US hospitals using predictive AI in EHRs rise to 71%
The use of predictive AI in US hospitals has increased to 71%, though experts warn that system interoperability and data quality remain significant hurdles. Effective implementation requires rigorous post-deployment evaluation to ensure models perform accurately across different clinical environments.
Why it matters
As AI becomes integrated into critical healthcare infrastructure, ensuring data accuracy and model reliability is essential for patient safety.
--> In the United States, 71% of non-federal acute care hospitals reported using predictive artificial intelligence integrated with electronic health records in 2024. A year earlier, this figure was 66%, MyJoyOnline reports , citing data presented in the article.
The article notes that the reliability of such systems depends not only on algorithms but also on the quality, meaning, security and traceability of medical data. A completed medical record can be clinically misleading: a diagnosis used for billing does not always reflect the full picture of a patient's condition, a laboratory result may contain an incorrect unit of measurement, and a timestamp may record when information was entered into the system rather than the medical event itself.
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