Accuracy of Large Language Models in Generating Rare Disease Differential Diagnosis Using Key Clinical Features.

Generating differential diagnoses for rare disease patients can be time intensive and highly dependent on the background and training of the evaluating physicians. Large language models (LLMs) have the potential to complement this process by automatically generating differentials to support physicians, but their performance in real-world patient populations remains underexplored. To this end, we assessed the diagnostic accuracy of ChatGPT-4o, Llama 3.

Insights from the trial innovation network's initial consultation process.

Multicenter clinical trials are essential for evaluating interventions but often face significant challenges in study design, site coordination, participant recruitment, and regulatory compliance. To address these issues, the National Institutes of Health's National Center for Advancing Translational Sciences established the Trial Innovation Network (TIN). The TIN offers a scientific consultation process, providing access to clinical trial and disease experts who provide input and recommendations throughout the trial's duration, at no cost to investigators.

Enhancing Clinical Data Management through Barcode Integration and REDCap: Innovations in Scalability and Adaptability.

Background: Effective data management is crucial in clinical studies for precise tracking, secure storage, and reliable analysis of samples. Traditional systems often encounter challenges like barcode recognition errors, inadequate data detail, and diminished performance under heavy workloads.

Objective: This paper aims to enhance clinical data management by improving barcode robustness, increasing data granularity, and boosting system throughput.

Leveraging data science to understand and address multimorbidity in sub-Saharan Africa: the MADIVA protocol.

Introduction: Multimorbidity (MM), defined as two or more chronic diseases in an individual, is linked to adverse outcomes. MM is increasing in sub-Saharan Africa due to rapidly advancing epidemiological and social transitions. The Research Hub (MADIVA) aims to address MM by developing data science solutions informed by stakeholder engagement.

Participants as Partners in Decentralized Clinical Trials.

AbstractDecentralized clinical trials (DCTs), meaning those trials in which activities are conducted outside of hospital or clinic settings, offer many advantages over traditional clinical trials and, when properly operationalized, provide unique benefits to researchers, participants, and society. The ability of individuals to participate remotely from their homes adds convenience, but, more importantly, democratizes access to trials that would otherwise be limited by geographic constraints. As personal interactions with participants are diminished in DCTs, trialists must be intentional when strategizing recruitment, retention, and return of value for participants.

Supporting rapid innovation in research data capture and management: the REDCap external module framework.

Objectives: Establishing a robust and secure framework allowing creation and sharing of custom features within the REDCap electronic data capture platform.

Materials And Methods: In partnership with REDCap Consortium members, we developed a framework for creating external modules enabling project-specific REDCap custom functionality (EM Framework). The EM Framework includes guidance and standard processes for developers to ensure basic functionality, compatibility, and security across REDCap instances.

Standards and infrastructure for multisite deployment of the research participant perception survey.

Objectives: To develop and disseminate a technical framework for administering the Research Participant Perception Survey (RPPS) and aggregating data across institutions using REDCap.

Materials And Methods: Six RPPS Steering Committee (RSC) member institutions met bi-weekly to achieve consensus on survey sampling techniques, data standards, participant and study descriptor variables, and dashboard design.

Results: RSC members implemented the infrastructure to send the RPPS to participants and shared data to the Empowering the Participant Voice Consortium Database.

Environment scan of generative AI infrastructure for clinical and translational science.

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers.

Consortium-driven rapid software validation for Research Electronic Data Capture (REDCap).

There is a growing trend for studies run by academic and nonprofit organizations to have regulatory submission requirements. As a result, there is greater reliance on REDCap, an electronic data capture (EDC) widely used by researchers in these organizations. This paper discusses the development and implementation of the Rapid Validation Process (RVP) developed by the REDCap Consortium, aimed at enhancing regulatory compliance and operational efficiency in response to the dynamic demands of modern clinical research.

Expert advice from ResearchMatch volunteers: Recruitment Innovation Center use cases and innovation.

Involving participants in the design of clinical trials should improve the overall success of a study. For this to occur, streamlined mechanisms are needed to connect the populations potentially impacted by a given study or health topic with research teams in order to inform trial design in a meaningful and timely manner. To address this need, we developed an innovative mechanism called the "ResearchMatch Expert Advice Tool" that quickly obtains volunteer perspectives from populations with specific health conditions or lived experiences using the national recruitment registry, ResearchMatch.

Data from the All of Us research program reinforces existence of activity inequality.

Large-scale and detailed analyses of activity in the United States (US) remain limited. In this work, we leveraged the comprehensive wearable, demographic, and survey data from the All of Us Research Program, the largest and most diverse population health study in the US to date, to apply and extend the previous global findings on activity inequality within the context of the US. We found that daily steps differed by sex at birth, age, body characteristics, geography, and built environment.

