Clinical research is critical for healthcare advancement, but participant recruitment remains challenging. Clinical research professionals (CRPs; e.g., clinical research coordinator, research assistant) perform eligibility prescreening, ensuring adherence to study criteria while upholding scientific and ethical standards. This study investigates the key information CRP prioritizes during eligibility prescreening, providing insights to optimize data standardization, and recruitment approaches.

We conducted a freelisting survey targeting 150 CRPs from diverse domains (i.e., neurological disorders, rare diseases, and other diseases) where they listed essential information they look for from medical records, participant/caregiver inquiries, and discussions with principal investigators to determine a potential participant’s research eligibility. We calculated the salience scores of listed items using Anthropac, followed by a two-level analytic procedure to classify and thematically categorize the data.

The majority of participants were female (81%), identified as White (44%) and as non-Hispanic (64.5%). The first-level analysis universally emphasized age, medication list, and medical history across all domains. The second-level analysis illuminated domain-specific approaches in information retrieval: for instance, history of present illness was notably significant in neurological disorders during participant and principal investigator inquiries, while research participation was distinctly salient in potential participant inquiries within the rare disease domain.

This study unveils the intricacies of eligibility prescreening, with both universal and domain-specific methods observed. Variations in data use across domains suggest the need for tailored prescreening in clinical research. Incorporating these insights into CRP training and refining prescreening tools, combined with an ethical, participant-focused approach, can advance eligibility prescreening practices.

Integrating social and environmental determinants of health (SEDoH) into enterprise-wide clinical workflows and decision-making is one of the most important and challenging aspects of improving health equity. We engaged domain experts to develop a SEDoH informatics maturity model (SIMM) to help guide organizations to address technical, operational, and policy gaps.

We established a core expert group consisting of developers, informaticists, and subject matter experts to identify different SIMM domains and define maturity levels. The candidate model (v0.9) was evaluated by 15 informaticists at a Center for Data to Health community meeting. After incorporating feedback, a second evaluation round for v1.0 collected feedback and self-assessments from 35 respondents from the National COVID Cohort Collaborative, the Center for Leading Innovation and Collaboration’s Informatics Enterprise Committee, and a publicly available online self-assessment tool.

We developed a SIMM comprising seven maturity levels across five domains: data collection policies, data collection methods and technologies, technology platforms for analysis and visualization, analytics capacity, and operational and strategic impact. The evaluation demonstrated relatively high maturity in analytics and technological capacity, but more moderate maturity in operational and strategic impact among academic medical centers. Changes made to the tool in between rounds improved its ability to discriminate between intermediate maturity levels.

The SIMM can help organizations identify current gaps and next steps in improving SEDoH informatics. Improving the collection and use of SEDoH data is one important component of addressing health inequities.

During the COVID-19 pandemic, research organizations accelerated adoption of technologies that enable remote participation. Now, there’s a pressing need to evaluate current decentralization practices and develop appropriate research, education, and operations infrastructure. The purpose of this study was to examine current adoption of decentralization technologies in a sample of clinical research studies conducted by academic research organizations (AROs).

The setting was three data coordinating centers in the U.S. These centers initiated coordination of 44 clinical research studies during or after 2020, with national recruitment and enrollment, and entailing coordination between one and one hundred sites. We determined the decentralization technologies used in these studies.

We obtained data for 44/44 (100%) trials coordinated by the three centers. Three technologies have been adopted across nearly all studies (98–100%): eIRB, eSource, and Clinical Trial Management Systems. Commonly used technologies included e-Signature (32/44, 73%), Online Payments Portals (26/44, 59%), ePROs (23/44, 53%), Interactive Response Technology (22/44, 50%), Telemedicine (19/44, 43%), and eConsent (18/44, 41%). Wearables (7/44,16%) and Online Recruitment Portals (5/44,11%) were less common. Rarely utilized technologies included Direct-to-Patient Portals (1/44, 2%) and Home Health Nurse Portals (1/44, 2%).

