ISimchaTechnology Platform for Recruiting a Diverse Population of Older Adults into Clinical Trials

AbstractUnderrepresentation of racial/ethnic minorities in clinical trials has been a long-standing problem as African Americans and Hispanics make up less than 10% of participants in clinical trials despite being approximately 30% of the US populations. As a result, minority populations bear the burden of health disparities but do not usually benefit from recent research advancements. Barriers to racial and ethnic underrepresentation in clinical trials is complex, individualized, and requires a solution aimed at clinical trial readiness at the participant level. iSimcha, LLC, a minority owned small business in North Carolina, is developing a multitier platform to that addresses barriers to enrollment by individualizing each participant’s clinical trial journey by aligning resources, education, and support specifically where they are needed. The platform uniquely combines community based research and medical education, a clinical trial finder application and plain language medical research information automated through AI/ML tools. Our short term goal for the Phase 1 research is to determine the design features, acceptance and usability of the clinical trial finder application. Our long term goal is to demonstrate that success of our platform which uniquely combines community medical research education driven via social media engagement with the ability to find, understand and enroll in clinical trials. Phase 1 specific aims: Aim 1- Define user needs and interests through design thinking sessions The outcome of Aim 1 will be to identify features and technology to develop 3 unique personas for user testing as is standard practice for design thinking and mobile app user testing. Aim 2- Determine the usability based on user feedback and heuristic testing. Using the personas that were generated based on Aim 1, prototyping and feedback will be done by user interviews examining pain points, needs and mindset. Usability will be evaluated by system usability score via two iterative user testing sessions to achieve a minimum score of 68. Aim 3. Aim 3 – Evaluate and refine AI/ML programming for medical research lay language translation. After training the model with lay data sources, the initial model will be iterated to meet a criteria of least 75% of records are scored at a Flesch Reading Ease score of 70 or above