Despite the recognition that diagnostic errors and delays are a major contributor to preventable deaths in the USA, little progress has been made to reduce mortality outcomes from this known killer. Although there is a considerable body of literature outlining contributing causes, an effective strategy leading to a meaningful reduction in diagnostic error and delay rates has not made its way into practice. This has, at least in part, been due to ineffective implementation that has focused on the healthcare team's role and has failed to incorporate the complexity of the organizational and systems processes within the clinical environment. This proposal is unique and novel and combines mixed-methods research approaches with systems engineering research approaches to understand the interplay of the multiple factors contributing to diagnostic error and delay. The knowledge gained from this holistic approach will then be used within the learning laboratory to inform the design, development, evaluation, and refinement of the solutions to diagnostic error and delay. ?Control Tower? will be the staging ground for the in situ learning laboratory and will be built on top of a well-established clinical informatics infrastructure and hospital environment open to innovation and practice change. Specifically, using this innovative approach we will evaluate the effectiveness of learning laboratory interventions on the rate of diagnostic error and delay in patients with emerging critical illness. The interventions developed through ?Control Tower? have the potential to be shared with multiple practices and adapted to a variety of clinical environments.
Diagnostic error and delay remain a leading cause of preventable harm and death in the United States. Using a learning laboratory structure, we will implement mixed-methods research approaches to identify the systemic weaknesses that contribute to diagnostic error and delay in the hospital setting. The knowledge gained from our innovative research will allow us to design, develop, implement, and refined a suite of human-centered tools that can be deployed to reduce the risk of diagnostic error and delay in both community and academic hospital settings.
