One study found preventable deaths from stroke are attributed to diagnostic error over 30 times more often than deaths from myocardial infarction. A better understanding of factors that predispose to stroke misdiagnosis could help spur interventions to reduce them*. For many providers, including those working in diagnostic radiology, these errors can potentially lead to costly and time-consuming litigation.
There are many examples of where the misdiagnosis and treatment of stroke have led to huge settlements for patients and their families. For example, the family of a 41-year old woman received $2.625 million as a settlement when the patient died as a result of medical malpractice and an undiagnosed intracranial hemorrhage (ICH)*.
In another New York medical malpractice case, a 56-year-old man was given $4 million when his provider failed to administer the medication known as tPA in a timely manner*. Lack of confidence in determining if an ICH is present represents a missed opportunity to decide upon the best course of treatment. One that can be incredibly costly to the patient and care provider alike.
AI-enabled solutions can address these gaps and help to reduce medical errors by augmenting the care provider to reach quicker and more accurate diagnoses of stroke, head trauma, and other life-threatening conditions, which could reduce the risk of litigation while improving patient outcomes.