Innovation Opportunities in Critical Results Communication: Theoretical Concepts

Reiner, Bruce I.

doi:10.1007/s10278-013-9609-4

Cited by 4 publications

(3 citation statements)

References 21 publications

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“…Growing radiologic workload, shortage of medical experts, and declining revenues often lead to potentially dangerous backlogs of unreported examinations, especially in publicly funded healthcare systems. With the increasing demand for radiological imaging, the continuous acceleration of image acquisition, and the expansion of teleradiological care, radiologists are nowadays working under increasing time pressure, which cannot be compensated by improving RIS-PACS integration or use of speech recognition software [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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).

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Growing radiologic workload, shortage of medical experts and declining revenues often lead to potentially dangerous backlogs of unreported examinations, especially in publicly funded health care systems. With the increasing demand for radiological imaging, the continuous acceleration of image acquisition and the expansion of teleradiological care, radiologists are nowadays working under increasing pressure of time, which cannot be compensated by improving RIS-PACS integration or use of speech recognition software [1].…”

Section: Introductionmentioning

confidence: 99%

Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation

Baltruschat¹,

Steinmeister²,

Nickisch³

et al. 2020

Preprint

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Growing radiologic workload and shortage of medical experts worldwide often lead to delayed or even unreported examinations, which poses a risk for patients safety in case of unrecognized findings in chest radiographs (CXR).The aim was to evaluate, whether deep learning algorithms for an intelligent worklist prioritization might optimize the radiology workflow and can reduce report turnaround times (RTAT) for critical findings, instead of reporting according to the First-In-First-Out-Principle (FIFO). Furthermore, we investigated the problem of false negative prediction in the context of worklist prioritization.To assess the potential benefit of an intelligent worklist prioritization, three different workflow simulations based on our analysis were run and RTAT were compared: FIFO (non-prioritized), Prio1 (prioritized) and Prio2 (prioritized, with RTATmax.). Examination triage was performed by "ChestXCheck", a convolutional neural network, classifying eight different pathological findings ranked in descending order of urgency: pneumothorax, pleural effusion, infiltrate, congestion, atelectasis, cardiomegaly, mass and foreign object.The average RTAT for all critical findings was significantly reduced by both Prio simulations compared to the FIFO simulation (e.g. pneumothorax: 32.1 min vs. 69.7 min; p < 0.0001), while the average RTAT for normal examinations increased at the same time (69.5 min vs. 90.0 min; p < 0.0001). Both effects were slightly lower at Prio2 than at Prio1, whereas the maximum RTAT at Prio1 was substantially higher for all classes, due to individual examinations rated false negative.Our Prio2 simulation demonstrated that intelligent worklist prioritization by deep learning algorithms reduces average RTAT for critical findings in chest X-ray while maintaining a similar maximum RTAT as FIFO.

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“…The potential impact of this development on the quality and quantity of the bidirectional communication has been debated 3–5. Poor communication among health care professionals has been identified as one of the primary causes of medical mistakes that adversely may affect patients’ health 6–9. Thus, the quality of communication between referring clinicians and radiologists may have an impact on patient care and patient safety, and miscommunication may have medico-legal consequences 10.…”

Section: Introductionmentioning

confidence: 99%

<p>Difficulties and possibilities in communication between referring clinicians and radiologists: perspective of clinicians</p>

Fatahi

Krupić

Hellström

2019

JMDH

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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.

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Innovation Opportunities in Critical Results Communication: Theoretical Concepts

Cited by 4 publications

References 21 publications

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

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

Smart Chest X-ray Worklist Prioritization using Artificial Intelligence: A Clinical Workflow Simulation

<p>Difficulties and possibilities in communication between referring clinicians and radiologists: perspective of clinicians</p>

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