Informatics innovation to provide return of value to participant communities in the All of Us Research Program.

Objectives: The All of Us Research Program harnesses advances in technology, science, and engagement for precision medicine research. We describe informatics innovations which support that goal and return value to the participant cohort and community.

Materials And Methods: Research data from the All of Us Research Program are available to authorized users on the All of Us Researcher Workbench.

Leveraging artificial intelligence to summarize abstracts in lay language for increasing research accessibility and transparency.

Objective: Returning aggregate study results is an important ethical responsibility to promote trust and inform decision making, but the practice of providing results to a lay audience is not widely adopted. Barriers include significant cost and time required to develop lay summaries and scarce infrastructure necessary for returning them to the public. Our study aims to generate, evaluate, and implement ChatGPT 4 lay summaries of scientific abstracts on a national clinical study recruitment platform, ResearchMatch, to facilitate timely and cost-effective return of study results at scale.

Selecting trial centers using a standardized, automated site assessment survey instrument (SASI).

Background: To improve the site selection process for clinical trials, we expanded a site survey to include standardized assessments of site commitment time, team experience, feasibility of tight timelines, and local medical community equipoise as factors that might better predict performance. We also collected contact information about institutional research services ahead of site onboarding.

Aim: As a first step, we wanted to confirm that an expanded survey could be feasible and generalizable-that asking site teams for more details upfront was acceptable and that the survey could be completed in a reasonable amount of time, despite the assessment length.

Impact of financial compensation on enrollment and participation in a remote, mobile-app based research study.

Background: There is no consensus on how to determine appropriate financial compensation for research recruitment. Selecting incentive amounts that are reasonable and respectful, without undue inducement, remains challenging. Previously, we demonstrated that incentive amount significantly impacts participants' willingness to complete various hypothetical research activities.

Illuminating the landscape of high-level clinical trial opportunities in the All of Us Research Program.

Objective: With its size and diversity, the All of Us Research Program has the potential to power and improve representation in clinical trials through ancillary studies like Nutrition for Precision Health. We sought to characterize high-level trial opportunities for the diverse participants and sponsors of future trial investment.

Materials And Methods: We matched All of Us participants with available trials on ClinicalTrials.

Classification of Breast Implant Malposition.

Background: Implant malposition is a well-recognized complication of prosthetic breast implants. However, to date, no objective classification system has been described.

Objectives: The aim of this study was to perform a prospective trial of an objective and reproducible classification system for implant malposition formulated by analyzing retrospective data from a large cohort of patients with implant malposition.

Physical Activity and Incident Obesity Across the Spectrum of Genetic Risk for Obesity.

Importance: Despite consistent public health recommendations, obesity rates in the US continue to increase. Physical activity recommendations do not account for individual genetic variability, increasing risk of obesity.

Objective: To use activity, clinical, and genetic data from the All of Us Research Program (AoURP) to explore the association of genetic risk of higher body mass index (BMI) with the level of physical activity needed to reduce incident obesity.

Development, implementation, and dissemination of operational innovations across the trial innovation network.

Improving the quality and conduct of multi-center clinical trials is essential to the generation of generalizable knowledge about the safety and efficacy of healthcare treatments. Despite significant effort and expense, many clinical trials are unsuccessful. The National Center for Advancing Translational Science launched the Trial Innovation Network to address critical roadblocks in multi-center trials by leveraging existing infrastructure and developing operational innovations.

ResearchMatch on FHIR: Development and evaluation of a recruitment registry and electronic health record system interface for volunteer profile completion.

Background: Obtaining complete and accurate information in recruitment registries is essential for matching potential participants to research studies for which they qualify. Since electronic health record (EHR) systems are required to make patient data available to external systems, an interface between EHRs and recruitment registries may improve accuracy and completeness of volunteers' profiles. We tested this hypothesis on ResearchMatch (RM), a disease- and institution-neutral recruitment registry with 1357 studies across 255 institutions.

Leveraging the Expertise of the CTSA Program to Increase the Impact and Efficiency of Clinical Trials.

Importance: Multicenter clinical trials play a critical role in the translational processes that enable new treatments to reach all people and improve public health. However, conducting multicenter randomized clinical trials (mRCT) presents challenges. The Trial Innovation Network (TIN), established in 2016 to partner with the Clinical and Translational Science Award (CTSA) Consortium of academic medical institutions in the implementation of mRCTs, consists of 3 Trial Innovation Centers (TICs) and 1 Recruitment Innovation Center (RIC).