Complexity, Incidence, and Costs Related to Delayed Diagnosis of Venous Thromboembolism in Urban and Rural Primary and Urgent Care Settings

Blood clots, known as VTE, are a serious and sometimes deadly condition that includes deep vein thrombosis (DVT) and pulmonary embolism (PE). If not diagnosed and treated in time, VTE can lead to severe complications, prolonged hospital stays, and even death. However, diagnosing VTE is challenging, especially in primary and urgent care settings, because its symptoms can be vague and easily mistaken for other conditions. Research has shown that delays in VTE diagnosis are common, with some patients waiting days or even weeks before receiving the correct treatment. These delays are not only dangerous but also more likely to occur in certain subpopulations of patients. Despite the risks, healthcare systems lack standardized tools to measure and track VTE diagnostic errors, making it difficult to identify where improvements are needed.

This research aims to use electronic health records (EHR) and artificial intelligence to automate the detection of missed and delayed VTE diagnoses, identify factors contributing to these errors, and develop strategies to improve diagnosis and patient outcomes, ultimately reducing disparities and saving lives.

The specific aims of the research are as follows:

• Expand the Diagnostic Delay of Venous Thromboembolism (DOVE) electronic clinical quality measure (eCQM) evaluation to include delayed and missed VTE diagnostic errors in primary care and urgent care settings across two large heterogeneous and geographically distant health systems.
• Identify VTE diagnostic error-associated risk factors and develop recommendations to improve timely diagnosis.
• Identify the costs associated with VTE diagnostic errors and the cost benefit of timely VTE diagnosis.

The researchers plan to study the costs and impacts of delayed and missed VTE diagnoses by analyzing EHR data from two healthcare systems. They will develop prediction models using ML to estimate the extra costs associated with these diagnostic errors compared to cases where VTE is identified and treated on time. By focusing on both direct costs (like medical treatments and hospital stays) and total costs (which include administrative expenses), they hope to determine how much could be saved by improving VTE diagnosis. They will also assess whether using DOVE eCQM—a tool that tracks delays in VTE diagnosis using structured and unstructured EHR data—along with expert recommendations, can help healthcare providers diagnose VTE faster and more accurately, ultimately improving patient outcomes and reducing healthcare costs.

The researchers hope their work will reduce the number of dangerous delays and missed cases in primary and urgent care settings. By improving how VTE is identified, they aim to lower patient deaths, prevent serious complications, and make healthcare more efficient by cutting unnecessary costs. Their findings could help providers better understand the risks and challenges of diagnosing VTE, especially in different types of healthcare settings and among multiple patient groups. In the long run, this research could lead to new tools, guidelines, and policies that improve patient safety and ensure that more people receive timely and life-saving treatment.