Prostate cancer clinical trial completion: The role of geography

Applying implementation frameworks to the clinical trial context

BACKGROUND

Clinical trials advance science, benefit society, and provide optimal care to individuals with some conditions, such as cancer. However, clinical trials often fail to reach their endpoints, and low participant enrollment remains a critical problem with trial conduct. In these ways, clinical trials can be considered beneficial evidence-based practices suffering from poor implementation. Prior approaches to improving trials have had difficulties with reproducibility and limited impact, perhaps due to the lack of an underlying trial improvement framework. For these reasons, we propose adapting implementation science frameworks to the clinical trial context to improve the implementation of clinical trials.

MAIN TEXT

We adapted an outcomes framework (Proctor's Implementation Outcomes Framework) and a determinants framework (the Consolidated Framework for Implementation Research) to the trial context. We linked these frameworks to ERIC-based improvement strategies and present an inferential process model for identifying and selecting trial improvement strategies based on the Implementation Research Logic Model. We describe example applications of the framework components to the trial context and present a worked example of our model applied to a trial with poor enrollment. We then consider the implications of this approach on improving existing trials, the design of future trials, and assessing trial improvement interventions. Additionally, we consider the use of implementation science in the clinical trial context, and how clinical trials can be "test cases" for implementation research.

CONCLUSIONS

Clinical trials can be considered beneficial evidence-based interventions suffering from poor implementation. Adapting implementation science approaches to the clinical trial context can provide frameworks for contextual assessment, outcome measurement, targeted interventions, and a shared vocabulary for clinical trial improvement. Additionally, exploring implementation frameworks in the trial context can advance the science of implementation through both "test cases" and providing fertile ground for implementation intervention design and testing.

Envisioning clinical trials as complex interventions

Clinical trials are critical components of modern health care and infrastructure. Trials benefit society through scientific advancement and individual patients through trial participation. In fact, billions of dollars are spent annually in support of these benefits. Despite the massive investments, clinical trials often fail to accomplish their primary aims and trial enrollment rates remain low. Prior efforts to improve trial conduct and enrollment have had limited success, perhaps due to oversimplification of the complex, multilevel nature of trials. For these reasons, the authors propose applying implementation science to the clinical trials context. In this commentary, the authors posit clinical trials as complex, multilevel evidence-based interventions with significant societal and individual benefits yet with persistent gaps in implementation. An application of implementation science concepts to the clinical trials context as means to build common vocabulary and establish a platform for applying implementation science and practice to improve clinical trial conduct is introduced. Applying implementation science to the clinical trials context can augment improvement efforts and build capacity for better and more efficient evidence-based care for all patients and trial stakeholders throughout the clinical trials enterprise.

Evolving a national clinical trials learning health system

Clinical trials generate key evidence to inform decision making, and also benefit participants directly. However, clinical trials frequently fail, often struggle to enroll participants, and are expensive. Part of the problem with trial conduct may be the disconnected nature of clinical trials, preventing rapid data sharing, generation of insights and targeted improvement interventions, and identification of knowledge gaps. In other areas of healthcare, a learning health system (LHS) has been proposed as a model to facilitate continuous learning and improvement. We propose that an LHS approach could greatly benefit clinical trials, allowing for continuous improvements to trial conduct and efficiency. A robust trial data sharing system, continuous analysis of trial enrollment and other success metrics, and development of targeted trial improvement interventions are potentially key components of a Trials LHS reflecting the learning cycle and allowing for continuous trial improvement. Through the development and use of a Trials LHS, clinical trials could be treated as a system, producing benefits to patients, advancing care, and decreasing costs for stakeholders.