Abstract Details

Title Automation in Acute Stroke Care: Using AI to Identify and Eliminate Workflow Delays

Topic Cerebrovascular Disease and Interventional Neurology

Presentation(s) P4 - Poster Session 4 (8:00 AM-9:00 AM)

Poster/Presentation
Number 4-009

Objective This study aims to show improvement in door-to-needle time using artificial intelligence in case of stroke activation, thereby improving stroke metrics in the community hospital.

Background Stroke is one of the leading causes of permanent morbidity in the general population. As Time is of the essence, treating stroke promptly to prevent serious complications and treat the reversible area of the infarct in the brain to prevent further spread of irreversible damage. Advancements in AI and technology provide relevant and apt information for making clinical decisions promptly. AI is used to mine the stroke metrics and provide inputs on where the delay in stroke management happens. This will show where we can approach and improve timely management and door-to-needle time.

Design/Methods AI automatically extracts timestamps in the EMR. It sends notifications to the responsible physician to manage stroke promptly so that the patient will get the highest quality of care. If there is no response, AI escalates the delay notification to the supervising/senior physician to ensure treatment. Each order’s timestamp is recorded to monitor stroke metrics and compared against average management times, allowing identification of delays and areas for improvement without the need for manual monitoring.

Results We hypothesize that AI mining of data for the Joint Commission stroke metrics and near-realtime notifications for individual cases will show the areas where improvements must be made for effective stroke management.

Conclusions AI-assisted data mining can provide accurate areas for improvement. This will reduce the time and cost of manual analysis to find the issue and help us focus more on solving issues and improving the performance and efficiency of stroke management in real time.

Authors/Disclosures
Lakshmi Sai Deepak Velugoti PRESENTER	Lakshmi Sai Deepak Velugoti has nothing to disclose.
Kashiff Ariff, MD	Dr. Ariff has nothing to disclose.
Sangeetha Devendiran, MD, MBBS	Dr. Devendiran has nothing to disclose.
Irenne Maliakkal, MD (GARDEN CITY HOSPITAL)	Ms. Maliakkal has nothing to disclose.
Gustavo E. Faria Mendez, MD (Garden City Hospital)	Dr. Faria Mendez has nothing to disclose.
Maen Saleh, MD (Garden City Hospital)	Dr. Saleh has nothing to disclose.
Supriya Panneer Selvam, MD	No disclosure on file
Maria G. Baquerizo Correa, MD	Dr. Baquerizo Correa has nothing to disclose.
Ramesh Madhavan, MD, FAAN (International Medical Clinic)	Dr. Madhavan has received stock or an ownership interest from TiaTech USA and TiaTech India. Dr. Madhavan has received intellectual property interests from a discovery or technology relating to health care.
Pratik D. Bhattacharya, MD, MPH (International Medical Clinic)	Dr. Bhattacharya has a non-compensated relationship as a Research Advisor with Defeat MSA Alliance 501 (c) (3) that is relevant to AAN interests or activities.
Ayaz M. Khawaja, MD	Dr. Khawaja has nothing to disclose.
Alexander Tobar, DO	Dr. Tobar has nothing to disclose.
Ismail Rahal, DO (Garden City Hospital)	Dr. Rahal has nothing to disclose.
Taylor Graham, MD	Dr. Graham has nothing to disclose.

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