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Pushing selected information to clinicians, as opposed to the traditional method of clinicians pulling information from an electronic medical record, has the potential to improve care. A digital notification platform was designed by clinicians and implemented in a tertiary hospital to flag dysglycaemia. There were 112 patients included in the study, and the post‐implementation group demonstrated lower rates of dysglycaemia (2.5% vs 1.1%, P = 0.038). These findings raise considerations for information delivery methods for multiple domains in contemporary healthcare. [ABSTRACT FROM AUTHOR]

Background: Biological age can be predicted using artificial intelligence (AI) trained on electrocardiograms (ECGs), which is prognostic for mortality and cardiovascular events., Objective: We developed an AI model to predict age from ECG and compared baseline characteristics to identify determinants of advanced biological age., Methods: An AI model was trained on ECGs from cardiology inpatients aged 20-90 years. AI analysis used a convolutional neural network with data divided in an 80:20 ratio (development:internal validation), with external validation undertaken using data from the UK Biobank. Performance and subgroup comparison measures included correlation, difference and mean absolute difference., Results: 63,246 patients with 353,704 total ECGs were included. In internal validation, the correlation coefficient was 0.72, with a mean absolute difference between chronological and AI-predicted age of 9.1 years. The same model performed similarly in external validation. In patients aged 20-29, AI-ECG biological age was older than chronological age by a mean 14.3±0.2 yrs. In patients aged 80-89 years, biological age was younger by a mean 10.5±0.1 yrs. Women were biologically younger than men by a mean of 10.7 months (P=0.023) and patients with a single ECG were biologically 1.0 years younger than those with multiple ECGs (P<0.0001)., Conclusion: There are significant between-group differences in AI-ECG biological age for patient subgroups. Biological age was greater than chronological age in young, hospitalized patient, and less than chronological age in the older hospitalized patient. Women and patients with a single ECG recorded were biologically younger than men and patients with multiple recorded ECGs., (Copyright © 2024. Published by Elsevier Inc.)