Research opportunities associated with the proliferation of the electronic health record (EHR), big data initiatives, and innovative approaches to trial design can present challenges for obtaining and documenting informed consent. Broad-scale informed consent (a term used herein to describe institutional models, rather than the Common Rule’s strict regulatory definition for “broad consent”) may facilitate the use of existing data and samples and speed the pace of research by minimizing barriers to consent. We explored the use of broad-scale informed consent within the Clinical Translational Science Award (CTSA) Program Network.

We surveyed CTSA Hubs concerning policies, practices, experiences, and needs within three domains of broad-scale informed consent: (1) participant recontact; (2) biospecimens; and (3) clinical data sharing.

Of 61 CTSA Hubs surveyed, 37 (61%) indicated ongoing work related to at least 1 domain of broad-scale informed consent; 18 Hubs (30%) reported work in all 3 domains. The EHR predominated as the implementation system across all three domains. Research and IT leadership and the Institutional Review Board were most commonly endorsed as institutional drivers, while systems/technical issues and impact on clinical workflow were the most commonly reported barriers.

While survey results indicate considerable variability in the implementation of broad-scale informed consent across the CTSA consortium, it is clear that all CTSA Hubs are actively considering policy and process related to these concepts. Next steps cluster within three areas: training and workforce development, streamlined policies and templates, and implementation strategies that facilitate integration into clinical workflow.

Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors.

This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets.

The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index.

The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals.

We aimed to improve the research consenting process by developing and evaluating simplified consent forms.

Four templates written at the eighth-tenth grade reading level were developed and trialed by a group of experts in clinical research, health literacy, national regulatory requirements, and end users. Researchers from protocols which had received expedited review were surveyed at 2 time points regarding their use and assessment of the templates.

At baseline 18/86 (20.9%) responding researchers had heard of the templates and 5 (5.8%) reported that they had used them; 2 years later, 54.2% (32/59) had heard of the templates and 87.5% (28/32) had used them (p<0.001).

Consent form templates may be one mechanism to improve patient comprehension of research protocols as well as efficiency of the review process, but require considerable time for development and implementation, and one key to their success is involvement and support from the IRB and technical staff.

Area-level social determinants of health (SDoH) and individual-level social risks are different, yet area-level measures are frequently used as proxies for individual-level social risks. This study assessed whether demographic factors were associated with patients being screened for individual-level social risks, the percentage who screened positive for social risks, and the association between SDoH and patient-reported social risks in a nationwide network of community-based health centers.

Electronic health record data from 1,330,201 patients with health center visits in 2021 were analyzed using multilevel logistic regression. Associations between patient characteristics, screening receipt, and screening positive for social risks (e.g., food insecurity, housing instability, transportation insecurity) were assessed. The predictive ability of three commonly used SDoH measures (Area Deprivation Index, Social Deprivation Index, Material Community Deprivation Index) in identifying individual-level social risks was also evaluated.

Of 244,155 (18%) patients screened for social risks, 61,414 (25.2%) screened positive. Sex, race/ethnicity, language preference, and payer were associated with both social risk screening and positivity. Significant health system-level variation in both screening and positivity was observed, with an intraclass correlation coefficient of 0.55 for social risk screening and 0.38 for positivity. The three area-level SDoH measures had low accuracy, sensitivity, and area under the curve when used to predict individual social needs.

Area-level SDoH measures may provide valuable information about the communities where patients live. However, policymakers, healthcare administrators, and researchers should exercise caution when using area-level adverse SDoH measures to identify individual-level social risks.

Multi-level dissemination strategies are needed to increase equitable access to effective treatment for high-risk outpatients with COVID-19, particularly among patients from disproportionately affected communities. Yet assessing population-level impact of such strategies can be challenging.

In collaboration with key contributors in Colorado, we conducted a retrospective cohort study to evaluate a multi-level dissemination strategy for neutralizing monoclonal antibody (mAb) treatment. Real-world data included county-level, de-identified output from a statewide mAb referral registry linked with publicly available epidemiological data. Outcomes included weekly number of mAb referrals, unique referring clinicians, and COVID-19 hospitalization rates. We assessed weekly changes in outcomes after dissemination strategies launched in July 2021.