All studies incorporated some type of decentralization technology, with more extensive adoption than found in previous research. However, adoption may be strongly influenced by institution-specific IT and informatics infrastructure and support. There are inherent needs, responsibilities, and challenges when incorporating decentralization technology into a research study, and AROs must ensure that infrastructure and informatics staff are adequate.

Randomized clinical trials (RCT) are the foundation for medical advances, but participant recruitment remains a persistent barrier to their success. This retrospective data analysis aims to (1) identify clinical trial features associated with successful participant recruitment measured by accrual percentage and (2) compare the characteristics of the RCTs by assessing the most and least successful recruitment, which are indicated by varying thresholds of accrual percentage such as ≥ 90% vs ≤ 10%, ≥ 80% vs ≤ 20%, and ≥ 70% vs ≤ 30%.

Data from the internal research registry at Columbia University Irving Medical Center and Aggregated Analysis of ClinicalTrials.gov were collected for 393 randomized interventional treatment studies closed to further enrollment. We compared two regularized linear regression and six tree-based machine learning models for accrual percentage (i.e., reported accrual to date divided by the target accrual) prediction. The outperforming model and Tree SHapley Additive exPlanations were used for feature importance analysis for participant recruitment. The identified features were compared between the two subgroups.

CatBoost regressor outperformed the others. Key features positively associated with recruitment success, as measured by accrual percentage, include government funding and compensation. Meanwhile, cancer research and non-conventional recruitment methods (e.g., websites) are negatively associated with recruitment success. Statistically significant subgroup differences (corrected p-value < .05) were found in 15 of the top 30 most important features.

This multi-source retrospective study highlighted key features influencing RCT participant recruitment, offering actionable steps for improvement, including flexible recruitment infrastructure and appropriate participant compensation.

It is important for SARS-CoV-2 vaccine providers, vaccine recipients, and those not yet vaccinated to be well informed about vaccine side effects. We sought to estimate the risk of post-vaccination venous thromboembolism (VTE) to meet this need.

We conducted a retrospective cohort study to quantify excess VTE risk associated with SARS-CoV-2 vaccination in US veterans age 45 and older using data from the Department of Veterans Affairs (VA) National Surveillance Tool. The vaccinated cohort received at least one dose of a SARS-CoV-2 vaccine at least 60 days prior to 3/06/22 (N = 855,686). The control group was those not vaccinated (N = 321,676). All patients were COVID-19 tested at least once before vaccination with a negative test. The main outcome was VTE documented by ICD10-CM codes.

Vaccinated persons had a VTE rate of 1.3755 (CI: 1.3752–1.3758) per thousand, which was 0.1 percent over the baseline rate of 1.3741 (CI: 1.3738–1.3744) per thousand in the unvaccinated patients, or 1.4 excess cases per 1,000,000. All vaccine types showed a minimal increased rate of VTE (rate of VTE per 1000 was 1.3761 (CI: 1.3754–1.3768) for Janssen; 1.3757 (CI: 1.3754–1.3761) for Pfizer, and for Moderna, the rate was 1.3757 (CI: 1.3748–1.3877)). The tiny differences in rates comparing either Janssen or Pfizer vaccine to Moderna were statistically significant (p < 0.001). Adjusting for age, sex, BMI, 2-year Elixhauser score, and race, the vaccinated group had a minimally higher relative risk of VTE as compared to controls (1.0009927 CI: 1.007673–1.0012181; p < 0.001).

The results provide reassurance that there is only a trivial increased risk of VTE with the current US SARS-CoV-2 vaccines used in veterans older than age 45. This risk is significantly less than VTE risk among hospitalized COVID-19 patients. The risk-benefit ratio favors vaccination, given the VTE rate, mortality, and morbidity associated with COVID-19 infection.

To identify the informatics educational needs of clinical and translational research professionals whose primary focus is not informatics.

Informatics and data science skills are essential for the full spectrum of translational research, and an increased understanding of informatics issues on the part of translational researchers can alleviate the demand for informaticians and enable more productive collaborations when informaticians are involved. Identifying the level of interest in different topics among various types of of translational researchers will help set priorities for development and dissemination of informatics education.

