Clinicians' Behavior Toward Radiology Reports: A Cross-Sectional Study

Reda, Azza Sami; Hashem, Dalia Abdulmonem; Khashoggi, Khalid; Abukhodair, Felwa

doi:10.7759/cureus.11336

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Previous studies have shown that around 10-20% of radiology reports are not read by clinicians [20,21]. Previous work also indicated that radiography reports are less frequently read than cross-sectional imaging reports [20].…”

Section: Projected Radiologist Workforce Requirementmentioning

confidence: 99%

Diagnostic radiology and its future: what do clinicians need and think?

2023

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Objective To investigate the view of clinicians on diagnostic radiology and its future. Methods Corresponding authors who published in the New England Journal of Medicine and the Lancet between 2010 and 2022 were asked to participate in a survey about diagnostic radiology and its future. Results The 331 participating clinicians gave a median score of 9 on a 0–10 point scale to the value of medical imaging in improving patient-relevant outcomes. 40.6%, 15.1%, 18.9%, and 9.5% of clinicians indicated to interpret more than half of radiography, ultrasonography, CT, and MRI examinations completely by themselves, without consulting a radiologist or reading the radiology report. Two hundred eighty-nine clinicians (87.3%) expected an increase in medical imaging utilization in the coming 10 years, whereas 9 clinicians (2.7%) expected a decrease. The need for diagnostic radiologists in the coming 10 years was expected to increase by 162 clinicians (48.9%), to remain stable by 85 clinicians (25.7%), and to decrease by 47 clinicians (14.2%). Two hundred clinicians (60.4%) expected that artificial intelligence (AI) will not make diagnostic radiologists redundant in the coming 10 years, whereas 54 clinicians (16.3%) thought the opposite. Conclusion Clinicians who published in the New England Journal of Medicine or the Lancet attribute high value to medical imaging. They generally need radiologists for cross-sectional imaging interpretation, but for a considerable proportion of radiographs, their service is not required. Most expect medical imaging utilization and the need for diagnostic radiologists to increase in the foreseeable future, and do not expect AI to make radiologists redundant. Clinical relevance statement The views of clinicians on radiology and its future may be used to determine how radiology should be practiced and be further developed. Key Points • Clinicians generally regard medical imaging as high-value care and expect to use more medical imaging in the future. • Clinicians mainly need radiologists for cross-sectional imaging interpretation while they interpret a substantial proportion of radiographs completely by themselves. • The majority of clinicians expects that the need for diagnostic radiologists will not decrease (half of them even expect that we need more) and does not believe that AI will replace radiologists.

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Section: Projected Radiologist Workforce Requirementmentioning

confidence: 99%

Diagnostic radiology and its future: what do clinicians need and think?

2023

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“…Suggest MRI to exclude hyperacute infarct if indicated. 3. Liver tumor cannot be excluded by noncontrast CT.…”

Section: Included Speculationsmentioning

confidence: 99%

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study (Preprint)

Weng¹,

Chen²,

Liu³

2023

Preprint

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BACKGROUND Negation and the speculation unrelated to abnormal findings can lead to false positive alarms for automatic radiology report highlighting or flagging by laboratory information systems. OBJECTIVE This internal validation study evaluates the performance of NLP methods (NegEx, NegBio, NegBERT, and Transformers). METHODS We annotated all negative and the speculative statements unrelated to abnormal findings in reports. In Experiment 1, we fine-tuned several Transformer models (ALBERT, BERT, DeBERTa, DistilBERT, ELECTRA, ERNIE, RoBERTa, SpanBERT, XLNet) and compared their performance using precision, recall, accuracy, and F1 scores. In Experiment 2, we compared the best model from Experiment 1 with three established negation and speculation detection algorithms (NegEx, NegBio, NegBERT). RESULTS Our study collected 6000 radiology reports from three branches of Chi Mei Hospital, covering multiple imaging modalities and body parts. 15.0% of words and 39.5% of important diagnostic keywords occurred in negative statements or speculative statements unrelated to abnormal findings. In experiment 1, all models achieved accuracy > 98% and F1 score > 90% on the test dataset. ALBERT showed the best performance (accuracy 99.1%, F1 score 95.8%). In experiment 2, ALBERT outperformed the optimized NegEx, NegBio, and NegBERT methods overall (accuracy 99.6%, F1 score 99.1%) and in the prediction of whether diagnostic keywords occur in speculative statements unrelated to abnormal findings. CONCLUSIONS The ALBERT deep learning method showed the best performance. Our result represents a significant advance in the clinical application of computer-aided notification systems. CLINICALTRIAL Not applicable

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“…However, even with this closed-loop system of communication implemented and the addition of an extra concordant/discordant component, issues can still arise. In a study by Reda et al, the authors reported that over 20% of clinicians did not read the radiology report for the patient in question [ 17 ]. Other authors described similar findings, with one study stating that only 55.7% of referring clinicians read the radiologic report in full, while the rest skim, or do not look at the report at all [ 18 ].…”

Section: The Missing Link In the Chain Of Reporting: Let’s Close The ...mentioning

confidence: 99%

An Evolution of Reporting: Identifying the Missing Link

et al. 2022

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In recent years, radiologic imaging has undergone tremendous technological advances and is now a pillar of diagnostic and treatment algorithms in clinical medicine. The increased complexity and volume of medical imaging has led clinicians to become ever more reliant on radiologists to both identify and interpret patient studies. A radiologist’s report provides key insights into a patient’s immediate state of health, information that is vital when choosing the most appropriate next steps in management. As errors in imaging interpretation or miscommunication of results can greatly impair patient care, identifying common error sources is vital to minimizing their occurrence. Although mistakes in medical imaging are practically inevitable, changes to the delivery of imaging reporting and the addition of artificial intelligence algorithms to analyze clinicians’ communication skills can minimize the impact of these errors, keep up with the continuously evolving landscape of medical imaging, and ultimately close the communication gap.

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Clinicians' Behavior Toward Radiology Reports: A Cross-Sectional Study

Cited by 5 publications

References 14 publications

Diagnostic radiology and its future: what do clinicians need and think?

Diagnostic radiology and its future: what do clinicians need and think?

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study (Preprint)

An Evolution of Reporting: Identifying the Missing Link

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