Study says diagnostic errors can occur in 1 in every 14 hospital patients

Diagnostic errors are likely to be occurring in as many as 1 in every 14 (7 per cent) hospital patients which requires new approaches to medical surveillance, a new study has stressed.

The study, published online in the journal BMJ Quality and Safety, said that 85 per cent of these errors are likely preventable and underscore 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).

According to the study, 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.

“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 findings showed.

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 per cent of harmful diagnostic errors were preventable, with older, White, non-Hispanic, non-privately insured and high-risk patients most at risk.

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