Innovation opportunities in critical results communication: theoretical concepts

Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation

Objective

The 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.

Methods

We 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.

Results

The 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).

Conclusion

Our 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).

Difficulties and possibilities in communication between referring clinicians and radiologists: perspective of clinicians

Purpose

To investigate modes and quality of interprofessional communication between clinicians and radiologists, and to identify difficulties and possibilities in this context, as experienced by referring clinicians.

Patients and methods

Focus group interviews with 22 clinicians from different specialties were carried out. The leading question was: "How do you experience communication, verbal and nonverbal, between referring clinicians and radiologists?" Content analysis was used for interpretation of data.

Results

Overall, referring clinicians expressed satisfaction with their interprofessional communication with radiologists, and digital access to image data was highly appreciated. However, increased reliance on digital communication has led to reduced face-to-face contacts between clinicians and radiologists. This seems to constitute a potential threat to bilateral feedback, joint educational opportunities, and interprofessional development. Cumbersome medical information software systems, time constraints, shortage of staff, reliance on teleradiology, and lack of uniform format of radiology reports were mentioned as problematic. Further implementation of structured reporting was considered beneficial.

Conclusion

Deepened face-to-face contacts between clinicians and radiologists were considered prerequisites for mutual understanding, deepened competence and mutual trust; a key factor in interprofessional communication. Clinicians and radiologists should come together in order to secure bilateral feedback and obtain deepened knowledge of the specific needs of subspecialized clinicians.