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New study suggests diagnostic errors may occur in as many as 1 in every 14 patients

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New study suggests diagnostic errors may occur in as many as 1 in every 14 patients

A new study highlights that diagnostic errors are likely to occur in 1 in every 14 (7 percent) hospital patients, requiring new approaches to medical monitoring.

The study, published online in the journal BMJ Quality & Safety, says 85 percent of these errors are preventable and highlights the need for new approaches to improving monitoring to prevent these mistakes from happening.

The most common diagnoses associated with diagnostic errors include heart failure, acute kidney failure, sepsis, pneumonia, respiratory failure, altered mental status, abdominal pain, and hypoxemia (low blood oxygen levels).

According to the study, cases at high risk of diagnostic error include transfer to intensive care 24 or more hours after admission, death in the hospital or within 90 days after discharge, and complex clinical issues. Was classified in, but there was no transfer to intensive care. care or death within 90 days of admission.

The findings showed, “Complex clinical issues include clinical deterioration, treatment by multiple different medical teams, unexpected events such as canceled surgeries, unclear or inconsistent clinical information recorded in medical notes.”

Harm was classified as minor, moderate, severe and fatal, and whether the diagnostic error contributed to the harm and whether it could have been prevented was also assessed. Cases with discrepancies or uncertainty about the diagnostic error or its impact were further reviewed by an expert panel.

Of all the cases reviewed, diagnostic errors were found in 160 cases (154 patients). These included intensive care transfers (54), death within 90 days (34), complex clinical issues (52) and low-risk patients (20).

The study authors wrote that an estimated 85 percent of harmful diagnostic errors could have been prevented, with older, white, non-Hispanic, non-privately insured and high-risk patients most at risk.

The researchers suggested that careful analysis of errors and integrating AI tools into workflows should help reduce their spread, by improving monitoring and initiating timely interventions.

(Except for the headline, this story has not been edited by NDTV staff and is published from a syndicated feed.)

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