scholarly journals Artificial intelligence MacHIne learning for the detection and treatment of atrial fibrillation guidelines in the emergency department setting (AIM HIGHER): Assessing a machine learning clinical decision support tool to detect and treat non‐valvular atrial fibrillation in the emergency department

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Kim Schwab ◽  
Dacloc Nguyen ◽  
GilAnthony Ungab ◽  
Gregory Feld ◽  
Alan S. Maisel ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Atrial Fibrillation ◽  
Emergency Department ◽  
Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Emergency Department Setting ◽  
Clinical Decision Support Tool

A Machine-Learning Clinical Decision Support Tool for Myocardial Infarction Diagnosis

2021 ◽  
Vol 28 ◽  
pp. S13
Author(s):  
Saarang Panchavati ◽  
Carson Lam ◽  
Anurag Garikipati ◽  
Nicole Zelin ◽  
Emily Pellegrini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

Retrospective evaluation of a clinical decision support tool for effective computed tomography angiography utilization in urgent brain imaging of suspected TIA/minor stroke in the emergency department

CJEM ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 343-351 ◽  
Author(s):  
Maximilian B. Bibok ◽  
Kristine Votova ◽  
Robert F. Balshaw ◽  
Melanie Penn ◽  
Mary L. Lesperance ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Emergency Department ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Computed Tomography Angiography ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Minor Stroke ◽  
Support Tool ◽  
Clinical Decision Support Tool

AbstractObjectivesThe Canadian Stroke Best Practice Recommendations suggests that patients suspected of transient ischemic attack (TIA)/minor stroke receive urgent brain imaging, preferably computed tomography angiography (CTA). Yet, high requisition rates for non-cerebrovascular patients overburden limited radiological resources, putting patients at risk. We hypothesize that our clinical decision support tool (CDST) developed for risk stratification of TIA in the emergency department (ED), and which incorporates Canadian guidelines, could improve CTA utilization.MethodsRetrospective study design with clinical information gathered from ED patient referrals to an outpatient TIA unit in Victoria, BC, from 2015-2016. Actual CTA orders by ED and TIA unit staff were compared to hypothetical CTA ordering if our CDST had been used in the ED upon patient arrival.ResultsFor 1,679 referrals, clinicians ordered 954 CTAs. Our CDST would have ordered a total of 977 CTAs for these patients. Overall, this would have increased the number of imaged-TIA patients by 89 (10.1%) while imaging 98 (16.1%) fewer non-cerebrovascular patients over the 2-year period. Our CDST would have ordered CTA for 18 (78.3%) of the recurrent stroke patients in the sample.ConclusionsOur CDST could enhance CTA utilization in the ED for suspected TIA patients, and facilitate guideline-based stroke care. Use of our CDST would increase the number of TIA patients receiving CTA before ED discharge (rather than later at TIA units) and reduce the burden of imaging stroke mimics in radiological departments.

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