Why the study?
Delayed diagnosis of heart failure is common, often leading to missed opportunities for early initiation of GDMT before an emergency hospital admission. These diagnostic delays are amplified in socioeconomically deprived populations due to barriers to access, lower health literacy, and competing social needs.
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AI point-of-care testing merits deployment trials in deprived communities to shorten heart failure diagnostic delays; extends evidence on socioeconomic barriers to guideline-directed therapy.
Heart failure (HF) affects more than 64 million people globally and remains a leading cause of hospitalization and mortality.1 Delayed diagnosis is common, and many patients first receive a diagnosis during an emergency hospital admission, by which time opportunities for earlier initiation of guideline-directed medical therapy (GDMT) may already have been missed. These challenges often are amplified in socioeconomically deprived populations, in whom barriers to access, lower health literacy, and competing social needs may delay presentation and investigation.
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Sankaranarayanan et al. (2026) conducted an editorial in Heart failure. Artificial intelligence-enabled point-of-care diagnostics was evaluated. AI-enabled point-of-care diagnostics are examined for community-based heart failure detection in a deprived UK population, though no trial outcomes are reported in the excerpt.
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