Diagnostic errors affecting one in fourteen hospital patients

Most (85%) 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, say the researchers.

Previously published reports suggest that current trigger tools for picking up medical mistakes aren't good enough to detect harmful diagnostic errors, including those with less severe outcomes, suggest the researchers.

They therefore developed and validated a structured case review process to enable clinicians to interrogate the electronic health record (EHR) to evaluate the diagnostic process for hospital patients, assess the likelihood of a diagnostic error, and characterize the impact and severity of harm.

They used the process to estimate retrospectively the prevalence of harmful diagnostic errors in a randomly selected sample of 675 hospital patients out of a total of 9147 in receipt of general medical care between July 2019 and September 2021, excluding the height of the COVID-19 pandemic (April-December 2020).

Cases deemed to be at low risk were those fulfilling none of the high risk criteria (106; 2.5%).

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); low-risk patients (20).

In all, an estimated 85% of harmful diagnostic errors were preventable, with older, White, non-Hispanic, non-privately insured and high-risk patients most at risk.

Weighted to take account of the population, the researchers estimated the proportion of harmful, preventable, and severely harmful diagnostic errors in general medical hospital patients to be just over 7%, 6%, and 1%, respectively.

Process failures were significantly associated with diagnostic errors, particularly uncertainty in initial assessments and complex diagnostic testing and interpretation (4 times the risk), suboptimal subspecialty consultation (3 times the risk), concerns reported by patients (3 times the risk) and history taking (2.5 times the risk).

Forty (48%) diagnostic errors were related to the primary diagnosis at admission or discharge and 44 (52.5%) to a secondary diagnosis. Fifty two (62%) were characterised as delays. Errors associated with major or fatal harm were frequent in the high risk group (55%, 43/78) and rare in the low risk group (0/6).

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

Careful analysis of the errors and integrating AI tools into the workflow should help to minimize their prevalence, by improving monitoring and triggering timely interventions, suggest the researchers.

This is an observational study, based on estimates, drawn from data on patients receiving general medical care at one single center, and should be interpreted in that context, caution the researchers.

They also acknowledge that the sample was restricted to patients with a length of hospital stay under 21 days, and that the study relied on information captured in the electronic health record, which is prone to inaccurate recording of deaths within 90 days.

Source:

BMJ Group

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