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Randomized clinical trials and large-scale, cohort studies continue to have a critical role in generating evidence in cardiovascular medicine; however, the increasing concern is that ballooning costs threaten the clinical trial enterprise. In this Perspectives article, we discuss the changing landscape of clinical research, and clinical trials in particular, focusing on reasons for the increasing costs and inefficiencies. These reasons include excessively complex design, overly restrictive inclusion and exclusion criteria, burdensome regulations, excessive source-data verification, and concerns about the effect of clinical research conduct on workflow. Thought leaders have called on the clinical research community to consider alternative, transformative business models, including those models that focus on simplicity and leveraging of digital resources. We present some examples of innovative approaches by which some investigators have successfully conducted large-scale, clinical trials at relatively low cost. These examples include randomized registry trials, cluster-randomized trials, adaptive trials, and trials that are fully embedded within digital clinical care or administrative platforms.

The objective of this study was to distinguish within a population of patients with known or suspected coronary artery disease groups at high and at low mortality rates. The study was based on Cleveland Clinic Foundation's dataset of 9454 patients, of whom 312 died during an observation period of 9 years. The Logical Analysis of Data method was adapted to handle the disproportioned size of the two groups of patients, and the inseparable character of this dataset – characteristic to many medical problems. As a result of the study, we have identified a high-risk group of patients representing 1/5 of the population, with a mortality rate 4 times higher than the average, and including 3/4 of the patients who died. The low-risk group identified in the study, representing approximately 4/5 of the population, had a mortality rate 3 times lower than the average. A Prognostic Index derived from the LAD model is shown to have a 83.95% correlation with the mortality rate of patients. The classification given by the Prognostic Index was also shown to agree in 3 out of 4 cases with that of the Cox Score, widely used by cardiologists, and to outperform it slightly, but consistently. An example of a highly reliable risk stratification system using both indicators is provided.