We surveyed clinical and translational science researchers in Clinical and Translational Science Award (CTSA) programs about their educational needs and preferences.

Researchers from 23 out of the 62 CTSA hubs responded to the survey. 67% of respondents across roles and topics expressed interest in learning about informatics topics. There was high interest in all 30 topics included in the survey, with some variation in interest depending on the role of the respondents.

Our data support the need to advance training in clinical and biomedical informatics. As the complexity and use of information technology and data science in research studies grows, informaticians will continue to be a limited resource for research collaboration, education, and training. An increased understanding of informatics issues across translational research teams can alleviate this burden and allow for more productive collaborations. To inform a roadmap for informatics education for research professionals, we suggest strategies to use the results of this needs assessment to develop future informatics education.

A national electronic health record is being procured for Health Service Executive hospitals in Ireland. A number of hospitals have implemented an electronic document management system. This study aimed to investigate the efficiency and safety of the electronic document management system in our centre.

A retrospective audit was performed of patients operated on at Galway University Hospital. The availability and location of patients’ admission data on the electronic document management system were recorded. These data were analysed using Microsoft Excel software, version 16.45.

The records of 100 patients were analysed. The main findings were: 5 per cent of operation notes were missing, 80 per cent were in the incorrect section, while 15 per cent were in the correct ‘procedure’ section on the electronic document management system.

This study shows there is potential for error with ‘paper-light’ solutions, whereby delayed scanning, misfiling of scanned records and missing records may lead to significant delays in treatment and potential patient safety issues.

COVID-19 is a major health threat around the world causing hundreds of millions of infections and millions of deaths. There is a pressing global need for effective therapies. We hypothesized that leukotriene inhibitors (LTIs), that have been shown to lower IL6 and IL8 levels, may have a protective effect in patients with COVID-19.

In this retrospective controlled cohort study, we compared death rates in COVID-19 patients who were taking a LTI with those who were not taking an LTI. We used the Department of Veterans Affairs (VA) Corporate Data Warehouse (CDW) to create a cohort of COVID-19-positive patients and tracked their use of LTIs between November 1, 2019 and November 11, 2021.

Of the 1,677,595 cohort of patients tested for COVID-19, 189,195 patients tested positive for COVID-19. Forty thousand seven hundred one were admitted. 38,184 had an oxygen requirement and 1214 were taking an LTI. The use of dexamethasone plus a LTI in hospital showed a survival advantage of 13.5% (CI: 0.23%–26.7%; p < 0.01) in patients presenting with a minimal O2Sat of 50% or less. For patients with an O2Sat of <60 and <50% if they were on LTIs as outpatients, continuing the LTI led to a 14.4% and 22.25 survival advantage if they were continued on the medication as inpatients.

When combined dexamethasone and LTIs provided a mortality benefit in COVID-19 patients presenting with an O2 saturations <50%. The LTI cohort had lower markers of inflammation and cytokine storm.

Despite significant advancements in healthcare technology, digital health solutions – especially those for serious mental illnesses – continue to fall short of their potential across both clinical practice and efficacy. The utility and impact of medicine, including digital medicine, hinges on relationships, trust, and engagement, particularly in the field of mental health. This paper details results from Phase 1 of a two-part study that seeks to engage people with schizophrenia, their family members, and clinicians in co-designing a digital mental health platform for use across different cultures and contexts in the United States and India.

Each site interviewed a mix of clinicians, patients, and their family members in focus groups (n = 20) of two to six participants. Open-ended questions and discussions inquired about their own smartphone use and, after a demonstration of the mindLAMP platform, specific feedback on the app's utility, design, and functionality.

Our results based on thematic analysis indicate three common themes: increased use and interest in technology during coronavirus disease 2019 (COVID-19), concerns over how data are used and shared, and a desire for concurrent human interaction to support app engagement.

People with schizophrenia, their family members, and clinicians are open to integrating technology into treatment to better understand their condition and help inform treatment. However, app engagement is dependent on technology that is complementary – not substitutive – of therapeutic care from a clinician.