Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

 This is so fucking simple, pick one of these much faster stroke diagnosis tools and get the subjective views of neurologists out of the picture. Reduce misdiagnosis substantially. Is your stroke hospital smart enough to do this? Or will incompetence reign once again? If so, the board of directors needs to be fired.  

• diagnosis failure (27) 

  • young adult strokes (54)

   

 

Maybe one of these much faster possibilities?

Hats off to Helmet of Hope - stroke diagnosis in 30 seconds   February 2017

 

Microwave Imaging for Brain Stroke Detection and Monitoring using High Performance Computing in 94 seconds March 2017

 

New Device Quickly Assesses Brain Bleeding in Head Injuries - 5-10 minutes April 2017

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Using artificial intelligence for improving stroke diagnosis in emergency departments: a practical framework

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Vida Abedi, Ayesha Khan, Durgesh Chaudhary, ...

First Published August 25, 2020 Review Article 

https://doi.org/10.1177/1756286420938962

Article information

   

Abstract

Stroke is the fifth leading cause of death in the United States and a major cause of severe disability worldwide. Yet, recognizing the signs of stroke in an acute setting is still challenging and leads to loss of opportunity to intervene, given the narrow therapeutic window. A decision support system using artificial intelligence (AI) and clinical data from electronic health records combined with patients’ presenting symptoms can be designed to support emergency department providers in stroke diagnosis and subsequently reduce the treatment delay. In this article, we present a practical framework to develop a decision support system using AI by reflecting on the various stages, which could eventually improve patient care and outcome. We also discuss the technical, operational, and ethical challenges of the process.

Keywords acute stroke, artificial intelligence, cerebrovascular disease/stroke, computer aided diagnosis, ischemic stroke, machine learning, stroke diagnosis, stroke in emergency department

Introduction

Stroke is the fifth leading cause of death in the United States and a significant cause of severe disability in adults.¹ Each year, around 800,000 Americans experience a new or recurrent stroke.² Rapid diagnosis and treatment of stroke is crucial and leads to improved outcomes and prognosis among patients treated within the ‘Golden Hour’.^3,4

However, strokes, especially posterior circulation strokes, are associated with significant (>10%) diagnostic error.⁵ The latter could be due to (1) some patients with acute stroke present with non-focal symptoms such as dizziness, diplopia, dysarthria, or ataxia,⁶ which may not trigger a neurology consult or a need for a more detailed neurological examination; (2) stroke is commonly misdiagnosed in younger patients^7,8; and (3) the emergency department (ED) is a challenging environment for providers, especially with the multiplicity of care protocols, and the dynamic nature of patient care.^8,9 Triage, consultations, admissions, discharge, and other steps in emergency care are time-sensitive, complex, and always changing to further improve efficacy and quality of care. Therefore, identifying potential stroke symptoms can be challenging,^10–12 especially when the providers are in training.^13,14 Besides, the risk of misdiagnosis can be higher among walk-in patients,¹⁵ when the providers do not receive a pre-arrival notification from emergency medical services,¹⁶ or when a neurologist is not readily available for an urgent consultation.^17–19 Scoring systems for the diagnosis of stroke and recurrent stroke do not have a high sensitivity to diagnose the posterior circulation stroke.^20,21 Furthermore, these tools are also not automatic, and require that the physicians suspect stroke as a differential diagnosis to apply the scoring system.

Artificial intelligence (AI), a computational framework meaning to emulate human insight, is one of the most transformative technologies.^22,23 The era of augmented intelligence in healthcare is driven by the notion that intelligent algorithms can support providers in diagnosis, treatment, and outcome prediction, especially with growing digital and connected patient data and advances in computational abilities.^24–26 The augmented-diagnostic model for stroke may be particularly helpful in low volume or non-stroke centers’ ED, where emergency providers have limited daily exposure to stroke. An automated, computer-assisted screening tool that can be seamlessly integrated into clinical workflow to quickly analyze patient symptoms and clinical data and suggest a diagnosis of stroke (‘StrokeAlert’ pop-up) in an ED setting could be valuable. Such a system will also help bring access and timely diagnosis for patients who choose to self-present to an ED. In this paper, we present a practical framework and summarize the stages needed to create a machine learning (ML)-enabled clinical decision support system for the screening of stroke patients in ED using data from electronic health records (EHRs) combined with the patient’s presenting symptoms at the point of care. We have assembled a team of experts and are leading such effort at Geisinger. Figure 1 summarizes the key steps of such a system.

Figure1. Key steps for a stroke ML-enabled decision support system for EDs.

ED, emergency department; ML, machine learning.