Overall, mAb referrals increased from a weekly average of 3.0 to 15.5, with an increase of 1.3 to 42.1 additional referrals per county in each post-period week (p < .05). Number of referring clinicians increased from a weekly average of 2.2 to 9.7, with an additional 1.5 to 22.2 unique referring clinicians observed per county per week beginning 5 weeks post-launch (p < .001). Larger effects were observed in communities specifically prioritized by the dissemination strategies. There were no observed differences in COVID-19 hospitalization rates between counties with and without mAb treatment sites.

Real-world data can be used to estimate population impact of multi-level dissemination strategies. The launch of these strategies corresponded with increases in mAb referrals, but no apparent population-level effects on hospitalization outcomes. Strengths of this analytic approach include pragmatism and efficiency, whereas limitations include inability to control for other contemporaneous trends.

Early-stage clinical and translational researchers who set and track career goals, milestones, and progress are successful in career development. We aimed to determine the effectiveness of the Customized Career Development Platform (CCDP), an online individual development plan (IDP), versus the traditional IDP template in improving research success and career satisfaction.

We conducted a pragmatic cluster-randomized controlled trial of 340 scholars and trainees at 27 US academic healthcare institutions. The primary outcome was number of published manuscripts 24 months post-intervention. Secondary outcomes included the number of grant proposals submitted and funded, job satisfaction, and level of communication with mentors. An analysis of CCDP participants assessed proficiency level for the 14 Clinical and Translational Science Award (CTSA) competencies. Data were analyzed using intention-to-treat.

Participants were mostly female (60.3%) and Caucasian (67.2%); mean age was 34 years. Twenty-four months following the intervention, the CCDP versus traditional IDP groups showed a similar number of publications (9.4 vs 8.6), grants submitted (4.1 vs 4.4) and funded (1.3 vs 2.0), and job satisfaction score (3.6 vs 3.7). The CCDP group had higher odds of discussing communication (OR = 2.08) and leadership skills (OR = 2.62) and broadening their network (2.31) than the traditional IDP group. The CCDP arm reported improvements in 9 of the 14 CTSA competencies.

The CCDP offers CTSA hubs an innovative alternative to traditional IDP tools. Future studies are needed to elucidate why the CCDP users did not fully appreciate or adopt the functionality of the online platform.

Given the reach and influence of social media, the National Children’s Study Vanguard Study evaluated the feasibility, acceptability, and cost of using social media to support participant retention.

We describe a social media experiment designed to assess the impact of social media on participant retention, discuss several key considerations for integrating social media into longitudinal research, and review factors that may influence engagement in research-related social media.

User participation varied but was most active when at launch. During the short life of the private online community, a total of 39 participants joined. General enthusiasm about the prospect of the online community was indicated. There were many lessons learned throughout the process in areas such as privacy, security, and Institutional Review Board clearance. These are described in detail.

The opportunity to engage participants in longitudinal research using online social networks is enticing; however, more research is needed to consider the feasibility of their use in an ongoing manner. Recommendations are presented for future research seeking to use social media to improve retention in longitudinal research.

Implementation scientists increasingly recognize that the process of implementation is dynamic, leading to ad hoc modifications that may challenge fidelity in protocol-driven interventions. However, limited attention to ad hoc modifications impairs investigators’ ability to develop evidence-based hypotheses about how such modifications may impact intervention effectiveness and cost. We propose a multi-method process map methodology to facilitate the systematic data collection necessary to characterize ad hoc modifications that may impact primary intervention outcomes.

We employ process maps (drawn from systems science), as well as focus groups and semi-structured interviews (drawn from social sciences) to investigate ad hoc modifications. Focus groups are conducted with the protocol’s developers and/or planners (the implementation team) to characterize the protocol “as envisioned,” while interviews conducted with frontline administrators characterize the process “as realized in practice.” Process maps with both samples are used to identify when modifications occurred across a protocol-driven intervention. A case study investigating a multistage screening protocol for autism spectrum disorders (ASD) is presented to illustrate application and utility of the multi-method process maps.

