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Jun 17, 2022, by Philips

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Match the right case to the right radiologist at the right time – automatically

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Worklist prioritization is a delicate operation for radiologists and administrators. In an ideal scenario, cases would be distributed among the radiology team based on urgency, clinical expertise and prior experience with particular patients. Imagine putting the power of AI algorithms to work to address the diagnostic image reading workflow burden for radiologists.

Knowing exactly which exam to read next can free up radiologist time and attention and relieve imaging backlog

In complex, multi-site radiology enterprises, where different locations generate an ongoing stream of imaging cases – some urgent, some less urgent, and some highly specialized – it can be a challenge to manage the prioritization and delegation of cases to the radiologist best suited to read a particular exam, based on expertise or prior history.

64% of radiologists’ time is spent on non-interpretive tasks, according to the American College of Radiology(1)

Streamlining workflow is imperative, not only to deliver a high level of patient care, but also to address the very real challenge of radiologist burnout.

Burnout is a concern for radiologists...greater for diagnostic radiologists than all other physicians. Risk factors include inadequate training, work overload, lack of control, severe time constraints for work output, prolonged stress." (2)

Report of the ACR commission on human resources

How do we balance radiologist workload and provide continuity of care?

Intelligent algorithms that automatically determine the best match can help deliver the right case to the right radiologist, based on their area of expertise, availability and current workload, including for academic and research institutions.

The ability to index multiple archives stored on disparate servers to automatically deliver the most urgent studies to the most qualified available radiologists can expedite reading and reporting so that appropriate treatment can be given as quickly as possible. This helps with balancing workload among radiologists, allowing the most relevant cases to be read first by the appropriate available subspecialist. It also helps with continuity of care since, when possible, the case can be routed to the radiologist who has already reviewed the prior exams for a given patient.

Meeting the increased demand for imaging

Addressing the imaging backlog caused by the COVID-19 pandemic will increase the number of studies that must be read. Prioritizing those studies is essential.

For example, when a patient with a sudden and acute headache is rushed into the emergency department, the CT scan demands immediate attention. AI algorithms* can detect an intracranial hemorrhage, which triggers the worklist to prioritize the case.

A smart worklist, like that in our Radiology Workflow Orchestrator, will move the exam to the top of the worklist for the most appropriate neuroradiologist. While the patient is still in the scanner, the radiologist reading the image can remotely connect to another specialist using native chat and screen-sharing to discuss the case in a secure digital environment.

Freeing up useful and precious time for the radiologist means you can not only double the number of exams to be reported, but more and more allows radiologists to grow and also improve their professional life ..."

A head of radiology in Europe

How Philips can support you

Philips Radiology Workflow Orchestrator offers the ability to index multiple archives stored on disparate servers to automatically deliver the most urgent studies to the most relevant radiologists. This helps address both quality standards and volume of work so that service level agreements can be efficiently met. See it in action below.

Go deeper

Read our brochure and learn how this solution can help address your workflow orchestration challenges.

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  • *Philips Radiology Workflow Orchestrator can be connected to external AI algorithms in order to prioritize worklists. Workflow Orchestrator itself has no AI internal algorithms.

  • Results from case studies are not predictive of results in other cases. Results in other cases may vary

  • 1. Dhanoa D, Dhesi T, Burton D, Nicolaou S, Liang T. The evolving role of the radiologist: the Vancouver Workload Utilization Evaluation Study. 2013;10(10):764-769. DOI:  doi.org/10.1016/j.jacr.2013.04.001

  • 2. Harolds J, et al. Burnout of radiologists: frequency, risk factors, and remedies: a report of the ACR commission on human resources. JACR. 2016;13(4):411-416. DOI: doi.org/10.1016/j.jacr.2015.11.003

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