OBJECTIVES: This study aimed to investigate the bottleneck of recruiting diverse patients in clinical trials, explore innovative data-linking methods to enhance participant diversity and examine the root causes of missing data on patients' race and socioeconomic information.

METHODS: Patients aged 40 and older with cardiovascular diseases were recruited through social media. Tokenization was employed to link real-world data (RWD) with survey data.

RESULTS: A total of 334 patients from 46 states in the US completed the surveys. 91% of the study population's claims data was retrieved, enabling a comprehensive evaluation. The demographic breakdown of the participants was as follows: 134 females and 131 males, median age: 66, 85.7% Caucasian, 6.4% African American, 1.5% Latino, 1% Asian American, and 4.5% mixed-race. The median annual income among the participants was $27,000. Three patients died one year after the survey was conducted, one committed suicide, and two passed due to Covid.

CONCLUSIONS: This study demonstrates the feasibility of recruiting diverse patients, including older adults using decentralized clinical trials (DCT). Digital literacy and cultural differences should be considered when a digital device is used, and the questionnaire design should be patient-centered in addition to fully considering the patient's disease status, such as potential barriers to questionnaire completion posed by disease. Combining survey data with claims data offered a better understanding of patient journeys. It provided insights to optimize interventions for improved patient outcomes and identify barriers to the enrollment of racial and ethnic minorities. Furthermore, the study provided valuable information on comorbidities and mental health during the pandemic. The study also highlights the root causes of missing data regarding patients' race and socioeconomic information, emphasizing the need for comprehensive data collection. These findings underscore the importance of considering patient diversity, engaging communities, and employing robust data-linking techniques to optimize interventions and improve patient outcomes in cardiovascular diseases.