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 f...
| Publicado en: | European Radiology Vol. 31; no. 6; pp. 3837 - 3846 |
|---|---|
| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jun2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=150343613&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 150343613 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09387994 NPH jtl: European Radiology issn: 09387994 maglogo: N pubinfo: dt: Jun2021 vid: 31 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 150343613 147210661 150343613 NLM33219850 10.1007/s00330-020-07480-7 NLM33219850 150343613 ppf: 3837 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Smart chest X-ray worklist prioritization using artificial intelligence: a clinical workflow simulation. aug: au: Baltruschat, Ivo Steinmeister, Leonhard Nickisch, Hannes Saalbach, Axel Grass, Michael Adam, Gerhard Knopp, Tobias Ittrich, Harald affil: Institute for Biomedical Imaging (E5), Hamburg University of Technology, Hamburg, Germany sug: subj: Artificial Intelligence Radiography X-Rays Systems Analysis Scales ab: 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). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|