A Checklist For Building a Scalable Content Architecture in Life Sciences

A pharmaceutical company aimed to launch a Phase III clinical trial for a new oncology drug, focusing on efficient and effective patient recruitment across diverse regions. They evaluated patient populations by analyzing demographics, social determinants of health, and geography. Investigator profiling included practice details, affiliations, referral networks, and clinical trial experience. The final step involved selecting and extracting target lists for implementation.

This white paper presents findings from the PurpleLab Migraine Study, which leverages real-world data to analyze migraine prevalence, treatment variations, and provider insights. The study examines data from approximately 10 million Americans diagnosed with migraines, focusing on patient demographics, treatment pathways, and healthcare provider specialties. Key findings include the high prevalence of migraines among females aged 18-54, racial and ethnic disparities in treatment, and the significant role of specialized providers like psychiatrists and neurologists in managing migraine care. It highlights the importance of tailored treatment approaches and the potential benefits of complementary and alternative medicine therapies.

Clinical trials are crucial for drug development but often face delays due to poor site selection, which can lead to protocol violations, poor data quality, and increased costs. Ninety percent of clinical trials fail to meet their timelines, and issues like inadequate staffing and patient recruitment are significant challenges. Traditional site selection methods are inefficient, and the COVID-19 pandemic has further complicated the process. Data-driven insights can optimize site selection by evaluating current data on patient populations, investigators, and site capabilities.

Modern clinical trials can be an overwhelming experience for participants, compromising everything from medication adherence and data quality to overall study outcomes. AiCure’s H.Code is a patient engagement platform that reinvents the clinical trial experience for participants using cutting-edge AI, easy communication, and dedicated support for sites. Read our fact sheet to learn how H.Code extends the reach of your clinical team using an AI-powered analytics platform and a user-friendly smartphone app.

Modern clinical trials are more complex than ever, and one significant reason is the increased focus on patient engagement. Incorporating a patient engagement platform into your clinical trial enhances the patient experience and can lead to more successful trials with stronger, more reliable outcomes. We put together this helpful checklist of key features to look out for when choosing a platform for your study.

Patient attrition can be a significant barrier to the success of a randomized controlled trial (RCT). Today, with the help of AI-powered predictive engagement tools, clinical study managers are finding ways to proactively reduce attrition rates in RCTs, and increase the effectiveness of their trial. In this guide, we look at the role AI-powered patient engagement tools play in clinical research, from the problems they’re being used to solve to the areas and indications in which they’re being deployed.