Combining Machine Learning With Real-World Data to Identify Gaps in Clinical Practice Guidelines: Feasibility Study Using the Prospective German Stroke Registry and the National Acute Ischemic Stroke Guidelines.

ABSTRACT:

Background

Clinical practice guidelines (CPGs) serve as essential tools for guiding clinicians in providing appropriate patient care. However, clinical practice does not always reflect CPGs. This is particularly critical in acute diseases requiring immediate treatment, such as acute ischemic stroke, one of the leading causes of morbidity and mortality worldwide. Adherence to CPGs improves patient outcomes, yet guidelines may not address all patient scenarios, resulting in variability in treatment decisions. Identifying such gaps would augment CPGs but is challenging when using traditional methods.

Objective

This study aims to leverage real-world data coupled with machine learning (ML) techniques to systematically identify and quantify gaps in German thrombolysis-in-stroke gu

SUBMITTER: Muller S 

PROVIDER: S-EPMC12274016 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature