Study: 1 of 14 patients at risk of wrong diagnosis

One out of every 14 patients hospitalised are at risk of wrong diagnosis though these diagnosis errors are preventable, a new research has revealed.

New York: One out of every 14 patients hospitalised are at risk of wrong diagnosis though these diagnosis errors are preventable, a new research has revealed.

The study, published in the journal BMJ Quality and Safety, said that 85% errors can be prevented, underscoring the need for new approaches to improving surveillance to avoid these mistakes from happening in the first place.

The most frequent diagnoses associated with diagnostic errors included heart failure, acute kidney failure, sepsis, pneumonia, respiratory failure, altered mental state, abdominal pain and hypoxaemia (low blood oxygen levels).

Cases deemed to be at high risk of diagnostic error were categorised as transfer to intensive care 24 or more hours after admission, death within 90 days of admission either in hospital or after discharge and complex clinical issues but no transfer to intensive care or death within 90 days of admission, the study said.

"Complex clinical issues included clinical deterioration, treatment by several different medical teams, unexpected events such as cancelled surgery, unclear or discrepant diagnostic information recorded in the medical notes," the study revealed.

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

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

The study authors wrote that an estimated 85% of harmful diagnostic errors were preventable, with older, White, non-Hispanic, non-privately insured and high-risk patients most at risk.

The researchers recommended that a careful analysis of the errors and integrating AI tools into the workflow should help reduce their prevalence, by improving monitoring and triggering timely interventions.

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