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.

We developed an interface where volunteers signing up for RM can authorize transfer of demographic data, medical conditions, and medications from the EHR into a registration form. We obtained feedback from a panel of community members to determine acceptability of the planned integration. We then developed the EHR interface and performed an evaluation study of 100 patients to determine whether RM profiles generated with EHR-assisted adjudication included more conditions and medications than those without the EHR connection.

Community member feedback revealed that members of the public were willing to authenticate into the EHR from RM with proper messaging about choice and privacy. The evaluation study showed that out of 100 participants, 75 included more conditions and 69 included more medications in RM profiles completed with the EHR connection than those without. Participants also completed the EHR-connected profiles in 16 fewer seconds than non-EHR-connected profiles.

The EHR to RM integration could lead to more complete profiles, less participant burden, and better study matches for many of the over 148,000 volunteers who participate in ResearchMatch.

The process of identifying and connecting with clinical trial study teams can be challenging and difficult for members of the public. The national volunteer community registry, ResearchMatch, and the public clinical trials search tool, Trials Today, work in tandem to bridge this connection by providing a streamlined process for potential participants to identify clinical trials which may be of interest.

Building on the existing infrastructure of ResearchMatch and Trials Today, we created a mechanism by which the public can request that their basic contact information (e.g., email/phone) be securely shared with any actively recruiting clinical trial, including trials with no existing relationship with ResearchMatch.

Within the first 2 years of use (July 2019–July 2021), ResearchMatch Volunteers sent 12,251 requests to study teams. On average, 20% of these requests were accepted by the study teams.

The utilization of this tool indicates that there is active interest among members of the public to independently contact study teams about trials of interest. Additionally, research teams unaffiliated with ResearchMatch are willing to at minimum accept contact information. This allows ResearchMatch to successfully serve as a medium, connecting members of the public with actively recruiting trials.

Clinical trial participation among US Hispanics remains low, despite a significant effort by research institutions nationwide. ResearchMatch, a national online platform, has matched 113,372 individuals interested in participating in research with studies conducted by 8778 researchers. To increase accessibility to Spanish speakers, we translated the ResearchMatch platform into Spanish by implementing tenets of health literacy and respecting linguistic and cultural diversity across the US Hispanic population. We describe this multiphase process, preliminary results, and lessons learned.

Translation of the ResearchMatch site consisted of several activities including: (1) improving the English language site’s reading level, removing jargon, and using plain language; (2) obtaining a professional Spanish translation of the site and incorporating iterative revisions by a panel of bilingual community members from diverse Hispanic backgrounds; (3) technical development and launch; and (4) initial promotion.

The Spanish language version was launched in August 2018, after 11 months of development. Community input improved the initial translation, and early registration and use by researchers demonstrate the utility of Spanish ResearchMatch in engaging Hispanics. Over 12,500 volunteers in ResearchMatch self-identify as Hispanic (8.5%). From August 2018 to March 2020, 162 volunteers registered through the Spanish language version of ResearchMatch, and over 500 new and existing volunteers have registered a preference to receive messages about studies in Spanish.

By applying the principles of health literacy and cultural competence, we developed a Spanish language translation of ResearchMatch. Our multiphase approach to translation included key principles of community engagement that should prove informative to other multilingual web-based platforms.

Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs.

We trained six supervised machine learning classifiers (Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor Classifier (KNC), Adaboost Classifier (ABC) and a Random Forest Classifier (RFC)), as well as a deep learning method, Convolutional Neural Network (CNN), using a dataset of 841,377 instances and 20 features, including demographic data, geographic constraints, medical conditions and ResearchMatch visit history. Our outcome variable consisted of responses showing specific participant interest when presented with specific clinical trial opportunity invitations (‘yes’ or ‘no’). Furthermore, we created four subsets from this dataset based on top self-reported medical conditions and gender, which were separately analysed.

The deep learning model outperformed the machine learning classifiers, achieving an area under the curve (AUC) of 0.8105.

The results show sufficient evidence that there are meaningful correlations amongst predictor variables and outcome variable in the datasets analysed using the supervised machine learning classifiers. These approaches show promise in identifying individuals who may be more likely to participate when offered an opportunity for a clinical trial.