Diagnostic errors are increasingly recognized as a cause of pain, suffering and increased healthcare costs. Diagnostic delays are an important class of diagnostic errors. While many diagnostic errors occur in hospital settings, emergency departments visits may be especially important to consider because they treat critically ill patients and because most decisions to admit patients to the hospital are made in emergency departments. Thus, to enable a more complete understanding of diagnostic delays requires consideration of healthcare visits across a range of healthcare settings including clinic visits, emergency department visits and hospitalizations. Delays in diagnosing infectious diseases are important to consider. For contagious infectious diseases, diagnostic delays increase the risk of additional exposures, potentially generating more cases. Second, many infectious diseases can be effectively treated, but even short delays in treatment lead to worse clinical outcomes. However, with the exception of a few infectious diseases (e.g., tuberculosis), diagnostic delays for infectious diseases are understudied. Thus, there is a critical need to investigate the incidence, risk factors and clinical impact for diagnostic delays for infectious diseases. The overarching goal of our research is to investigate diagnostic delays associated with infectious diseases using existing data along with methods from the fields of computer science and statistics. While our research relies upon ?big data?, we will also use clinical experts to review and contribute to all of our results. Our subject matter experts incorporate expertise in infectious diseases, emergency medicine, acute care, medical education, diagnostic reasoning, healthcare epidemiology, public health, industry, and professional infectious disease societies. Specifically, we will 1) determine the incidence of diagnostic delays for a wide range of infectious diseases; 2) identify the risk factors associated with diagnostic delays for infectious diseases that are frequently delayed or have serious outcomes; and 3) estimate the impact of diagnostic delays in terms of healthcare costs and mortality. With our data, methods and clinical experts, we will be able to translate our results into future interventions designed to decrease diagnostic delays and improve healthcare outcomes. In addition, while our proposal focuses on infectious diseases, the methods and approaches that we will develop can be adopted to investigate non-infectious diseases and conditions.
Diagnostic delays for infectious diseases contribute to worse clinical outcomes, increased healthcare costs and, for some infectious diseases, outbreaks of great public health importance. We will use existing large data sets along with machine-learning techniques and expert clinical guidance to detect patterns of healthcare visits representing diagnostic delays. Our goal is to characterize the incidence, risk factors and clinical impact of diagnostic delays for a wide range of infectious diseases to inform future interventions.
