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

Zwaan, Laura; Singh, Hardeep

doi:10.1136/bmjqs-2020-011099

Cited by 19 publications

(9 citation statements)

References 30 publications

(25 reference statements)

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“…26 GPs are often tasked with identifying patients with a serious disease from large numbers who present with common symptoms and mostly benign non-urgent diseases, many of which evolve over time. 27 A significant proportion of face-to-face consultations are for reasons other than making a diagnosis. Nonetheless, although reviewers often differed about fine-grained details such as levels of available evidence and degrees of confidence that an MDO had occurred, joint review agreement was high regarding the essential question of whether or not a consultation included a missed opportunity to make a diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Incidence, origins and avoidable harm of missed opportunities in diagnosis: longitudinal patient record review in 21 English general practices

et al. 2021

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BackgroundDiagnostic error is a global patient safety priority.ObjectivesTo estimate the incidence, origins and avoidable harm of diagnostic errors in English general practice. Diagnostic errors were defined as missed opportunities to make a correct or timely diagnosis based on the evidence available (missed diagnostic opportunities, MDOs).MethodRetrospective medical record reviews identified MDOs in 21 general practices. In each practice, two trained general practitioner reviewers independently conducted case note reviews on 100 randomly selected adult consultations performed during 2013–2014. Consultations where either reviewer identified an MDO were jointly reviewed.ResultsAcross 2057 unique consultations, reviewers agreed that an MDO was possible, likely or certain in 89 cases or 4.3% (95% CI 3.6% to 5.2%) of reviewed consultations. Inter-reviewer agreement was higher than most comparable studies (Fleiss’ kappa=0.63). Sixty-four MDOs (72%) had two or more contributing process breakdowns. Breakdowns involved problems in the patient–practitioner encounter such as history taking, examination or ordering tests (main or secondary factor in 61 (68%) cases), performance and interpretation of diagnostic tests (31; 35%) and follow-up and tracking of diagnostic information (43; 48%). 37% of MDOs were rated as resulting in moderate to severe avoidable patient harm.ConclusionsAlthough MDOs occurred in fewer than 5% of the investigated consultations, the high numbers of primary care contacts nationally suggest that several million patients are potentially at risk of avoidable harm from MDOs each year. Causes of MDOs were frequently multifactorial, suggesting the need for development and evaluation of multipronged interventions, along with policy changes to support them.

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Section: Discussionmentioning

confidence: 99%

Incidence, origins and avoidable harm of missed opportunities in diagnosis: longitudinal patient record review in 21 English general practices

et al. 2021

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“…Problematic codes are attributed to a range of factors in previous research, including a) lack of necessary knowledge and skills in disease diagnosis and use of ICD codes by medical practitioners and coders [ 34 – 37 ]; b) heavy workload for medical practitioners and coders [ 38 ]; and c) lack of work enthusiasm for many coders [ 39 , 40 ].…”

Section: Discussionmentioning

confidence: 99%

Coding quality of deaths and its impact on elderly unintentional fall mortality data from 1990 to 2019: a retrospective analysis of the WHO Mortality Database

et al. 2022

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Background Several studies have assessed the reporting quality of all-cause mortality data from the WHO Mortality Database, but little is known about coding quality and its impact on elderly unintentional fall mortality data worldwide. We aimed to assess the coding quality of deaths and its impact on elderly unintentional fall mortality. Methods Using data from the WHO Mortality Database, 1990–2019, we calculated the number of countries/territories that had mortality data in the database, and the proportion of deaths with five types of problematic codes based on the 10th International Classification of Disease (unspecified deaths, injury deaths with undetermined intent, unspecified unintentional injury, unintentional falls with unspecified mechanism, unintentional falls with unknown occurrence place). We estimated age-adjusted unintentional fall mortality before and after correcting problematic codes. Results Only 64% (124/194) of WHO member states had at least 1 year of mortality data in the database during 1990–2019, and data unavailability was more common for underdeveloped countries/territories than for developed countries/territories. Coding quality was poor for many countries/territories. Among the study years when countries/territories possessed mortality data, 80, 53, 51, and 63% had a proportion of unintentional fall deaths with unspecified mechanism over 50% in low-income, lower middle-income, upper middle-income, and high-income countries/territories, respectively; comparable proportions for unintentional fall deaths with unknown occurrence place were 100, 42, 71, and 62%. Among the 94 countries/territories having mortality data, problematic codes caused a relative mortality difference ≥ 50% in 59 countries/territories (63%). After correcting problematic codes, 5 of 55 countries/territories with data witnessed a reverse in mortality changes between 2005 and 2015. Among the 82 countries/territories with mortality data for 5 or more years, 18 countries/territories (22%) experienced a directional reverse in linear regression coefficient. Conclusions The availability and coding quality of global data related to elderly unintentional fall mortality was poor between 1990 and 2019. When data are available, varying coding quality across countries/territories and over time have a substantial impact on mortality estimates and mortality comparisons. Global agencies plus each individual government should be aware of the importance of collecting and sharing high-quality mortality data, and take action to improve data quality for inclusion in the WHO Mortality Database.

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“…Mislabelling controls has a smaller effect on power [Edwards et al, 2005], yet determining which error rate is larger is difficult and largely unknown [Zwaan and Singh, 2020]. Previous studies have considered treating cases as positive only and control data as unlabelled data [Halpern et al, 2014, Agarwal et al, 2016.…”

Section: Related Workmentioning

confidence: 99%

Phenotyping with Positive Unlabelled Learning for Genome-Wide Association Studies

Vauvelle¹,

Tomlinson²,

Sim³

et al. 2022

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Identifying phenotypes plays an important role in furthering our understanding of disease biology through practical applications within healthcare and the life sciences. The challenge of dealing with the complexities and noise within electronic health records (EHRs) has motivated applications of machine learning in phenotypic discovery. While recent research has focused on finding predictive subtypes for clinical decision support, here we instead focus on the noise that results in phenotypic misclassification, which can reduce a phenotypes ability to detect associations in genome-wide association studies (GWAS). We show that by combining anchor learning and transformer architectures into our proposed model, AnchorBERT, we are able to detect genomic associations only previously found in large consortium studies with 5× more cases. When reducing the number of controls available by 50%, we find our model is able to maintain 40% more significant genomic associations from the GWAS catalog compared to standard phenotype definitions.

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

Cited by 19 publications

References 30 publications

Incidence, origins and avoidable harm of missed opportunities in diagnosis: longitudinal patient record review in 21 English general practices

Incidence, origins and avoidable harm of missed opportunities in diagnosis: longitudinal patient record review in 21 English general practices

Coding quality of deaths and its impact on elderly unintentional fall mortality data from 1990 to 2019: a retrospective analysis of the WHO Mortality Database

Phenotyping with Positive Unlabelled Learning for Genome-Wide Association Studies

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