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New Tool Helps Predict Stroke Outcome with Higher Accuracy

DAIC

29, 2025 Researchers atOchsner Health, led byHernan Bazan, MD, DFSVS, FACS, have developed a predictive model with a 93% accuracy rate in determining whether urgent carotid-intervention patients will regain functional independence. Stroke care presents distinct challenges that require swift action and informed decisions.

Strokes 52
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Bridging the Gap: Enhancing Stroke Recovery Through Digital Health Solutions

DAIC

Stroke recovery is a challenging process that extends for months after hospital discharge. Issues like cognitive impairment and social determinants of health (SDOH) hurdles often go unrecognized until patients are home. After the stroke, I had new prescriptions, and this was the first obstacle for me.

Strokes 115
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STS Announces Late-breaker Research to Be Presented at the 2024 Annual Meeting

DAIC

The event’s late-breaking trial sessions focus on studies anticipated to significantly influence advances in cardiothoracic patient care. The analysis included 4,798 patients from 207 STS sites who underwent esophagectomy between 2012-2019.

Research 111
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Abstract TP120: Hospital Finance Optimization Through AI-based Stroke Care Coordination Platform

Stroke Journal

Stroke, Volume 56, Issue Suppl_1 , Page ATP120-ATP120, February 1, 2025. Introduction:AI-based Stroke Care Coordination Platforms (AI-SCCP) have been shown to improve patient transfer decisions and provide access to the highest quality standards of care for all patients.