In this case study, frontline administrators reported ad hoc modifications that potentially influenced the primary study outcome (e.g., time to ASD diagnosis). Ad hoc modifications occurred to accommodate (1) whether providers and/or parents were concerned about ASD, (2) perceptions of parental readiness to discuss ASD, and (3) perceptions of family service delivery needs and priorities.

Investigation of ad hoc modifications on primary outcomes offers new opportunities to develop empirically based adaptive interventions. Routine reporting standards are critical to provide full transparency when studying ad hoc modifications.

The Rare and Atypical DIAbetes NeTwork (RADIANT) aims to discover the underlying pathoetiology of atypical diabetes by conducting both genotyping and non-genetic deep phenotyping. While the return of genetic test results in research settings has been investigated, the return of non-genetic results (RoR-NG) has received less attention. We explore the RoR-NG with RADIANT investigators and participants.

We conducted one-on-one interviews with 10 adult RADIANT participants and 10 RADIANT investigators. Participants also completed two health literacy screening tools and a survey on perspectives regarding return of results (RoR). Investigators completed one survey on experience and confidence in explaining clinical tests utilized in the RADIANT study and another survey on perspectives regarding RoR.

Most participants were non-Hispanic White. All participants had high scores on health literacy screens. Both RADIANT participants and investigators expressed strong support for RoR-NG. RADIANT participants and investigators acknowledged the different roles and responsibilities between research and clinical care for interpreting and acting on non-genetic results. However, the lines between clinical care and research in returning and acting on results were often blurred by both participants and investigators.

Our study provides important insight into how both investigators and participants simultaneously distinguish and blur clinical and research roles and responsibilities when discussing non-genetic research results and the return of these results. Further study should engage individuals from diverse racial and ethnic backgrounds and with varying levels of health literacy to understand how best to support all participants when returning research results.

Although the science of team science is no longer a new field, the measurement of team science and its standardization remain in relatively early stages of development. To describe the current state of team science assessment, we conducted an integrative review of measures of research collaboration quality and outcomes.

Collaboration measures were identified using both a literature review based on specific keywords and an environmental scan. Raters abstracted details about the measures using a standard tool. Measures related to collaborations with clinical care, education, and program delivery were excluded from this review.

We identified 44 measures of research collaboration quality, which included 35 measures with reliability and some form of statistical validity reported. Most scales focused on group dynamics. We identified 89 measures of research collaboration outcomes; 16 had reliability and 15 had a validity statistic. Outcome measures often only included simple counts of products; publications rarely defined how counts were delimited, obtained, or assessed for reliability. Most measures were tested in only one venue.

Although models of collaboration have been developed, in general, strong, reliable, and valid measurements of such collaborations have not been conducted or accepted into practice. This limitation makes it difficult to compare the characteristics and impacts of research teams across studies or to identify the most important areas for intervention. To advance the science of team science, we provide recommendations regarding the development and psychometric testing of measures of collaboration quality and outcomes that can be replicated and broadly applied across studies.

HIV viral load self-testing could enable people living with HIV (PLHIV) to monitor their viral suppression status more easily, potentially facilitating medication adherence and safe behavior decision-making. Smartphone-based viral load testing innovations have the potential to reach resource-limited and vulnerable communities to address inequities in access to HIV care. However, successful development and translation of these tests requires meaningful investigation of end-user contexts and incorporation of those context-specific needs early in the design process. The objective of this study is to engage PLHIV and HIV healthcare providers in human-centered design research to inform key design and implementation considerations for a smartphone-based HIV viral load self-testing device prototype in development.

Semi-structured in-depth interviews were conducted with PLHIV (n = 10) and HIV providers (n = 4) in Indiana, a state with suboptimal viral suppression rates and marked disparities in access to HIV care. Interview guides were developed based on contextual investigation and human-centered design frameworks and included a demonstration of the device prototype with feedback-gathering questions.

Thematic analysis of interview transcripts revealed important benefits, concerns, and user requirements for smartphone-based HIV VL self-testing within the context of PLHIV lived experience, knowledge, and barriers to care in Indiana.

End-user needs and preferences were identified as key design specifications and implementation considerations to facilitate the acceptability and inform ongoing development and ultimately real-world translation of the HIV VL monitoring device prototype.