regression analyses were used to evaluate whether study location and risks were associated with rural participation in trials.

Among 292 trials, including 136 (47%) cancer trials, there were 2313 participants. Of these, 731 (32%) were rural participants, which is greater than the rural population in these 9 states (19%, P < 0.001). Compared to urban participants, rural participants were older (65 ± 12 years vs 64 ± 12 years, P = 0.004) and travelled further to the medical center (103 ± 104 vs 68 ± 88 miles, P < 0.001). The proportion of urban and rural participants who were remunerated was comparable. In the multivariable analysis, the proportion of rural participants was lower (P < 0.001) in Arizona (10%) and Florida (18%) than the Midwest (38%) but not significantly associated with the study-related risks.

Approximately one in three clinical trial participants were rural residents versus one in five in the population. Rural residents travelled further to access clinical trials. The study-associated risks were not associated with the distribution of rural and urban participants in trials.

Growing recognition that collaboration among scientists from diverse disciplines fosters the emergence of solutions to complex scientific problems has spurred initiatives to train researchers to collaborate in interdisciplinary teams. Evaluations of collaboration patterns in these initiatives have tended to be cross-sectional, rather than clarifying temporal changes in collaborative dynamics. Mobile health (mHealth), the science of using mobile, wireless devices to improve health outcomes, is a field whose advancement needs interdisciplinary collaboration. The NIH-supported annual mHealth Training Institute (mHTI) was developed to meet that need and provides a unique testbed.

In this study, we applied a longitudinal social network analysis technique to evaluate how well the program fostered communication among the disciplinarily diverse scholars participating in the 2017−2019 mHTIs. By applying separable temporal exponential random graph models, we investigated the formation and persistence of project-based and fun conversations during the mHTIs.

We found that conversations between scholars of different disciplines were just as likely as conversations within disciplines to form or persist in the 2018 and 2019 mHTI, suggesting that the mHTI achieved its goal of fostering interdisciplinary conversations and could be a model for other team science initiatives; this finding is also true for scholars from different career stages. The presence of team and gender homophily effects in certain years suggested that scholars tended to communicate within the same team or gender.

Our results demonstrate the usefulness of longitudinal network models in evaluating team science initiatives while clarifying the processes driving interdisciplinary communications during the mHTIs.