Impact of diagnostic errors on adverse outcomes: learning from emergency department revisits with repeat CT or MRI

Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization

Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.

Utility of cranial MRI in non-traumatic headache patients with prior negative head CT within 1 month

AIM

To investigate the importance of additional cranial magnetic resonance imaging (cMRI) in non-traumatic headache patients with a prior negative head computed tomography (CT) examination within 1 month.

MATERIALS AND METHODS

This retrospective study analysed 162 adult patients with non-traumatic headache who underwent cMRI within 1 month of a negative initial head CT at the emergency department (ED). The diagnostic yield and false-referral rate were analysed according to the revisit duration (early [≤1 week] versus late [>1-4 weeks] revisits), patient care settings (ED versus outpatient clinics [OPC]), and clinical variables. Subsequent patient management change (PMC), such as admission and treatment (AT) or outpatient clinic treatment (OT), were also investigated.

RESULTS

The overall diagnostic yield of cMRI was 17.3% (28/162) and the false-referral rate was 1.2% (2/162). The diagnostic yield of cMRI was significantly different according to the patient care settings (ED, 24.7% [21/85] versus OPC, 9.1% [7/77]; p=0.02). The diagnostic yield was highest in the ED-early-revisit group (25.4% [18/71]), 45% (9/20) in those with systemic signs, and 46.7% (14/30) in those with symptom change. Among patients with positive cMRI findings, 90% (27/30) received AT and 3.3% (1/30) received OT. Among OPC-revisit-negative cMRI patients, PMC occurred in 0% (0/50).

CONCLUSION

The diagnostic yield of cMRI was relatively high for headache patients who revisited the ED earlier, especially in those with systemic signs or symptom change. Most positive cMRI cases experienced PMC. Negative cMRI in OPC-revisit patients might help clarify the benign nature of a condition.

Influence of Prior Imaging Information on Diagnostic Accuracy for Focal Skeletal Processes—A Retrospective Analysis of the Consistency between Biopsy-Verified Imaging Diagnoses

Introduction: Comparing imaging examinations with those previously obtained is considered mandatory in imaging guidelines. To our knowledge, no studies are available on neither the influence, nor the sequence, of prior imaging and reports on diagnostic accuracy using biopsy as the reference standard. Such data are important to minimize diagnostic errors and to improve the preparation of diagnostic imaging guidelines. The aim of our study was to provide such data. Materials and methods: A retrospective cohort of 216 consecutive skeletal biopsies from patients with at least 2 different imaging modalities (X-ray, CT and MRI) performed within 6 months of biopsy was identified. The diagnostic accuracy of the individual imaging modality was assessed. Finally, the possible influence of the sequence of imaging modalities was investigated. Results: No significant difference in the accuracy of the imaging modalities was shown, being preceded by another imaging modality or not. However, the sequence analyses indicate sequential biases, particularly if MRI was the first imaging modality. Conclusion: The sequence of the imaging modalities seems to influence the diagnostic accuracy against a pathology reference standard. Further studies are needed to establish evidence-based guidelines for the strategy of using previous imaging and reports to improve diagnostic accuracy.

Patient recalls associated with resident-to-attending radiology report discrepancies: predictive factors for risky discrepancies

BACKGROUND

This study aimed to identify predictive factors for risky discrepancies in the emergency department (ED) by analyzing patient recalls associated with resident-to-attending radiology report discrepancies (RRDs).

RESULTS

This retrospective study analyzed 759 RRDs in computed tomography (CT) and magnetic resonance imaging and their outcomes from 2013 to 2021. After excluding 73 patients lost to follow-up, we included 686 records in the final analysis. Risky discrepancies were defined as RRDs resulting in (1) inpatient management (hospitalization) and (2) adverse outcomes (delayed operations, 30-day in-hospital mortality, or intensive care unit admission). Predictors of risky discrepancies were assessed using multivariable logistic regression analysis. The overall RRD rate was 0.4% (759 of 171,419). Of 686 eligible patients, 21.4% (147 of 686) received inpatient management, and 6.0% (41 of 686) experienced adverse outcomes. RRDs with neurological diseases were associated with the highest ED revisit rate (79.4%, 81 of 102) but not with risky RRDs. Predictive factors of inpatient management were critical finding (odds ratio [OR], 5.60; p < 0.001), CT examination (OR, 3.93; p = 0.01), digestive diseases (OR, 2.54; p < 0.001), and late finalized report (OR, 1.65; p = 0.02). Digestive diseases (OR, 6.14; p = 0.006) were identified as the only significant predictor of adverse outcomes.

CONCLUSIONS

Risky RRDs were associated with several factors, including CT examination, digestive diseases, and late finalized reports, as well as critical image findings. This knowledge could aid in determining the priority of discrepancies for the appropriate management of RRDs.