New and emerging technological capabilities for diagnosing, treating, and preventing disease promise significant advances in patient care. However, realizing the benefits of these achievements will require new methods for generating evidence that can inform more personalized care decisions.
While traditional clinical trials are a powerful tool, they tend to have significant limitations. For example, it can be difficult for traditional clinical trials to recruit patients that are representative of the diversity in the population; many patients are systematically left out of clinical trials due to comorbidities, the practical challenges of participating in complex studies far from home, and other hurdles. Traditional trials collect only certain information during a short time window in a patient’s care, missing much of the data that may be relevant to understanding both the patient’s health background and long-term measures of safety and effectiveness for novel interventions. These limitations – stemming from a ‘closed system’ approach to clinical studies – impact the utility and generalizability of clinical trial findings in the real-world setting, resulting in outcome variation and the inability to consistently and accurately predict individual patient outcomes prior to therapeutic intervention.