Diagnostic errors, including missed or delayed diagnosis of vascular events, account for the single largest source of medical harm and death to patients each year. A spotlight is being shone on the opportunity to use advancing technologies like artificial intelligence (AI) to guide informed clinical decision making that will significantly decrease the incidence of missed diagnosis. Recent evidence supports that patients experiencing vascular emergencies like stroke make up some of the most dangerous errors, contributing up to 17.5% of the serious harm rate calculated among dangerous disease cases. This can likely be attributed to the complex presentation of stroke patients, the critical thinking skills needed to properly assess and treat the patients and underutilization of advancing technology like AI to effectively care for this population.
Current research supports posterior strokes make up 20% of all ischemic strokes and include a staggering 20-60% miss rate. The hallmark signs and symptoms that accompany anterior ischemic strokes are not always present in posterior strokes and are commonly overlooked or dismissed during assessments by inexperienced caregivers. For example, younger patients presenting with ‘dizziness’ may often be attributed to vertigo as opposed to an acute stroke. Likewise, severity is often underestimated due to variable and non-specific symptomology and localization.
Additionally, the incidence of patients presenting to emergency rooms with neurologic issues has increased in recent years, while hospitals presently face staffing shortages and challenges associated with deploying experienced caregiver teams for medical emergencies like strokes. Evidence suggesting inadequate education of stroke warning signs and low awareness of appropriate stroke patient management contribute to the likelihood of misdiagnosis. It is not surprising that ineffective education toward complex neurovascular patients improperly prepares caregivers to apply their knowledge and skills to clinical practice. Supporting evidence shows criftical thinking is currently considered weak among inexperienced medical staff and emphasizes the importance of utilizing available technology to address this gap by enhancing clinical practice.
Imaging remains central to the diagnosis and management of all stroke types. Predictably, an increase in the probability of correct and timely diagnosis should produce a substantial reduction in the morbidity and mortality associated with diagnostic error and preventable harm. Although quality improvement initiatives focusing on prompt diagnosis and discovering preventability impact are on the rise, clinical support technology like AI offers a beacon of hope to support better informed critical decisions and reduce costly errors. AI-enabled technologies such as algorithms that alert to critical findings, aid in stroke diagnosis and management and coordinate patient care improve both accuracy and efficiency of clinical care decisions.
The evidence supporting the use of AI tools in clinical practice to reduce the gaps in treatment and decision making is clear. Using enhanced technology in conjunction with adequate education and developing better clinical assessment skills will properly prepare medical staff to develop the critical thinking needed to care for these vulnerable patient populations.
As organizations work to adopt these technologies into practice, thoughtful consideration must be made to ensure adequate adoption and utilization amongst the care teams. Integrating AI-enabled technology and software solutions into workflows with effectively trained clinical staff can both improve speed and increase diagnostic accuracy of these burdensome and harmful diseases.
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