NSF/FDA SIR: A Modeling Tool for Assessment of Radiological Workflow Prioritization Based on Computer-assisted Diagnosis

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Advances in machine learning (ML) algorithms support the diagnosis and detection of various disease conditions within minutes of medical image acquisition. One emerging application of these algorithms is as an aid in determining the priority for (human) image review of a patient with an abnormal condition. Since 2018, the FDA has approved several of these ML-aided triage devices. The patient/case-level output of these algorithms does not replace the interpretation by a radiologist but it can inform the radiologist's prioritization of case reviews and hence the radiologist's workflow. On one hand, such ML algorithms can lead to better patient outcomes by increasing the likelihood for earlier diagnosis and treatment of severe and time-sensitive conditions. On the other hand, the radiologist’s re-prioritization of work can delay the review of cases that are incorrectly analyzed and missed or those not in the scope of the ML algorithm. While the algorithms typically process the images in minutes, the overall, risk-adjusted, patient waiting time-saving benefits are difficult to discern due to the complexity of the clinician's workflow. To improve the understanding of the interplay between the ML-aided triage and the workflow into which it feeds, this project develops flexible analytical and simulation models which accommodate various ML-aided triage algorithms and workflow management rules in clinical reading. The aim of the research is to provide regulators with a quantitative principled approach to evaluate, in a workflow-relevant way, the prioritization performance of these triage algorithms. The results of this project will enable developers to identify optimal prioritization strategies, and let users have science-based information about these devices so that they can make informed health care decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.