Contributors to Diagnostic Error or Delay in the Acute Care Setting: A Survey of Clinical Stakeholders

Redmond, Sarah; Barwise, Amelia; Zornes, Sarah; Dong, Yue; Herasevich, Svetlana; Pinevich, Yuliya; Soleimani, Jalal; LeMahieu, Allison; Leppin, Aaron L.; Pickering, Brian

doi:10.1177/11786329221123540

Cited by 3 publications

(2 citation statements)

References 37 publications

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“…Charlson Comorbidity Index, a validated method to assess patient comorbidity, was calculated based on electronic note search strategies, as described elsewhere [ 26 ]. A physician–researcher (YP) and a PhD researcher with qualitative and mixed-methods research expertise in the field of diagnostic errors (SR) [ 27 ] reviewed EHR in order to assess possible reasons for diagnostic delays in selected patients.…”

Section: Methodsmentioning

confidence: 99%

Time to diagnostic certainty for saddle pulmonary embolism in hospitalized patients

Pinevich¹,

Barwise²,

Austin³

et al. 2023

Biomol Biomed

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There is a lack of diagnostic performance measures associated with pulmonary embolism (PE). We aimed to explore the concept of the time to diagnostic certainty, which we defined as the time interval that elapses between first presentation of a patient to a confirmed PE diagnosis with computed tomography pulmonary angiogram (CT PA). This approach could be used to highlight variability in health system diagnostic performance, and to select patient outliers for structured chart review in order to identify underlying contributors to diagnostic error or delay. We performed a retrospective observational study at academic medical centers and associated community-based hospitals in one health system, examining randomly selected adult patients admitted to study sites with a diagnosis of acute saddle PE. One hundred patients were randomly selected from 340 patients discharged with saddle PE. Twenty-four patients were excluded. Among the 76 included patients, time to diagnostic certainty ranged from 1.5 to 310 hours. We found that 73/76 patients were considered to have PE present on admission (CT PA ≤ 48 hours). The proportion of patients with PE present on admission with time to diagnostic certainty of > 6 hours was 26% (19/73). The median (IQR) time to treatment (thrombolytics/anticoagulants) was 3.5 (2.5-5.1) hours among the 73 patients. The proportion of patients with PE present on admission with treatment delays of > 6 hours was 16% (12/73). Three patients acquired PE during hospitalization (CT PA > 48 hours). In this study, we developed and successfully tested the concept of time to diagnostic certainty for saddle PE.

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Section: Methodsmentioning

confidence: 99%

Time to diagnostic certainty for saddle pulmonary embolism in hospitalized patients

Pinevich¹,

Barwise²,

Austin³

et al. 2023

Biomol Biomed

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“…Any delay in initiating the timely management of pathology can have a negative impact on patient outcomes. This becomes more critical in acute settings, where delayed diagnosis could lead to permanent disability or death [5]. Extended wait times can strain hospital resources, leading to longer hospital stays, increased costs, and worse patient outcomes [6].…”

Section: Introductionmentioning

confidence: 99%

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust

Blake,

Das,

Tadepalli

et al. 2023

Diagnostics

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Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as ‘abnormal’ or ‘normal’. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

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The Big Three diagnostic errors through reflections of Japanese internists

Kunitomo,

Gupta,

Harada

et al. 2024

Diagnosis

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Objectives To analyze the Big Three diagnostic errors (malignant neoplasms, cardiovascular diseases, and infectious diseases) through internists’ self-reflection on their most memorable diagnostic errors. Methods This secondary analysis study, based on a web-based cross-sectional survey, recruited participants from January 21 to 31, 2019. The participants were asked to recall the most memorable diagnostic error cases in which they were primarily involved. We gathered data on internists’ demographics, time to error recognition, and error location. Factors causing diagnostic errors included environmental conditions, information processing, and cognitive bias. Participants scored the significance of each contributing factor on a Likert scale (0, unimportant; 10, extremely important). Results The Big Three comprised 54.1 % (n=372) of the 687 cases reviewed. The median physician age was 51.5 years (interquartile range, 42–58 years); 65.6 % of physicians worked in hospital settings. Delayed diagnoses were the most common among malignancies (n=64, 46 %). Diagnostic errors related to malignancy were frequent in general outpatient settings on weekdays and in the mornings and were not identified for several months following the event. Environmental factors often contributed to cardiovascular disease-related errors, which were typically identified within days in emergency departments, during night shifts, and on holidays. Information gathering and interpretation significantly impacted infectious disease diagnoses. Conclusions The Big Three accounted for the majority of cases recalled by Japanese internists. The most relevant contributing factors were different for each of the three categories. Addressing these errors may require a unique approach based on the disease associations.

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Contributors to Diagnostic Error or Delay in the Acute Care Setting: A Survey of Clinical Stakeholders

Cited by 3 publications

References 37 publications

Time to diagnostic certainty for saddle pulmonary embolism in hospitalized patients

Time to diagnostic certainty for saddle pulmonary embolism in hospitalized patients

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust

The Big Three diagnostic errors through reflections of Japanese internists

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