Abstract
ObjectiveThe aim is to evaluate whether smart worklist prioritization by artificial intelligence (AI) can optimize the radiology workflow and reduce report turnaround times (RTATs) for critical findings in chest radiographs (CXRs). Furthermore, we investigate a method to counteract the effect of false negative predictions by AI—resulting in an extremely and dangerously long RTAT, as CXRs are sorted to the end of the worklist.MethodsWe developed a simulation framework that models the current workflow at a university hospital by incorporating hospital-specific CXR generation rates and reporting rates and pathology distribution. Using this, we simulated the standard worklist processing “first-in, first-out” (FIFO) and compared it with a worklist prioritization based on urgency. Examination prioritization was performed by the AI, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass, and foreign object. Furthermore, we introduced an upper limit for the maximum waiting time, after which the highest urgency is assigned to the examination.ResultsThe average RTAT for all critical findings was significantly reduced in all prioritization simulations compared to the FIFO simulation (e.g., pneumothorax: 35.6 min vs. 80.1 min; p < 0.0001), while the maximum RTAT for most findings increased at the same time (e.g., pneumothorax: 1293 min vs 890 min; p < 0.0001). Our “upper limit” substantially reduced the maximum RTAT in all classes (e.g., pneumothorax: 979 min vs. 1293 min/1178 min; p < 0.0001).ConclusionOur simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO.Key Points• Development of a realistic clinical workflow simulator based on empirical data from a hospital allowed precise assessment of smart worklist prioritization using artificial intelligence.• Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time.• Use of a state-of-the-art convolution neural network can reduce the average report turnaround time almost to the upper limit of a perfect classification algorithm (e.g., pneumothorax: 35.6 min vs. 30.4 min).
Highlights
IntroductionShortage of medical experts, and declining revenues often lead to potentially dangerous backlogs of unreported examinations, especially in publicly funded healthcare systems
Materials and methodsGrowing radiologic workload, shortage of medical experts, and declining revenues often lead to potentially dangerous backlogs of unreported examinations, especially in publicly funded healthcare systems
Employing a smart worklist prioritization without a threshold for maximum waiting time runs the risk of false negative predictions of the artificial intelligence greatly increasing the report turnaround time
Summary
Shortage of medical experts, and declining revenues often lead to potentially dangerous backlogs of unreported examinations, especially in publicly funded healthcare systems. Delayed communication of critical findings to the referring physician bears the risk of delayed clinical intervention and impairs the outcome of medical treatment [2,3,4], especially in cases requiring immediate action, e.g., tension pneumothorax or misplaced catheters. For this reason, the Joint Commission defined the timely reporting of critical diagnostic results as an important goal for patient safety [5]. The ordering physician’s urgency information is often incomplete or presented as ambiguous and ill-defined priority level, such as “critical,” “ASAP,” or “STAT” [6, 7]
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