Enhancing study recruitment through implementation of an opt-out, cold contact process with consideration for autonomy, beneficence and justice.

The potential utilization of a cold-contact approach to research recruitment, where members of the research team are unknown to the patient, has grown with the expanded use of electronic health records (EHRs) and affiliated patient portals. Institutions that permit this strategy vary in their implementation and management of it but tend to lean towards more conservative approaches. This process paper describes the Medical University of South Carolina's transition to an opt-out model of "cold-contact" recruitment (known as patient outreach recruitment or POR), wherein patients can be contacted so long as they do not express an unwillingness to receive such communication. The work highlights the benefits of this model by explaining how it, in many ways, supports and protects autonomy, beneficence, and justice for patients. The paper then describes the process of standing up the recruitment strategy, communicating the change to patients and the community, and documenting study team contact and patient research preference. Data supporting increased access to potentially eligible patients of greater diversity as well as initial researcher feedback on perceived success of POR is also shared. The paper ends with a discussion of next steps to enhance the POR process via more detailed data collection and reengagement with community stakeholders.

Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models.

BACKGROUND: To advance new therapies into clinical care, clinical trials must recruit enough participants. Yet, many trials fail to do so, leading to delays, early trial termination, and wasted resources. Under-enrolling trials make it impossible to draw conclusions about the efficacy of new therapies. An oft-cited reason for insufficient enrollment is lack of study team and provider awareness about patient eligibility. Automating clinical trial eligibility surveillance and study team and provider notification could offer a solution. METHODS: To address this need for an automated solution, we conducted an observational pilot study of our TAES (TriAl Eligibility Surveillance) system. We tested the hypothesis that an automated system based on natural language processing and machine learning algorithms could detect patients eligible for specific clinical trials by linking the information extracted from trial descriptions to the corresponding clinical information in the electronic health record (EHR). To evaluate the TAES information extraction and matching prototype (i.e., TAES prototype), we selected five open cardiovascular and cancer trials at the Medical University of South Carolina and created a new reference standard of 21,974 clinical text notes from a random selection of 400 patients (including at least 100 enrolled in the selected trials), with a small subset of 20 notes annotated in detail. We also developed a simple web interface for a new database that stores all trial eligibility criteria, corresponding clinical information, and trial-patient match characteristics using the Observational Medical Outcomes Partnership (OMOP) common data model. Finally, we investigated options for integrating an automated clinical trial eligibility system into the EHR and for notifying health care providers promptly of potential patient eligibility without interrupting their clinical workflow. RESULTS: Although the rapidly implemented TAES prototype achieved only moderate accuracy (recall up to 0.778; precision up to 1.000), it enabled us to assess options for integrating an automated system successfully into the clinical workflow at a healthcare system. CONCLUSIONS: Once optimized, the TAES system could exponentially enhance identification of patients potentially eligible for clinical trials, while simultaneously decreasing the burden on research teams of manual EHR review. Through timely notifications, it could also raise physician awareness of patient eligibility for clinical trials.

Communicating and disseminating research findings to study participants: Formative assessment of participant and researcher expectations and preferences.

INTRODUCTION: Translating research findings into practice requires understanding how to meet communication and dissemination needs and preferences of intended audiences including past research participants (PSPs) who want, but seldom receive, information on research findings during or after participating in research studies. Most researchers want to let others, including PSP, know about their findings but lack knowledge about how to effectively communicate findings to a lay audience. METHODS: We designed a two-phase, mixed methods pilot study to understand experiences, expectations, concerns, preferences, and capacities of researchers and PSP in two age groups (adolescents/young adults (AYA) or older adults) and to test communication prototypes for sharing, receiving, and using information on research study findings. PRINCIPAL RESULTS: PSP and researchers agreed that sharing study findings should happen and that doing so could improve participant recruitment and enrollment, use of research findings to improve health and health-care delivery, and build community support for research. Some differences and similarities in communication preferences and message format were identified between PSP groups, reinforcing the best practice of customizing communication channel and messaging. Researchers wanted specific training and/or time and resources to help them prepare messages in formats to meet PSP needs and preferences but were unaware of resources to help them do so. CONCLUSIONS: Our findings offer insight into how to engage both PSP and researchers in the design and use of strategies to share research findings and highlight the need to develop services and support for researchers as they aim to bridge this translational barrier.