Nearly 800,000 people in the United States each year are affected by stroke, which remains the leading cause of adult disability and 5th leading cause of death. Several proven medical and surgical treatments exist for acute stroke. However, diagnostic error and resultant treatment delays contribute to missed opportunities to reduce death and disability in acute stroke patients. Indiscriminate neurologic consultation and over-testing in the emergency department (ED) will stress and deplete currently available staff and resources and are not practical solutions. Furthermore, incomplete problem analysis and risk assessments can lead to unintended consequences of an intervention. Although recommendations and pathways exist for stroke evaluation and management, none has been systematically engineered, designed, or tested to identify and effectively address system failures especially in diagnostic error. The Targeted Healthcare Engineering for Systems Interventions in Stroke (THESIS) study will bring together dynamic learning laboratories consisting of stroke neurology, emergency medicine, informatics, engineering, and health services experts at 3 large, diverse healthcare systems in Chicago with experience in the application of systems engineering, problem analysis, design and development, implementation, and evaluation methods to identify and test solutions to reduce diagnostic error and resultant delays to evidence-based treatments for acute stroke patients in the ED.
Acute stroke is a major cause of death and disability in the US and timely acute stroke treatments can be life- saving and reduce disability. However, a timely and accurate diagnosis is needed before any treatment can be delivered. This study will use a Learning Collaborative approach across three, large, Chicago healthcare systems, as a Patient Safety Learning Laboratory, and use systems engineering methods to identify and understand the underlying causes; user-centered design, design of experiments and clinical simulations to design and test solutions; implementation science principles and a hybrid implementation-effectiveness evaluation to reduce acute stroke diagnostic error.
