Abstract
ObjectiveTo determine the anticipated contribution of recently published medical imaging literature, including artificial intelligence (AI), on the workload of diagnostic radiologists.MethodsThis study included a random sample of 440 medical imaging studies published in 2019. The direct contribution of each study to patient care and its effect on the workload of diagnostic radiologists (i.e., number of examinations performed per time unit) was assessed. Separate analyses were done for an academic tertiary care center and a non-academic general teaching hospital.ResultsIn the academic tertiary care center setting, 65.0% (286/440) of studies could directly contribute to patient care, of which 48.3% (138/286) would increase workload, 46.2% (132/286) would not change workload, 4.5% (13/286) would decrease workload, and 1.0% (3/286) had an unclear effect on workload. In the non-academic general teaching hospital setting, 63.0% (277/240) of studies could directly contribute to patient care, of which 48.7% (135/277) would increase workload, 46.2% (128/277) would not change workload, 4.3% (12/277) would decrease workload, and 0.7% (2/277) had an unclear effect on workload. Studies with AI as primary research area were significantly associated with an increased workload (p < 0.001), with an odds ratio (OR) of 10.64 (95% confidence interval (CI) 3.25–34.80) in the academic tertiary care center setting and an OR of 10.45 (95% CI 3.19–34.21) in the non-academic general teaching hospital setting.ConclusionsRecently published medical imaging studies often add value to radiological patient care. However, they likely increase the overall workload of diagnostic radiologists, and this particularly applies to AI studies.
Highlights
The workload of radiologists has increased considerably over the past decades
Study purpose: “The objectives of this study were to assess whether the accuracy of urologists in identifying the presence of clinically significant cancer based on a standardized multiparametric MRI set could be improved by completion of a 2-d training course” Study conclusion: “Whilst we require expert radiologists to report prostate MRI, this study has demonstrated that identification of clinically significant cancer on prostate MRI by urologists is improved following exposure to a 2-d teaching course
Both artificial intelligence (AI) as research area and a lower impact factor of the journal in which the study was published were significantly associated with an increased workload (Table 4)
Summary
The workload of radiologists (i.e., the product of the number and complexity of examinations performed per time unit) has increased considerably over the past decades This is largely due to the growth in the number of cross-sectional imaging examinations ( CT and MRI), increased complexity of image interpretation because of the acquisition of larger datasets, and declining imaging reimbursements [1,2,3,4]. The latter forces radiology practices to increase productivity to maintain income levels, while limiting their financial possibilities to employ new staff. Work overload may compromise the quality and safety of patient care that can be provided by radiologists [7,8,9]
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