Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the “Big Three”

Abstract

BackgroundMissed vascular events, infections, and cancers account for ~75% of serious harms from diagnostic errors. Just 15 diseases from these “Big Three” categories account for nearly half of all serious misdiagnosis-related harms in malpractice claims. As part of a larger project estimating total US burden of serious misdiagnosis-related harms, we performed a focused literature review to measure diagnostic error and harm rates for these 15 conditions.MethodsWe searched PubMed, Google, and cited references. For errors, we selected high-quality, modern, US-based studies, if available, and best available evidence otherwise. For harms, we used literature-based estimates of the generic (disease-agnostic) rate of serious harms (morbidity/mortality) per diagnostic error and applied claims-based severity weights to construct disease-specific rates. Results were validated via expert review and comparison to prior literature that used different methods. We used Monte Carlo analysis to construct probabilistic plausible ranges (PPRs) around estimates.ResultsRates for the 15 diseases were drawn from 28 published studies representing 91,755 patients. Diagnostic error (false negative) rates ranged from 2.2% (myocardial infarction) to 62.1% (spinal abscess), with a median of 13.6% [interquartile range (IQR) 9.2–24.7] and an aggregate mean of 9.7% (PPR 8.2–12.3). Serious misdiagnosis-related harm rates per incident disease case ranged from 1.2% (myocardial infarction) to 35.6% (spinal abscess), with a median of 5.5% (IQR 4.6–13.6) and an aggregate mean of 5.2% (PPR 4.5–6.7). Rates were considered face valid by domain experts and consistent with prior literature reports.ConclusionsDiagnostic improvement initiatives should focus on dangerous conditions with higher diagnostic error and misdiagnosis-related harm rates.

Bringing the clinical laboratory into the strategy to advance diagnostic excellence

“…Further, it may be helpful to focus on medical conditions reported to be more prone to diagnostic errors. One target may be the three-fourths of serious misdiagnoses that are attributable to major vascular events, cancer, and infectious diseases [ 74 , 75 ]. Diagnostic surveillance can provide a data-driven approach to quality management that spans both patient-care and laboratory settings may provide an approach to advancing diagnostic excellence.…”

A Perspective: Use of Machine Learning Models to Predict the Risk of Multimorbidity

“…Misdiagnosis is not common, but still a possibility, with studies citing 10% to 15% of clinical diagnosis to be false-negatives [17]. Misdiagnosis rate varies across diseases, ranging from 2.2% for myocardial infarction to 62.1% for spinal abscess according to one study [18]. This study also reported that 53.9% of the patients who…”

Diagnostic error in hospitals: finding forests not just the big trees

“…The prevalence however is lower than what might be expected looking at previous research, mostly in outpatient care, and based on expert estimates. [8][9][10][11] The prevalence of diagnostic error in hospital care may be lower because outpatient care, especially primary care, has the challenging task of identifying patients with a serious disease from a large sample of patients who present with common symptoms and mostly benign non-urgent diseases. A higher state of attention in the hospital and higher prior probability of a patient having a more serious disease may also reduce the likelihood of something being missed (ie, the prevalence effect).…”