Clinical Researcher

Artificial intelligence (AI) has fundamentally reshaped the clinical data industry. We routinely harness AI to generate deeper insights from complex trial data, accelerate detection of risk, and enhance oversight. Yet across the broader enterprise, many organizations have not embedded AI deeply enough into their internal workflows to unlock its full value.

For many early-career clinical researchers, trial design can feel rigid. A primary endpoint is selected, the study is powered around it, and success is determined by that single measure. But patients do not experience treatment effects through one outcome. They experience a combination of efficacy, side effects, symptoms, and quality of life. Reducing this to a single outcome can risk missing the bigger picture.

The need for diversity in clinical trials and the challenges of identifying patients who are unknown to the healthcare system complicate trial enrollment and add financial stress that may hinder the success of a study. A targeted, engineered approach to patient enrollment and diversity, using a financially calculable strategy, can ameliorate these challenges.

In this article, the authors explore how clinical research professionals can alter their approach to developing informed consent documents and use both existing resources and incremental change to better serve research participants. They also describe how a small team developed a toolkit composed of visual aids, study visit schedule templates, risk-communication graphics, language repositories, and supplementary guidance documents with the goal of improving informed consent documents and participant understanding.